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Purpose The purpose of this paper is to develop a new ontology for knowledge management (KM) technologies, determining the relationships between these technologies and classification of them. Design/methodology/approach The study applies NOY methodology – named after Natalya F. Noy who initiated this methodology. Protégé software and ontology web language are used for building the ontology. The presented ontology is evaluated with abbreviation and consistency criteria and knowledge retrieval of KM technologies by experts. Findings All the main concepts in the scope of KM technologies are extracted from existing literature. There are 241 words, 49 out of them are domain concepts, eight terms are about taxonomic and non-taxonomic relations, one term relates to data property and 183 terms are instances. These terms are used to develop KM technologies’ ontology based on three factors: facilitating KM processes, supporting KM strategies and the position of technology in the KM technology stage model. The presented ontology is created a common understanding in the field of KM technologies. Research limitations/implications Lack of specific documentary about logic behind decision making and prioritizing criteria in choosing KM technologies. Practical implications Uploading the presented ontology in the web environment provides a platform for knowledge sharing between experts from around the world. In addition, it helps to decide on the choice of KM technologies based on KM processes and KM strategy. Originality/value Among the many categories of KM technologies in literature, there is no classifying according to several criteria simultaneously. This paper contributes to filling this gap and considers KM processes, KM strategy and stages of growth for KM technologies simultaneously to choice the KM technologies and also there exists no formal ontology regarding KM technologies. This study has tried to propose a formal KM technologies’ ontology.

PurposeDuring recent years, web technologies and mass media have become prevalent in the context of medicine and health. Two examples of important web technologies used in health are news media and patient forums. Both have a significant role in shaping patients' perspective and behaviour in relation to health and illness, as well as the way that they might choose or change their treatment. In this paper, the authors investigated the application of web technologies using the data analysis approach. The authors did this analysis from the point of view of topics being discussed and disseminated via patients and journalists in breast and lung cancer. The study also investigated the (dis)alignment amongst these two groups and scientists in terms of topics.Design/methodology/approachThree data sets comprised documents published between 2014 and 2018 obtained from ProQuest and Web of Science Medline databases, alongside data from three major patient forums on breast and lung cancer. The analysis and visualisation in this paper have been done using the udpipe, igraph R packages and VOSviewer.FindingsThe study’s findings showed that in general scientists focussed more on prognosis and treatment of cancer, whereas patients and journalists focussed more on detection, prevention and role of social and emotional support. The only exception was for news coverage of lung cancer where the largest cluster was related to treatment, research in cancer treatment and therapies. However, when comparing coverage by scientists and journalists in terms of treatment, the focus of news articles in both cancer types was mainly on chemotherapy and complimentary therapies. Finally, topics such as lifestyle or pain management were only discussed by breast cancer patients.Originality/valueThe results obtained from this study may provide valuable insights into topics of interest for each group of scientists, journalist and patients as well as (dis)alignment among them in terms of topics. These findings are important as scientific research is heavily dependent on communication, and research does not exist in a bubble. Scientists and journalists can gain insights from patients' experiences and needs, which in turn may help them to have a more holistic and realistic view.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0228

PurposeMobile device users are frequently faced with a decision to allow access to their personal information that resides on their devices in order to install mobile applications (apps) and use their features. This paper examines the impact of satisfaction on the intention to allow access to personal information. The paper achieves this by acknowledging the affective and cognitive components of satisfaction derived from affect heuristic and privacy calculus theories, respectively.Design/methodology/approachSurvey data was collected from mobile device users who download and install mobile apps on their devices. Overall, 489 responses were collected and analyzed using LISREL 8.80.FindingsThe findings suggest that personal information disclosure decision is mainly a matter of being satisfied with the mobile app or not. We show that perceived benefits are more critical than perceived risks in determining satisfaction, and that perceived benefits influence intention to allow access to personal information indirectly through satisfaction.Originality/valueThis study offers a more nuanced analysis of the influence of satisfaction by examining the role of its two components: the cognitive (represented in perceived benefits and perceived risks) and the affective (represented in affect). We show that information disclosure decision is a complicated process that combines both rational and emotional elements.

PurposeThe purpose of this paper is to provide a comprehensive review on electronic word-of-mouth (eWOM) credibility. Further, the authors propose a comprehensive and integrated model on eWOM credibility.Design/methodology/approachThe authors conducted a systematic review of the extant literature on marketing, sociology and psychology to identify the factors that affect eWOM credibility. Further, the authors developed themes and identified factors which lead to eWOM credibility.FindingsFour factors were identified, i.e. content, communicator, context and consumer, which affect eWOM credibility. Several variables associated with these four factors were identified, which result in eWOM credibility. Further, the authors developed 22 propositions to explain the causal relationship between these variables and eWOM credibility.Research limitations/implicationsThe conceptual model needs empirical validation across various eWOM platforms, i.e. social networking websites, e-commerce websites, etc.Practical implicationsManagers and e-commerce vendors can use these inputs to develop specific design elements and assessment tools which can help consumers to identify credible eWOM messages. Credible eWOM messages, in turn, will increase the “trust” and “loyalty” of the customers on e-commerce vendors.Originality/valueThis paper provides a conclusive takeaway of eWOM credibility literature by integrating multiple perspectives and arguments from the extant literature. This study also presents an integrated model, which provides a theoretical framework for researchers to further examine the interaction effect of various variables, which results in eWOM credibility.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0263

PurposeThe purpose of this paper is to test relevance of the information quality (IQ) framework in understanding quality of advertisements (ads) posted by ordinary consumers.Design/methodology/approachThe main objective of this study is to assess quality ads posted on customer-to-customer (C2C) social commerce platforms from an IQ framework. The authors deployed innovative text mining techniques to generate features from the IQ framework and then used a machine learning (ML) algorithm to classify ads into three categories ‐ high quality, medium quality and low quality.FindingsThe results show that not all dimensions of IQ framework are important to assess quality of ads posted on the platforms. Potential buyers on these platforms look for appropriate amount of information, which is objective, concise and complete, to make a potential purchase decision.Research limitations/implicationsAs the research focuses on specific product categories, it lacks generalisability. Therefore, it needs to be tested for other product categories.Practical implicationsThe paper includes recommendation for C2C marketplaces on how to increase quality of ads posted by consumers on the platform.Originality/valueThis study has focused on the user-generated content posted by ordinary consumers on the C2C commerce platform to sell used goods. Though C2C model has been developed on ads posted on C2C platforms, it can be established for brands as it provides them with an insight into latent dimensions that a consumer shall look for in an ad on social commerce platforms.

PurposeSocial comments are rich in information and useful in evaluating, ranking or retrieving different kinds of materials. However, their merits in representing or providing added values to scientific articles have not yet been studied. Therefore, the present study investigates the informativeness of open review reports as a kind of social comments in a scholarly setting.Design/methodology/approachA test collection was built consisting of 100 randomly selected queries, 1,962 reviewed documents and their reviewers' open reports from F1000Research. They were analyzed using natural language techniques. The comments' salient words were compared to the documents' and also to the Medical Subject Headings (MeSH) salient words. The receiver operating characteristic (ROC) curve was used to test the accuracy of the comments in representing their related articles.FindingsThe papers' contents and comments have a considerable number of salient words in common. The comments' salient words are also largely found in the MeSH, signifying their consistency with the knowledge tree and their potential to add some complementary features to their related items. The ROC curves confirm the accuracy of the comments in retrieving their related papers.Originality/valueThis research is the first to reveal the merits of open review reports on scientific papers, in terms of their relatedness to their mother articles, in specific, and to the knowledge tree, in general. They are found informative in not only representing the reviewed papers but also in adding values to the contents of the papers.

PurposeNowadays some consumers consider themselves as “omnichannels” – they combine both physical and digital channels expecting a seamless shopping experience – since they view their shopping process from a multiple-channel viewpoint. Giving that situation, the aim of this paper is to test the role of consumers’ omnichannel tendency (omni-tendency) in the information processing in the digital channel.Design/methodology/approachBased on the Elaboration Likelihood Model (ELM), emotions as well as utilitarian and hedonic experiences are proposed to understand consumer attitude towards the digital store. Through a survey, data were collected from 284 digital shoppers. PLS path modelling and PLS-MGA were used to test the research hypotheses.FindingsThe results confirm that emotions positively affect the evaluation of the experiences, which in turn improves the attitude towards the digital store. Focusing on the differences among consumers, the findings show that for consumers with low omni-tendency the emotions are key to improve the evaluation of their experiences. Moreover, regarding the attitude, consumers with more omni-tendency follow the central route to process the information; and consumers with less omni-tendency follow the peripheral route.Originality/valueThis paper contributes to the literature in three ways. First, this research includes the study of omni-tendency, as a consumer trait, in the information processing developed in the digital channel, ignored in the literature. Second, this work contributes to information processing theories in digital context confirming, specifically the applicability of ELM into the omnichannel context. This offers support to the application of traditional theories to explain new phenomena. Third, and in line with the previous contribution, this work goes a step further in understanding ELM theory by including other constructs –the omni-tendency and emotions– to explain the information processing in the digital context.

Purpose Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-generated content such as product reviews to enhance recommendation performance. The purpose of this paper is to show that the performance of a recommender system can be enhanced by integrating explicit knowledge extracted from product reviews with implicit knowledge extracted from analysis of consumer’s purchase behaviour. Design/methodology/approach The authors introduce a sentiment and preference-guided strategy for product recommendation by integrating not only explicit, user-generated and sentiment-rich content but also implicit knowledge gleaned from users’ product purchase preferences. Integration of both of these knowledge sources helps to model sentiment over a set of product aspects. The authors show how established dimensionality reduction and feature weighting approaches from text classification can be adopted to weight and select an optimal subset of aspects for recommendation tasks. The authors compare the proposed approach against several baseline methods as well as the state-of-the-art better method, which recommends products that are superior to a query product. Findings Evaluation results from seven different product categories show that aspect weighting and selection significantly improves state-of-the-art recommendation approaches. Research limitations/implications The proposed approach recommends products by analysing user sentiment on product aspects. Therefore, the proposed approach can be used to develop recommender systems that can explain to users why a product is recommended. This is achieved by presenting an analysis of sentiment distribution over individual aspects that describe a given product. Originality/value This paper describes a novel approach to integrate consumer purchase behaviour analysis and aspect-level sentiment analysis to enhance recommendation. In particular, the authors introduce the idea of aspect weighting and selection to help users identify better products. Furthermore, the authors demonstrate the practical benefits of this approach on a variety of product categories and compare the approach with the current state-of-the-art approaches.

Purpose The purpose of this paper is to propose a novel method to enhance the spread of messages in social networks by “Spreading Groups.” These sub-structures of highly connected accounts intentionally echo messages between the members of the subgroup at the early stages of a spread. This echoing further boosts the spread to regions substantially larger than the initial region. These spreading accounts can be actual humans or social bots. Design/methodology/approach The paper reveals an interesting anomaly in information cascades in Twitter and proposes the spreading group model that explains this anomaly. The model was tested using an agent-based simulation, real Twitter data and questionnaires. Findings The messages of few anonymous Twitter accounts spread on average more than well-known global financial media groups, such as The Wall Street Journal or Bloomberg. The spreading groups (also sometimes called BotNets) model provides an effective mechanism that can explain these findings. Research limitations/implications Spreading groups are only one possible mechanism that can explain the effectiveness of spread of tweets from lesser known accounts. The implication of this work is in showing how spreading groups can be used as a mechanism to spread messages in social networks. The construction of spreading groups is rather technical and does not require using opinion leaders. Similar to the case of “Fake News,” we expect the topic of spreading groups and their aim to manipulate information to receive growing attention in public discussion. Practical implications While harnessing opinion leaders to spread messages is costly, constructing spreading groups is more technical and replicable. Spreading groups are an efficient method to amplify the spread of message in social networks. Social implications With the blossoming of fake news, one might tend to assess the reliability of news by the number of users involved in its spread. This heuristic might be easily fooled by spreading groups. Furthermore, spreading groups consisting of a blend of human and computerized bots might be hard to detect. They can be used to manipulate financial markets or political campaigns. Originality/value The paper demonstrates an anomaly in Twitter that was not studied before. It proposes a novel approach to spreading messages in social networks. The methods presented in the paper are valuable for anyone interested in spreading messages or an agenda such as political actors or other agenda enthusiasts. While social bots have been widely studied, their synchronization to increase the spread is novel.

PurposeThe purpose of this paper is to propose that in order to tackle the question of bias in algorithms, a systemic, sociotechnical and holistic perspective is needed. With reference to the term “algorithmic culture,” the interconnectedness and mutual shaping of society and technology are postulated. A sociotechnical approach requires translational work between and across disciplines. This conceptual paper undertakes such translational work. It exemplifies how gender and diversity studies, by bringing in expertise on addressing bias and structural inequalities, provide a crucial source for analyzing and mitigating bias in algorithmic systems.Design/methodology/approachAfter introducing the sociotechnical context, an overview is provided regarding the contemporary discourse around bias in algorithms, debates around algorithmic culture, knowledge production and bias identification as well as common solutions. The key concepts of gender studies (situated knowledges and strong objectivity) and concrete examples of gender bias then serve as a backdrop for revisiting contemporary debates.FindingsThe key concepts reframe the discourse on bias and concepts such as algorithmic fairness and transparency by contextualizing and situating them. The paper includes specific suggestions for researchers and practitioners on how to account for social inequalities in the design of algorithmic systems.Originality/valueA systemic, gender-informed approach for addressing the issue is provided, and a concrete, applicable methodology toward a situated understanding of algorithmic bias is laid out, providing an important contribution for an urgent multidisciplinary dialogue.

Purpose The purpose of this paper is to examine how claims to “ownership” are asserted over publicly accessible platform data and critically assess the nature and scope of rights to reuse these data. Design/methodology/approach Using Airbnb as a case study, this paper examines the data ecosystem that arises around publicly accessible platform data. It analyzes current statute and case law in order to understand the state of the law around the scraping of such data. Findings This paper demonstrates that there is considerable uncertainty about the practice of data scraping, and that there are risks in allowing the law to evolve in the context of battles between business competitors without a consideration of the broader public interest in data scraping. It argues for a data ecosystem approach that can keep the public dimension issues more squarely within the frame when data scraping is judicially considered. Practical implications The nature of some sharing economy platforms requires that a large subset of their data be publicly accessible. These data can be used to understand how platform companies operate, to assess their compliance with laws and regulations and to evaluate their social and economic impacts. They can also be used in different kinds of data analytics. Such data are therefore sought after by civil society organizations, researchers, entrepreneurs and regulators. This paper considers who has a right to control access to and use of these data, and addresses current uncertainties in how the law will apply to scraping activities, and builds an argument for a consideration of the public interest in data scraping. Originality/value The issue of ownership/control over publicly accessible information is of growing importance; this paper offers a framework for approaching these legal questions.

Purpose Mobile health service (MHS) can provide users with convenient health services and information to reduce their medical costs from aging or other health issues. Previous studies confirm the underdevelopment of the Chinese MHS. The purpose of this paper is to analyze the factors that affect the intention to use MHS. Design/methodology/approach This paper develops a research model that integrates personal health differences with theory of planned behavior (TPB) and protection motivation theory (PMT). This model is empirically tested using data from 494 valid questionnaires. Structural equation modeling is used to test the hypotheses. Findings Perceived vulnerability to disease, perceived severity of disease, response efficacy and self-efficacy positively affect attitude, thereby exerting a positive influence on the behavioral intention to use MHS. Subjective norms also influence users’ behavioral intention. Personal health status and personal health value have quasi-moderating effects on the relationship between attitude and behavioral intention. Originality/value This paper presents an early attempt to conceptualize and validate a research model of MHS acceptance by integrating TPB and PMT in a complementary manner. The integrated model provides a holistic view of people’s intention to use MHS by considering health threat beliefs, individual role (i.e. attitude and self-efficacy) and social influences (i.e. subjective norms). Furthermore, this research highlights the role of two individual health characteristics (i.e. personal health status and personal health value) in MHS adoption. These new findings are beneficial toward an in-depth understanding of technology adoption in the MHS context.

Purpose The purpose of this paper is to treat WeChat moments as social media environments and applies the research model to explore the effect of social media environments on perceived interactivity from the perspective of environmental psychology. Design/methodology/approach This paper proposes social media environments as effective stimuli for future participate in online social interactions. First, two cues of social media environments (user-to-system cues and user-to-user cues) can be important antecedents of users’ perception of interactivity. Second, users’ intention of future participates in online social interactions can be influenced by three dimensions of perceived interactivity (action control, connectedness and responsiveness). Using data from 334 users of WeChat moments, the authors conduct partial least squares analysis to validate the research model. Findings The results indicate that both technological and social environments positively affect three dimensions of perceived interactivity, respectively, including action control, connectedness and responsiveness. Moreover, actual findings also suggest that higher perceived interactivity increases users’ intention of future participate in online social interactions. Originality/value This work contributes to in-depth research on the relationships between social environments and perceived interactivity. Besides, this paper demonstrates that both technological and social cues of social media environments are significant elements in simulating users’ internal experience and behavioral intention. The main conclusions of this study can be valuable to social media developers and managers.

PurposeSocial media platforms provide a source of information about events. However, this information may not be credible, and the distance between an information source and the event may impact on that credibility. Therefore, the purpose of this paper is to address an understanding of the relationship between sources, physical distance from that event and the impact on credibility in social media.Design/methodology/approachIn this paper, the authors focus on the impact of location on the distribution of content sources (informativeness and source) for different events, and identify the semantic features of the sources and the content of different credibility levels.FindingsThe study found that source location impacts on the number of sources across different events. Location also impacts on the proportion of semantic features in social media content.Research limitations/implicationsThis study illustrated the influence of location on credibility in social media. The study provided an overview of the relationship between content types including semantic features, the source and event locations. However, the authors will include the findings of this study to build the credibility model in the future research.Practical implicationsThe results of this study provide a new understanding of reasons behind the overestimation problem in current credibility models when applied to different domains: such models need to be trained on data from the same place of event, as that can make the model more stable.Originality/valueThis study investigates several events – including crisis, politics and entertainment – with steady methodology. This gives new insights about the distribution of sources, credibility and other information types within and outside the country of an event. Also, this study used the power of location to find alternative approaches to assess credibility in social media.

Purpose Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue. Design/methodology/approach This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships. Findings The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored. Research limitations/implications Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems. Originality/value This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.

Purpose The purpose of this paper is to predict Twitter satisfaction by healthcare professionals through integrating constructs of Csikszentmihalyi’s flow theory, quality dimensions and usefulness. Design/methodology/approach Survey responses of 108 physicians from a variety of specialisations in the United Arab Emirates have been validated and analysed by means of partial least squares-based structural equation modelling method using smartPLS software. Findings Service quality has emerged as the most influential quality dimension that positively impact flow state and perceived usefulness of Twitter, while information quality, surprisingly, does not show any effect. The findings also indicate that flow state plays a significant role in shaping physicians’ satisfaction with Twitter. The study also enhances our understanding concerning the effects of perceived usefulness on flow state and satisfaction. Research limitations/implications Understanding factors that influence Twitter satisfaction can help healthcare managers construct appropriate intervention strategies for maximising professional benefits of social media and minimising user resistance. This is important because top managers usually ratify traditional practices that are only of limited effect. Also, the findings help vendors to accentuate user’s concerns in addition to system functionalities in social media applications. Originality/value The paper is an early attempt to propose a model for social media success in a professional context in general and healthcare in particular. It also one of first studies that examine social media satisfaction through integrating contemporary information system success and acceptance models with flow theory.

Purpose Considering that users’ information privacy concerns may affect the development of e-commerce, the purpose of this paper is to explore what drives internet users’ willingness to provide personal information; further, the paper examines how extrinsic rewards moderate the relationship between users’ information privacy concerns and willingness to provide personal information. Design/methodology/approach Data collected from 345 valid internet users in the context of electronic commerce were analyzed using the partial least squares approach. Findings The result showed that agreeableness, risk-taking propensity and experience of privacy invasion were three main antecedents of information privacy concerns among the seven individual factors. Additionally, information privacy concerns did not significantly affect users’ willingness to provide personal information in the privacy calculation mechanism; however, extrinsic rewards directly affected users’ disclosure intention. The authors found that extrinsic rewards had not moderated the relationship between users’ information privacy concerns and their willingness to provide personal information. Originality/value This study is an exploratory effort to develop and validate a model for explaining why internet users were willing to provide personal information. The results of this study are helpful to researchers in developing theories of information privacy concerns and to practitioners in promoting internet users’ willingness to provide personal information in an e-commerce context.

Purpose The purpose of this paper is to investigate image search and retrieval problems in selected search engines in relation to Persian writing style challenges. Design/methodology/approach This study is an applied one, and to answer the questions the authors used an evaluative research method. The aim of the research is to explore the morphological and semantic problems of Persian language in connection with image search and retrieval among the three major and widespread search engines: Google, Yahoo and Bing. In order to collect the data, a checklist designed by the researcher was used and then the data were analyzed by descriptive and inferential statistics. Findings The results indicate that Google, Yahoo and Bing search engines do not pay enough attention to morphological and semantic features of Persian language in image search and retrieval. This research reveals that six groups of Persian language features include derived words, derived/compound words, Persian and Arabic Plural words, use of dotted T and the use of spoken language and polysemy, which are the major problems in this area. In addition, the results suggest that Google is the best search engine of all in terms of compatibility with Persian language features. Originality/value This study investigated some new aspects of the above-mentioned subject through combining morphological and semantic aspects of Persian language with image search and retrieval. Therefore, this study is an interdisciplinary research, the results of which would help both to offer some solutions and to carry out similar research on this subject area. This study will also fill a gap in research studies conducted so far in this area in Farsi language, especially in image search and retrieval. Moreover, findings of this study can help to bridge the gap between the user’s questions and search engines (systems) retrievals. In addition, the methodology of this paper provides a framework for further research on image search and retrieval in databases and search engines.

Purpose The quality of consumer-oriented health information on the web has been defined and evaluated in several studies. Usually it is based on evaluation criteria identified by the researchers and, so far, there is no agreed standard for the quality indicators to use. Based on such indicators, tools have been developed to evaluate the quality of web information. The HONcode is one of such tools. The purpose of this paper is to investigate the influence of web document features on their quality, using HONcode as ground truth, with the aim of finding whether it is possible to predict the quality of a document using its characteristics. Design/methodology/approach The present work uses a set of health documents and analyzes how their characteristics (e.g. web domain, last update, type, mention of places of treatment and prevention strategies) are associated with their quality. Based on these features, statistical models are built which predict whether health-related web documents have certification-level quality. Multivariate analysis is performed, using classification to estimate the probability of a document having quality given its characteristics. This approach tells us which predictors are important. Three types of full and reduced logistic regression models are built and evaluated. The first one includes every feature, without any exclusion, the second one disregards the Utilization Review Accreditation Commission variable, due to it being a quality indicator, and the third one excludes the variables related to the HONcode principles, which might also be indicators of quality. The reduced models were built with the aim to see whether they reach similar results with a smaller number of features. Findings The prediction models have high accuracy, even without including the characteristics of Health on the Net code principles in the models. The most informative prediction model considers characteristics that can be assessed automatically (e.g. split content, type, process of revision and place of treatment). It has an accuracy of 89 percent. Originality/value This paper proposes models that automatically predict whether a document has quality or not. Some of the used features (e.g. prevention, prognosis or treatment) have not yet been explicitly considered in this context. The findings of the present study may be used by search engines to promote high-quality documents. This will improve health information retrieval and may contribute to reduce the problems caused by inaccurate information.

Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers. Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems. Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations. Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

Purpose With the development of the internet, huge numbers of reviews are generated, disseminated, and shared on e-commerce and social media websites by internet users. These reviews usually indicate users’ opinions about products or services directly, and are thus valuable for efficient marketing. The purpose of this paper is to mine online users’ attitudes from a huge pool of reviews via automatic question answering. Design/methodology/approach The authors make use of online reviews to complete an online investigation via automatic question answering (AQA). In the process of AQA, question generation and extraction of corresponding answers are conducted via sentiment computing. In order to verify the performance of AQA for online investigation, online reviews from a well-known travel website, namely Tuniu.com, are used as the experimental data set. Finally, the experimental results from AQA vs a traditional questionnaire are compared. Findings The experimental results show that results between the AQA-based automatic questionnaire and the traditional questionnaire are consistent. Hence, the AQA method is reliable in identifying users’ attitudes. Although this paper takes Chinese tourism reviews as the experimental data, the method is domain and language independent. Originality/value To the best of the authors’ knowledge, this is the first study to use the AQA method to mine users’ attitudes towards tourism services. Using online reviews may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

Purpose The purpose of this paper is to investigate whether machine learning induces gender biases in the sense of results that are more accurate for male authors or for female authors. It also investigates whether training separate male and female variants could improve the accuracy of machine learning for sentiment analysis. Design/methodology/approach This paper uses ratings-balanced sets of reviews of restaurants and hotels (3 sets) to train algorithms with and without gender selection. Findings Accuracy is higher on female-authored reviews than on male-authored reviews for all data sets, so applications of sentiment analysis using mixed gender data sets will over represent the opinions of women. Training on same gender data improves performance less than having additional data from both genders. Practical implications End users of sentiment analysis should be aware that its small gender biases can affect the conclusions drawn from it and apply correction factors when necessary. Users of systems that incorporate sentiment analysis should be aware that performance will vary by author gender. Developers do not need to create gender-specific algorithms unless they have more training data than their system can cope with. Originality/value This is the first demonstration of gender bias in machine learning sentiment analysis.

Purpose The purpose of this paper is to assess whether Europeana can be really seen as the new “Library of Alexandria” for digital resources from the perspective of teachers, academics and researchers that might want to retrieve and reuse those resources for learning purposes. Design/methodology/approach The authors define a systematic method for studying Europeana digital resources and their potential for reuse in e-learning environments. To achieve this, the authors explore the coverage of digital resources when searching information about a specific area. The paper studies the relevance of the results (thematic coverage) provided by queries targeting the resources’ metadata elements defined by the EDM Europeana model. All the data collected for this study from Europeana digital resources are the result of four explorations since 2012-2016. Findings The autors found that Europeana as a digital library has good levels of coverage of digital resources for those terms in the AAT thesaurus explored (terms from the “Styles and Periods” subset). This subset was selected as an example of faceted search given that it is a common search topic in the academic environment. However, it is important to remark the use of common vocabulary words provided by participants in order to obtain relevant results of search based on specific knowledge areas defined by AAT terms. Research limitations/implications This research used a relatively small sample size of resources which may not be representative of the general size of Europeana digital resources. However, the results are illustrative as they are based on a specific knowledge area of AAT (118 terms) which contains very common topics used in high school courses by students and teachers in the field of art and cultural heritage. The study also aspires to provide a systematic method for conducting future studies in other knowledge areas. Originality/value The coverage study aims to analyze if Europeana is a digital library that teachers could use for the development of learning objects in specific knowledge areas, through the reuse of free/open access digital resources.

Purpose The purpose of this paper is to find a representative subset from large-scale online reviews for consumers. The subset is significantly small in size, but covers the majority amount of information in the original reviews and contains little redundant information. Design/methodology/approach A heuristic approach named RewSel is proposed to successively select representatives until the number of representatives meets the requirement. To reveal the advantages of the approach, extensive data experiments and a user study are conducted on real data. Findings The proposed approach has the advantage over the benchmarks in terms of coverage and redundancy. People show preference to the representative subsets provided by RewSel. The proposed approach also has good scalability, and is more adaptive to big data applications. Research limitations/implications The paper contributes to the literature of review selection, by proposing a heuristic approach which achieves both high coverage and low redundancy. This study can be applied as the basis for conducting further analysis of large-scale online reviews. Practical implications The proposed approach offers a novel way to select a representative subset of online reviews to facilitate consumer decision making. It can also enhance the existing information retrieval system to provide representative information to users rather than a large amount of results. Originality/value The proposed approach finds the representative subset by adopting the concept of relative entropy and sentiment analysis methods. Compared with state-of-the-art approaches, it offers a more effective and efficient way for users to handle a large amount of online information.

Purpose The purpose of this paper is to develop a multi-agent-based simulation model for the online opinion dissemination during hazardous chemical leakage emergencies into rivers in China, to explore an appropriate crisis information release policy of China’s government for controlling public panic. Design/methodology/approach In the proposed model, two fundamental attributes of crisis information, i.e., truthfulness (for true or false news) and attitude (for positive, neutral or negative opinion), are considered. Four major agents in the online community system, i.e., citizens, the government, media and opinion leaders, are included. Using four typical accidents of hazardous chemical leakage into rivers in China as case studies, insightful policy implications can be obtained for crisis management and panic control. Findings The news about the terrible potential damages from such a type of accidents will instantly arise wide-ranging public panic; therefore, the corresponding crisis information release policy should be carefully designed. It is strongly advised against publishing false news to temporarily conceal the accidents, which will seriously hurt the government’s reputation and agitate much larger-scale public panic in terms of degree and duration. To mitigate public panic, the true news especially about treatment measurements should be published immediately. If the government does nothing and releases no crisis information, the public panic will go out of control. Research limitations/implications This paper only focuses on the crisis information release policies from the perspectives of the government. Furthermore, this study especially focuses on the cases in China, and extending the proposed model study for general contexts is an important direction to improve this study. Finally, the proposed model should be extended to other types of emergencies to further justify its generalization and universality, especially various natural catastrophes like storms, floods, tsunamis, etc. Originality/value This paper develops a multi-agent-based model for online public opinion dissemination in emergency to explore an appropriate crisis information release policy for controlling public panic stemming from hazardous chemicals leakage accidents into rivers. The proposed model makes major contributions to the literature from two perspectives. First, the crisis information about emergency accidents are divided into true and false news based on the truthfulness attribute, and into neutral, positive and negative emotions based on the attitude attribute. Second, the proposed model covers the main agents in the online virtual community.

Purpose Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments. Design/methodology/approach In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early warning via a semantic expansion technique. Findings Empirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and efficiently using cross-media analytics. In addition, the proposed system can be used for decision support and real-time response for government and emergency management departments. Research limitations/implications This paper takes full advantage of cross-media information and proposes novel emergency event detection and opinion mining methods for emergency management using cross-media analytics. The empirical analysis results illustrate the efficiency of the proposed method. Practical implications The proposed method can be applied for detection of emergency events and tracking of public opinions for emergency decision support and governmental real-time response. Originality/value This research work contributes to the design of a decision support system for emergency event detection and opinion mining. In the proposed approaches, emergency events are detected by leveraging cross-media analytics, and public sentiments are measured using an auto-expansion of the domain dictionary in the field of emergency management to eliminate the misclassification of the general dictionary and to make the quantization more accurate.

Purpose Misinformation can have lasting impacts in the management and control of a public emergency. The purpose of this paper is to demonstrate how misinformation flows and how user characteristics can shape such flows in the context of a violent riot in Singapore. Design/methodology/approach The authors apply the two-step flow theory and discuss the mixed methods approach involving wrangling Twitter data and descriptive analysis to develop and analyse two corpuses of misinformation related to the riot. Findings The findings are mostly consistent with the two-step flow theory, in that misinformation flows to the masses from opinion leaders (as indicated by higher measures such as online social influence and followers/following ratio). In the presence of misinformation, tweets opposing such misinformation may not always come from opinion leaders. Practical implications The authors work furthers knowledge about how misinformation goes viral, which provides practical implications to help policymakers and scholars in understanding and managing the dynamics and pitfalls of misinformation during an emergency. Originality/value This paper tackles the problem of misinformation in public emergencies using a mixed methods approach and contributes to ongoing theoretical work on managing online misinformation especially in public emergencies and crises.

Purpose The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network. Design/methodology/approach The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles. Findings The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method. Originality/value This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.

Purpose The paper addresses various cyber threats and their effects on the internet. A review of the literature on intrusion detection systems (IDSs) as a means of mitigating internet attacks is presented, and gaps in the research are identified. The purpose of this paper is to identify the limitations of the current research and presents future directions for intrusion/malware detection research. Design/methodology/approach The paper presents a review of the research literature on IDSs, prior to identifying research gaps and limitations and suggesting future directions. Findings The popularity of the internet makes it vulnerable against various cyber-attacks. Ongoing research on intrusion detection methods aims to overcome the limitations of earlier approaches to internet security. However, findings from the literature review indicate a number of different limitations of existing techniques: poor accuracy, high detection time, and low flexibility in detecting zero-day attacks. Originality/value This paper provides a review of major issues in intrusion detection approaches. On the basis of a systematic and detailed review of the literature, various research limitations are discovered. Clear and concise directions for future research are provided.

Purpose This paper reports on a quantitative study of data gathered from the Linked Open Vocabularies (LOV) catalogue, including the use of network analysis and metrics. The purpose of this paper is to gain insights into the structure of LOV and the use of vocabularies in the Web of Data. It is important to note that not all the vocabularies in it are registered in LOV. Given the de-centralised and collaborative nature of the use and adoption of these vocabularies, the results of the study can be used to identify emergent important vocabularies that are shaping the Web of Data. Design/methodology/approach The methodology is based on an analytical approach to a data set that captures a complete snapshot of the LOV catalogue dated April 2014. An initial analysis of the data is presented in order to obtain insights into the characteristics of the vocabularies found in LOV. This is followed by an analysis of the use of Vocabulary of a Friend properties that describe relations among vocabularies. Finally, the study is complemented with an analysis of the usage of the different vocabularies, and concludes by proposing a number of metrics. Findings The most relevant insight is that unsurprisingly the vocabularies with more presence are those used to model Semantic Web data, such as Resource Description Framework, RDF Schema and OWL, as well as broadly used standards as Simple Knowledge Organization System, DCTERMS and DCE. It was also discovered that the most used language is English and the vocabularies are not considered to be highly specialised in a field. Also, there is not a dominant scope of the vocabularies. Regarding the structural analysis, it is concluded that LOV is a heterogeneous network. Originality/value The paper provides an empirical analysis of the structure of LOV and the relations between its vocabularies, together with some metrics that may be of help to determine the important vocabularies from a practical perspective. The results are of interest for a better understanding of the evolution and dynamics of the Web of Data, and for applications that attempt to retrieve data in the Linked Data Cloud. These applications can benefit from the insights into the important vocabularies to be supported and the value added when mapping between and using the vocabularies.

Purpose Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient’s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words. Design/methodology/approach This paper proposes a detection algorithm to a medical-opinion-mining system using rule-based and support vector machines (SVM) algorithms. A corpus from 225 drug reviews was manually annotated by a medical expert for training and testing. Findings The results show that SVM significantly outperforms a rule-based algorithm. However, the results of both algorithms are encouraging and a good foundation for future research. Obviating the limitations and exploiting combined approaches would improve the results. Practical implications An automatic extraction for adverse drug effects information from online text can help regulatory authorities in rapid information screening and extraction instead of manual inspection and contributes to the acceleration of medical decision support and safety alert generation. Originality/value The results of this study can help database curators in compiling adverse drug effects databases and researchers to digest the huge amount of textual online information which is growing rapidly.

Purpose The purpose of this paper is to present a new concept – task affordance in crowdsourcing context, and build it as a theoretical lens to help the authors reconfigure the artifacts and process in task-oriented crowdsourcing projects. The paper differs from previous studies by focusing on the relationships between the task artifacts, systems and goal-directed actors in crowdsourcing process rather than on the pure examination of task properties. Design/methodology/approach An operational definition of task affordance was proposed and a pseudo-entity-relationship model based approach was employed to portrait the task affordance in online crowdsourcing context. Furthermore, the authors developed a typology of task affordance and decomposed the concept into five dimensions, namely, design affordance, presentation affordance, assignment affordance, task-platform fit affordance, and task-worker fit affordance. A preliminary analysis of task affordances across various crowdsourcing categories was also conducted to validate the proposed typological framework. Findings The findings show that the task affordances have varying degree and extend among the diverse crowdsourcing categories. For instance, task design affordances seem to be low in the crowd processing and crowd rating cases compared with that in the crowd solving and crowd creation cases. For another example, in terms of the task presentation affordance, crowd rating cases need the lowest affordance while the crowd creation cases need the highest affordance. Therefore, the authors would like to emphasize that the successful adoption, implementation, and design of the task-oriented crowdsourcing owes to the careful examination of the relationships among the actors, artifacts, and environment of the crowdsourcing projects. Originality/value To the authors’ best knowledge, this paper is the first study on conceptualizing the task affordance in online crowdsourcing context. The study contributes to the academic literature on a comprehensive overview of task-related studies in crowdsourcing, which are scattered in several information related fields. Furthermore, this research contributes directly to the area of information science and technology due to a common interest in studying the environments and contexts in which people, information and technology interact and interplay. Practically, this study may yield some implications for the requester and platform operator when designing the relevant tasks or developing the specific crowdsourcing platform.

Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

Purpose The purpose of this paper is to review interventions/methods for engaging older adults in meaningful digital public service design by enabling them to engage critically and productively with open data and civic tech. Design/methodology/approach The paper evaluates data walks as a method for engaging non-tech-savvy citizens in co-design work. These were evaluated along a framework considering how such interventions allow for sharing control (e.g. over design decisions), sharing expertise and enabling change. Findings Within a co-creation project, different types of data walks may be conducted, including ideation walks, data co-creation walks or user test walks. These complement each other with respect to how they facilitate the sharing of control and expertise, and enable change for a variety of older citizens. Practical implications Data walks are a method with a low-threshold, potentially enabling a variety of citizens to engage in co-design activities relating to open government and civic tech. Social implications Such methods address the digital divide and further social participation of non-tech-savvy citizens. They value the resources and expertise of older adults as co-designers and partners, and counter stereotypical ideas about age and ageing. Originality/value This pilot study demonstrates how data walks can be incorporated into larger co-creation projects.

Purpose Social network sites have been widely adopted by politicians in the last election campaigns. To increase the effectiveness of these campaigns the potential electorate is to be identified, as targeted ads are much more effective than non-targeted ads. Therefore, the purpose of this paper is to propose and implement a new methodology for automatic prediction of political orientation of users on social network sites by comparison to texts from the overtly political parties’ pages. Design/methodology/approach To this end, textual information on personal users’ pages is used as a source of statistical features. The authors apply automatic text categorization algorithms to distinguish between texts of users from different political wings. However, these algorithms require a set of manually labeled texts for training, which is typically unavailable in real life situations. To overcome this limitation the authors propose to use texts available on various political parties’ pages on a social network site to train the classifier. The political leaning of these texts is determined by the political affiliation of the corresponding parties. The classifier learned on such overtly political texts is then applied on the personal user pages to predict their political orientation. To assess the validity and effectiveness of the proposed methodology two corpora were constructed: personal Facebook pages of 450 Israeli citizens, and political parties Facebook pages of the nine prominent Israeli parties. Findings The authors found that when a political tendency classifier is trained and tested on data in the same corpus, accuracy is very high. More significantly, training on manifestly political texts (political party Facebook pages) yields classifiers which can be used to classify non-political personal Facebook pages with fair accuracy. Social implications Previous studies have shown that targeted ads are more effective than non-targeted ads leading to substantial saving in the advertising budget. Therefore, the approach for automatic determining the political orientation of users on social network sites might be adopted for targeting political messages, especially during election campaigns. Originality/value This paper proposes and implements a new approach for automatic cross-corpora identification of political bias of user profiles on social network. This suggests that individuals’ political tendencies can be identified without recourse to any tagged personal data. In addition, the authors use learned classifiers to determine which self-identified centrists lean left or right and which voters are likely to switch allegiance in subsequent elections.

Purpose– Social Media is one of the largest platforms to voluntarily communicate thoughts. With increase in multimedia data on social networking websites, information about human behaviour is increasing. This user-generated data are present on the internet in different modalities including text, images, audio, video, gesture, etc. The purpose of this paper is to consider multiple variables for event detection and analysis including weather data, temporal data, geo-location data, traffic data, weekday’s data, etc.Design/methodology/approach– In this paper, evolution of different approaches have been studied and explored for multivariate event analysis of uncertain social media data.Findings– Based on burst of outbreak information from social media including natural disasters, contagious disease spread, etc. can be controlled. This can be path breaking input for instant emergency management resources. This has received much attention from academic researchers and practitioners to study the latent patterns for event detection from social media signals.Originality/value– This paper provides useful insights into existing methodologies and recommendations for future attempts in this area of research. An overview of architecture of event analysis and statistical approaches are used to determine the events in social media which need attention.

Purpose – The purpose of this paper from the series “Monitoring the Media: Spotlight on Social Media Research” is to look into different approaches to study uses of social media platforms – from user statistics to motivations for using specific features within a platform. Design/methodology/approach – Based on a literature review some general findings on social media usage are summarized alongside with examples of user activities that are rarely studied. Findings – The paper concludes that social media research has neglected to question the use of more recent features in social media platforms, such as Twitter favorites or Facebook hashtags, as well as the more “destructive” activities in social networking such as unfollowing. Originality/value – The paper draws attention to some features of popular social media platforms which are currently understudied. It raises awareness for these specific gaps in social media research and could inspire future studies to close the gap.

Purpose – The purpose of this paper is to estimate Google search engine’s ranking function from a search engine optimization (SEO) perspective. Design/methodology/approach – The paper proposed an estimation function that defines the query match score of a search result as the weighted sum of scores from a limited set of factors. The search results for a query are re-ranked according to the query match scores. The effectiveness was measured by comparing the new ranks with the original ranks of search results. Findings – The proposed method achieved the best SEO effectiveness when using the top 20 search results for a query. The empirical results reveal that PageRank (PR) is the dominant factor in Google ranking function. The title follows as the second most important, and the snippet and the URL have roughly equal importance with variations among queries. Research limitations/implications – This study considered a limited set of ranking factors. The empirical results reveal that SEO effectiveness can be assessed by a simple estimation of ranking function even when the ranks of the new and original result sets are quite dissimilar. Practical implications – The findings indicate that web marketers should pay particular attention to a webpage’s PR, and then place the keyword in URL, the page title, and snippet. Originality/value – There have been ongoing concerns about how to formulate a simple strategy that can help a website get ranked higher in search engines. This study provides web marketers much needed empirical evidence about a simple way to foresee the ranking success of an SEO effort.

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap. Research limitations/implications – This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well. Originality/value – This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

Purpose – The purpose of this paper is to offer a theory-driven, evidence-based approach to developing a brand’s messaging strategies on social media encompassing three messaging dimensions, namely, appeal, content, and richness. Design/methodology/approach – Using longitudinal data from three Fortune 200 companies – Delta Airlines, Wal-Mart, and McDonald’s – the authors empirically investigate comprehensive strategic messaging framework. Using ANOVAs and regression analyses, the authors test a set of hypotheses regarding the relations between a brand ' s purchase involvement, its message appeal, message content, and message richness, and engagement. Findings – Findings reveal significant relations between purchase involvement and appeal. Furthermore, the authors find that abstract content categories are best combined with richer media. Finally, both transformation appeal and richer media have a highly significant and positive effect on engagement. Research limitations/implications – The authors offer a theoretical ground and empirical validation of both a comprehensive typology of content categories and a holistic strategic messaging framework that can fill a significant void in the social media marketing literature that lacks integrative models for assessing, classifying, analyzing, and in turn, informing future social media marketing strategies. Practical implications – The validated framework can help managers better understand the diversity of messaging components as well as offer an analytical tool for assessing the nature of engagement associated with each appeal and category. Originality/value – To the best of the author’s knowledge, this paper offers the first comprehensive typology of content categories and validates it in the context of a strategic messages framework using real-world data finding strong support for all hypotheses.

PurposeComplex social interactions such as argumentation and persuasion are increasingly common in online communications. To better understand these interactions and their impacts on people and on the society, it is important for the authors to understand how people reason online such as when they need to justify themselves or convince others with their perspectives. Reasoning in online discussions is expectedly to be different from doing so offline, as one often communicates with others anonymously and asynchronously in such contexts (e.g. Reddit discussions). The purpose of this paper is to investigate people's reasoning behavior in online environments focusing on how they justify their perspectives.Design/methodology/approachIn this study, the authors examined how a subreddit Change My View (CMV) users offer their opinions and justify them through the lens of argumentation and reasoning. The authors annotated, 330 Reddit discussion original posts (OPs) to identify claims, rationales and supports for reasoning, i.e. personal experiences, definitions, domain expertise and external sources. The authors investigated the correlations among the occurrences of these supporting statements and whether they are related to the topics of the posts.FindingsThe findings suggest that if people mention their domain expertise, they tend to provide related personal experiences as well. Additionally, if the participants talk about the topic of domestic politics, they tend to utilize their personal experiences.Research limitations/implicationsFurther research may be conducted to help system designers. System designers (e.g. online debate systems, collective decision-making systems, etc.) may benefit from the findings to design systems by considering commonly used supporting statements, which may enhance people's reasoning and argumentation processes. The sample size is a small sample. The authors acknowledge that the small sample size of the study may limit the generalizability of the findings; however, it is still acceptable compared to the existing literature. One future study could be annotating a larger dataset to further probe the use of supporting statements in online reasoning.Practical implicationsThe authors' findings might be useful to understand how Reddit users are justifying their opinions as the reflection of their reasoning processes. In order to contribute further research in argumentation and reasoning in online platforms, the authors make the annotated dataset publicly available.Originality/valueTo best of the authors' knowledge, this study was one of a few studies whose purpose is to understand Reddit CMV users' reasoning processes. To understand how online users offer their reasons while providing their ideas is important to have effective communication processes and to improve online discussion experiences which are very common in today's digital era.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0330

PurposeThe reviews submitted by users are the foundation of user-generated content (UGC) platforms. However, the rapid growth of users brings the problems of information overload and spotty content, which makes it necessary for UGC platforms to screen out reviews that are really helpful to users. The authors put forward in this paper the factors influencing review helpfulness voting from the perspective of review characteristics and reviewer characteristics.Design/methodology/approachThis study uses 8,953 reviews from 20 movies listed on Douban.com with variables focusing on review characteristics and reviewer characteristics that affect review helpfulness. To verify the six hypotheses proposed in the study, Stata 14 was used to perform tobit regression.FindingsFindings show that review helpfulness is significantly influenced by the length, valence, timeliness and deviation rating of the reviews. The results also underlie that a review submitted by a reviewer who has more followers and experience is more affected by review characteristics.Originality/valuePrevious literature has discussed the factors that affect the helpfulness of reviews; however, the authors have established a new model that explores more comprehensive review characteristics and the moderating effect reviewer characteristics have on helpfulness. In this empirical research, the authors selected a UGC community in China as the research object. The UGC community may encourage users to write more helpful reviews by highlighting the characteristics of users. Users in return can use this to establish his/her image in the community. Future research can explore more variables related to users.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0186.

PurposeThe purpose of this paper is to develop an integrated research model that can explain current users' continuance intention regarding the adoption of virtual reality (VR).Design/methodology/approachThis study builds a new research model based on the perceived value theory. The model and the hypotheses formulated are tested using partial least squares analysis with 295 respondents.FindingsBoth utilitarian value and hedonic value have positive impacts on VR users' continuance intention. Among the four constructs reflecting VR device and service-related attributes, content quality and ease of use have significant impacts on the utilitarian value and hedonic value. Visual attractiveness is positively associated with hedonic value while portability affected only utilitarian value.Practical implicationsTo increase users' retention of VR, VR service providers should focus developing high-quality VR content, and VR device manufacturers should consider both ease of use and visual attractiveness of VR devices.Originality/valueThis study is the first empirical study to investigate user adoption of VR devices and services synthetically, as most of prior studies related to VR adoption mainly focused on VR devices or specific services.

PurposeThis exploratory study explores usage habits in music streaming applications (MSA) and their influence on various dimensions, such as: changes in personal music management (PMM) and musical information retrieval; sense of ownership over songs being stored on MSA; and privacy concerns when using those applications. It also investigates the potential effect of demographic factors and personal musical preferences on the tested variables.Design/methodology/approachThis is examined by using a mixed methodology that is consisted of two phases – qualitative and quantitative: The qualitative phase includes semistructured interviews with three MSA users, and the quantitative phase includes the distribution of closed-ended questionnaires among 192 users of MSA.FindingsIt seems that the musical information management methods have changed dramatically with the introduction of MSA. Also, the findings show that even though users are only moderately concerned about their privacy within MSA, they are willing to pay for premium services that will ensure privacy protection. Interestingly, personal musical preference was found to affect the belief in the potential of using MSA as part of a social activity and on the willingness to pay for premium services within them.Originality/valueThis paper represents an extension of the article by Weinberger and Bouhnik (2019). It appears to be the first academic research to investigate the issue of musical information retrieval and PMM among MSA. In addition, it is the first study that takes into account privacy-related issues among MSA users.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2020-0118

PurposeThe purpose of this paper is to examine the mechanism of how peer review helpfulness evaluation in online review communities is established, drawing upon the internalization and identification routes of persuasion effect.Design/methodology/approachBased on book reviews selected from Douban.com (a prestigious review community in China), this study used econometric models to investigate the effects of both reviews and reviewers’ characteristics on peer review helpfulness evaluation in review communities.FindingsReview internalization is more persuasive than reviewers’ identification in peer evaluations, in terms of both short and long reviews. Reviews with extreme negative ratings tend to obtain higher level of helpfulness evaluation than those with positive or moderate ratings. The influence of reviewers’ characteristics is a significant cue in helping consumers to establish the trust perception in the context of short reviews, while its function diminishes in the context of long reviews, thus suggesting the importance of reviewers’ identification for short reviews in review communities.Social implicationsThe findings will enhance current understanding of peer review review helpfulness evaluation in online review communities and help practitioners administrate community reviews intelligently, help members write better reviews and customers in their product browsing experience.Originality/valueFirst, this study enriches review evaluation research in review communities by demonstrating the importance of internalization and identification lens of persuasion effect when explaining review helpfulness; second, this study helps to confirm the existing findings that reviews with extreme negative ratings are more helpful than those with moderate or positive ratings in review communities; third, this study proposes a new perspective pertaining to the relationship between reviewers’ identification and helpfulness evaluation.

PurposeThe purpose of this paper is to analyze cloud reviews according to the end-user context and requirements.Design/methodology/approachpropose a comprehensive knowledge base composed of interconnected Web Ontology Language, namely, modular ontology for cloud service opinion analysis (SOPA). The SOPA knowledge base will be the basis of context-aware cloud service analysis using consumers' reviews. Moreover, the authors provide a framework to evaluate cloud services based on consumers' reviews opinions.FindingsThe findings show that there is a positive impact of personalizing the cloud service analysis by considering the reviewers' contexts in the performance of the framework. The authors also proved that the SOPA-based framework outperforms the available cloud review sites in term of precision, recall and F-measure.Research limitations/implicationsLimited information has been provided in the semantic web literature about the relationships between the different domains and the details on how that can be used to evaluate cloud service through consumer reviews and latent opinions. Furthermore, existing approaches are lacking lightweight and modular mechanisms which can be utilized to effectively exploit information existing in social media.Practical implicationsThe SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.Originality/valueThe SOPA ontology is capable of representing the content of a product/service as well as its related opinions, which are extracted from the customer's reviews written in a specific context. Furthermore, the SOPA-based framework facilitates the opinion based service evaluation through a large number of consumer's reviews and assists the end-users in analyzing services as per their requirements and their own context.

PurposeResearch on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.Design/methodology/approachAn enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing.FindingsThe enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools.Originality/valueThe study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.

PurposeThe purpose of this paper is to examine how subjective characteristics of social network sites (SNSs) affect consumers' positive and negative word-of-mouth (WOM) sharing.Design/methodology/approachThe data used for this study were obtained from an online survey with a sample size of 369 consumers. Structural equation modeling was performed to test hypotheses and examine the research questions.FindingsThe authors found that the perceived anonymity of an SNS is negatively correlated with its perceived interpersonal closeness of friends, and the number of friends in an SNS is positively correlated with its perceived interpersonal closeness of friends. With regard to positive WOM, the perceived anonymity of the SNS has a significant negative influence on consumers' WOM, and both perceived interpersonal closeness and the number of friends have a significant positive influence on consumers' WOM. But, in the case of negative WOM, only perceived interpersonal closeness of friends has a significant positive influence on consumers' WOM.Practical implicationsWhen attempting to promote positive WOM, marketers should choose consumers who possess the “right” subjective characteristics of SNSs (i.e. low anonymity, high interpersonal closeness of friends and a large number of friends). At the same time, marketers should monitor the emergence of consumers' negative WOM, especially those consumers who have a high level of interpersonal closeness of friends in SNSs, and respond to the content of negative WOM without delay.Originality/valueThis study investigates the influence that subjective characteristics of SNSs have on consumers' WOM sharing and therefore contributes to the literature on the antecedents of WOM generation and also contributes to the research that compares positive WOM with its negative counterpart.

PurposePeer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research's open peer review publishing platform.Design/methodology/approachPeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings.FindingsPeerJudge can predict F1000Research judgements from negative evaluations in reviewers' comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision.Originality/valuePeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments.

PurposeThrough a two-stage survey, this paper examines how researchers judge the quality of answers on ResearchGate Q&A, an academic social networking site.Design/methodology/approachIn the first-stage survey, 15 researchers from Library and Information Science (LIS) judged the quality of 157 answers to 15 questions and reported the criteria that they had used. The content of their reports was analyzed, and the results were merged with relevant criteria from the literature to form the second-stage survey questionnaire. This questionnaire was then completed by researchers recognized as accomplished at identifying high-quality LIS answers on ResearchGate Q&A.FindingsMost of the identified quality criteria for academic answers—such as relevance, completeness, and verifiability—have previously been found applicable to generic answers. The authors also found other criteria, such as comprehensiveness, the answerer's scholarship, and value-added. Providing opinions was found to be the most important criterion, followed by completeness and value-added.Originality/valueThe findings here show the importance of studying the quality of answers on academic social Q&A platforms and reveal unique considerations for the design of such systems.