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Background: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called "long-COVID." Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media. Objective: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users' self-reports to support hypothesis generation for drug repurposing, by incorporating patients' experiences. Methods: We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the "/r/covidlonghaulers" subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters. Results: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. "Histamine antagonists," "famotidine," "magnesium," "vitamins," and "steroids" were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns. Conclusions: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users. [ABSTRACT FROM AUTHOR]

Purpose: Treatment with the tyrosine kinase inhibitor (TKI) imatinib in patients with gastrointestinal stromal tumours (GIST) causes symptoms that could negatively impact health-related quality of life (HRQoL). Treatment-related symptoms are usually clinician-reported and little is known about patient reports. We used survey and online patient forum data to investigate (1) prevalence of patient-reported symptoms; (2) coverage of symptoms mentioned on the forum by existing HRQoL questionnaires; and (3) priorities of prevalent symptoms in HRQoL assessment.Methods: In the cross-sectional population-based survey study, Dutch GIST patients completed items from the EORTC QLQ-C30 and Symptom-Based Questionnaire (SBQ). In the forum study, machine learning algorithms were used to extract TKI side-effects from English messages on an international online forum for GIST patients. Prevalence of symptoms related to imatinib treatment in both sources was calculated and exploratively compared.Results: Fatigue and muscle pain or cramps were reported most frequently. Seven out of 10 most reported symptoms (i.e. fatigue, muscle pain or cramps, facial swelling, joint pain, skin problems, diarrhoea, and oedema) overlapped between the two sources. Alopecia was frequently mentioned on the forum, but not in the survey. Four out of 10 most reported symptoms on the online forum are covered by the EORTC QLQ-C30. The EORTC-SBQ and EORTC Item Library cover 9 and 10 symptoms, respectively.Conclusion: This first overview of patient-reported imatinib-related symptoms from two data sources helps to determine coverage of items in existing questionnaires, and prioritize HRQoL issues. Combining cancer-generic instruments with treatment-specific item lists will improve future HRQoL assessment in care and research in GIST patients using TKI. [ABSTRACT FROM AUTHOR]

Purpose—Using a sample of universities from Europe and North America the research herein seeks to understand the content trends of university brand pages through an exploration of the social community and the measurement of user participation and behavior. The analysis relies on an artificial intelligence approach. Through the verification of interactions between users and content on the university brand pages, recommendations are made, which aim to ensure the pages meet the needs of users in the future. Design/methodology/approach—The study sample was drawn from six well-known universities in Europe and North America. The content of 23,158 posts made over the course of nine years between 1 January 2011 to 31 December 2019 was obtained by a web crawler. Concepts in the fields of computer science, data mining, big data and ensemble learning (Random Decision Forests, eXtreme Gradient Boosting and AdaBoost) were combined to analyze the results obtained from social media exploration. Findings—By exploring community content and using artificial intelligence analysis, the research identified key information on the university brand pages that significantly affected the cognition and behavior of users. The results suggest that distinct levels of user participation arise from the use of different key messages on the university fan page. The interactive characteristics identified within the study sample were classified as one of the following module-types: (a) information and entertainment satisfaction module, (b) compound identity verification module or (c) compound interactive satisfaction module. Research limitations/implications—The study makes a contribution to the literature by developing a university community information interaction model, which explains different user behaviors, and by examining the impact of common key (image) clues contained within community information. This work also confirms that the behavioral involvement of users on the university’s brand page is closely related to the information present within the university community. A limitation of the study was the restriction of the sample to only European and North American cultural and economic backgrounds and the use of Facebook as the sole source of information about the university community. Practical implications—Practically, the research contributes to our understanding of how, in official community interactions, user interactions can be directed by features such as information stimuli and brand meanings. In addition, the work clarifies the relationship between information and user needs, explaining how the information characteristics and interaction rules particular to a given school can be strengthened in order to better manage the university brand page and increase both the attention and interaction of page users. Originality/value—This research provides an important explanation of the role of key information on the university fan pages and verifies the importance of establishing key (image) clues in the brand community, which in turn affect user cognition and interaction. Although related research exists on information manipulation and the importance of online communities, few studies have directly discussed the influence of key information on the fan pages of university brands. Therefore, this research will help to fill gaps in the literature and practice by examining a specific context, while at the same time providing a valuable and specific reference for the community operation and management of other related university brands.

Chatbots have been widely employed across a wide variety of companies and industries, from small- and medium-sized businesses to large corporations, and from e-commerce to financial institutions. Although chatbots have proven to be far more efficient and quicker than human agents, they do not always provide customers with a satisfactory experience because they lack a personal touch. Customer issues are often left unresolved and many are unsatisfied with chatbot services. This is unfavorable for firms that use chatbots for customer services as this jeopardizes their relationship with valued consumers. Thus, customer input is essential to streamline the product innovation process. This study uses a hybrid method involving lexicon-based TextBlob and logistic regression techniques to identify the sentiments of consumers toward chatbots for customer services based on user-generated content on Twitter. The results show that although people generally have positive encounters with chatbots, the gap between positive and negative sentiments is relatively small. This research provides insights that businesses can use to improve chatbot technology based on the voice of the customer to provide users with higher quality customer services in the future, especially since unsatisfied customers could be a threat to a business's performance. [ABSTRACT FROM AUTHOR]

With the rise of social media, brand communication has undergone tremendous changes. In particular, small brands have been affected severely. Given this, this study investigated strategies for how small brands can survive in the digital age using a case study of Louise Kennedy, a representative Irish fashion designer brand. After examining a total of 2,899 tweets, the study finds a strategic mechanism for heritage branding on social media, and confirms the important role of cultural heritage in the success of small brands. As a result, the study extends prior studies on heritage branding to fashion marketing on social media. Also, it provides actionable insights for small brands to survive the social media firestorm. [ABSTRACT FROM AUTHOR]

The use of social media has accelerated information sharing and instantaneous communications. The low barrier to enter social media enables more users to participate and makes them stay engaged longer, while incentivizing individuals with a hidden agenda to use disinformation to manipulate information and influence opinions. Disinformation, such as fake news, hoaxes, and conspiracy theories, has increasingly been weaponized to divide people and create detrimental societal effects. Therefore, it is imperative to understand disinformation and systematically investigate how we can improve resistance against it, taking into account the tension between the need for information and the need for security and protection against disinformation. In this survey, we look into the concepts, methods, and recent advancements of detecting disinformation from a computational perspective. We will also discuss open issues and future research directions for combating disinformation on social media.

Point-of-Interest recommendation is an efficient way to explore interesting unknown locations in social media mining of social networks. In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method. [ABSTRACT FROM AUTHOR]

Climate change remains one of the most important challenges of the 21st century, both for organizations as well as for society. While many corporates around the globe embed sustainability goals into their strategies, inclusive research on the public perceptions of the same is limited. Aiming to fill this gap, we apply an innovative natural language processing approach to determine the persuasiveness of corporate climate agendas. Based on public opinion data from various online platforms (n=5284), research is conducted to understand whether stakeholders structurally support or oppose the sustainability agendas. For this purpose, reactions and statements of users were mined, subjected to a sentiment analysis and have been examined concerning their polarity both platform-specific and cross-platform. While on the one hand the research helps company representatives to better understand the effectiveness of their proposed agenda and strategic positioning, our approach also challenges traditional ways of collecting data and measuring public opinion through interviews, questionnaires or surveys. Compared to other studies dealing with opinion research, our analysis sets new academic impulses by analyzing the very topical and demanded issue of evaluating sustainability campaigns. The study does not only provide evidence of an overall optimistic attitude towards corporate sustainability targets, but also sheds light on the polarity of public opinion and the share of perceptions. We were able to show that with an overarching average ratio of 4:1, sustainability ambitions are supported, whereby concerns on average can only be found in 1 of out 10 reactions, which contributes significant insights for steering transitioning companies as well as for the corresponding management by campaign leaders. [ABSTRACT FROM AUTHOR]

The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. [ABSTRACT FROM AUTHOR]

• Pharmaceutical companies must address the unmet medical needs of patients. • Patient-centered drug development has gained traction in recent years. • We discuss the fundamental methods of social media mining. • We present five use cases to foster patient-centered drug discovery. The incorporation of patients' perspectives into drug discovery and development has become critically important from the viewpoint of accounting for modern-day business dynamics. There is a trend among patients to narrate their disease experiences on social media. The insights gained by analyzing the data pertaining to such social-media posts could be leveraged to support patient-centered drug development. Manual analysis of these data is nearly impossible, but artificial intelligence enables automated and cost-effective processing, also referred as social media mining (SMM). This paper discusses the fundamental SMM methods along with several relevant drug-development use cases. [ABSTRACT FROM AUTHOR]

Objective: Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs.Materials and Methods: We present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average 'natural balance' with ADEs present in about 7% of the tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all 3 tasks.Results: The system presented achieved state-of-the-art performance on comparable datasets and scored a classification performance of F1 = 0.63, span extraction performance of F1 = 0.44 and an end-to-end entity resolution performance of F1 = 0.34 on the presented dataset.Discussion: The performance of the models continues to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements.Conclusion: Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task. [ABSTRACT FROM AUTHOR]

Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.

Health authorities worldwide strive to detect Influenza prevalence as early as possible in order to prepare for it and minimize its impacts. To this end, we address the Influenza prevalence surveillance and prediction problem. In this paper, we develop a new Influenza prevalence prediction model, called Tweetluenza, to predict the spread of the Influenza in real time using cross-lingual data harvested from Twitter data streams with emphases on the United Arab Emirates (UAE). Based on the features of tweets, Tweetluenza filters the Influenza tweets and classifies them into two classes, reporting and non-reporting. To monitor the growth of Influenza, the reporting tweets were employed. Furthermore, a linear regression model leverages the reporting tweets to predict the Influenza-related hospital visits in the future. We evaluated Tweetluenza empirically to study its feasibility and compared the results with the actual hospital visits recorded by the UAE Ministry of Health. The results of our experiments demonstrate the practicality of Tweetluenza, which was verified by the high correlation between the Influenza-related Twitter data and hospital visits due to Influenza. Furthermore, the evaluation of the analysis and prediction of Influenza shows that combining English and Arabic tweets improves the correlation results.

Social media, including Twitter, has become an important source for disaster response. Yet most studies focus on a very limited amount of geotagged data (approximately 1% of all tweets) while discarding a rich body of data that contains location expressions in text. Location information is crucial to understanding the impact of disasters, including where damage has occurred and where the people who need help are situated. In this paper, we propose a novel two-stage machine learning- and deep learning-based framework for power outage detection from Twitter. First, we apply a probabilistic classification model using bag-of-ngrams features to find true power outage tweets. Second, we implement a new deep learning method–bidirectional long short-term memory networks–to extract outage locations from text. Results show a promising classification accuracy (86%) in identifying true power outage tweets, and approximately 20 times more usable tweets can be located compared with simply relying on geotagged tweets. The method of identifying location names used in this paper does not require language- or domain-specific external resources such as gazetteers or handcrafted features, so it can be extended to other situational awareness analyzes and new applications.

Social media mining is the process of collecting large datasets from user-generated content and extracting and analyzing social media interactions to recognize meaningful patterns in individual and social behavior. Everyday, more contents related to social media are generated by social media users (e.g., Facebook, Twitter). As the components of big data continue to expand, the task of extracting useful information becomes critical. Topic extraction refers to the process of extracting main topics from the pool of news feed and a typical method to perform topic extraction is through clustering. Clustering defines or organizes a group of patterns or objects into clusters, allows high-dimensional data to be presented in an apprehensive fashion to humans. Although effective, the performance of the k-means clustering algorithm depends heavily on the initial centroids and the number of clusters, k. Recently, several effective supervised and unsupervised machine learning methods have been developed in the domain of topics extraction. However, less works have been conducted in applying multiobjective based algorithm for topic extraction. Most of these algorithms are not optimized, even if they are, they are only optimized by using a single objective method and may underperform when solving real-world problems which are typically multi-objectives in nature. This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. Then, the mapping percentages between the predefined and produced clusters are used to assess the performance of the proposed algorithm. The best mapping percentage of 62.7 obtained using the proposed algorithm when k = 15 is obtained to outperform the performance of the generic k-means. The top five most representative words from each cluster are also extracted and validated by computing the number of tweets related to the predefined tags. [ABSTRACT FROM AUTHOR]

This paper explores the public perception of economic measures implemented as a reaction to the COVID-19 pandemic in Poland in March–June 2020. A mixed-method approach was used to analyse big data coming from tweets and Facebook posts related to the mitigation measures to provide evidence for longitudinal trends, correlations, theme classification and perception. The online discussion oscillated around political and economic issues. The implementation of the anti-crisis measures triggered a barrage of criticism pointing out the shortcomings and ineffectiveness of the solutions. The revised relief legislation was accompanied by a wide-reaching informative campaign about the relief package, which decreased negative sentiment. The analysis also showed that with regard to online discussion about risk mitigation, social media users are more concerned about short-term economic and social effects rather than long-term effects of the pandemic. The findings have significant implications for the understanding of public sentiment related to the COVID-19 pandemic, economic attitudes and relief support implemented to fight the adverse effects of the pandemic. [ABSTRACT FROM AUTHOR]

The amount of content on social media platforms such as Twitter is expanding rapidly. Simultaneously, the lack of patient information seriously hinders the diagnosis and treatment of rare/intractable diseases. However, these patient communities are especially active on social media. Data from social media could serve as a source of patient-centric knowledge for these diseases complementary to the information collected in clinical settings and patient registries, and may also have potential for research use. To explore this question, we attempted to extract patient-centric knowledge from social media as a task for the 3-day Biomedical Linked Annotation Hackathon 6 (BLAH6). We selected amyotrophic lateral sclerosis and multiple sclerosis as use cases of rare and intractable diseases, respectively, and we extracted patient histories related to these health conditions from Twitter. Four diagnosed patients for each disease were selected. From the user timelines of these eight patients, we extracted tweets that might be related to health conditions. Based on our experiment, we show that our approach has considerable potential, although we identified problems that should be addressed in future attempts to mine information about rare/intractable diseases from Twitter.

Despite a growing number of natural language processing shared-tasks dedicated to the use of Twitter data, there is currently no ad-hoc annotation tool for the purpose. During the 6th edition of Biomedical Linked Annotation Hackathon (BLAH), after a short review of 19 generic annotation tools, we adapted GATE and TextAE for annotating Twitter timelines. Although none of the tools reviewed allow the annotation of all information inherent of Twitter timelines, a few may be suitable provided the willingness by annotators to compromise on some functionality.

Customers' demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management.

Social media has already become one of the mainstream enterprise marketing channels recently. That consists of various media elements, such as text, picture, and even newly developed instant music video etc. Although several text or picture mining techniques could be directly utilized to analyse online user comments, few researches focus on how to improve the marketing performance of social media platforms through a multimedia approach. Therefore, this paper studies the social media marketing problem of user generated instant music video. A hybrid variable-scale clustering algorithm (HVSC) is proposed to analyse user feature through both textual and video content. Combining with the information dissemination characteristics of social media platforms, we also put forth a marketing strategy that intensively enlarges the transmission audience of influential UGC videos. Experiment results show that the HVSC is able to support managers to discover the target potential customer base of each UGC video following their music preference and current interest/concerns. Finally, according to the content relevance and customer influence, the video producers’ incentive mechanism is further discussed.

Coronavirus 2019, or COVID-19, is a contagious disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With origins in Wuhan, China, this disease has since spread globally, resulting in the ongoing 2019–2020 coronavirus pandemic. As of May 3, 2020, the Ministry of Health and Family Welfare confirmed a total of 39,980 positive COVID-19 cases and 1,301 deaths in India (more than 3.42 million positive COVID-19 cases resulting in more than 243,000 deaths worldwide). To flatten the curve, India has been locking down its country from March 24 to May 17, 2020. This study collected "COVID-19 in India" related tweets (totaling 410,643 tweets in English) from March 22 to April 21, 2020 to gauge the unknowns and contexts associated with public sentiments during the lockdown. This work contributes to the growing body of studies on COVID-19 social media mining by extracting emotions and sentiments over time, which could potentially shed some lights on the contexts of expressions during pandemic. [ABSTRACT FROM AUTHOR]

Traffic safety culture has emerged as a significant factor in support of the recognition of existing traffic safety policies and programs and as a contextual variable to describe high-risk behaviors of drivers. However, it is an arduous task to understand people's beliefs and attitudes that collectively make up traffic safety-related influences. The growing acceptance of social media platforms such as Facebook and Twitter have spurred great interest in massive data collection and its use in conducting a comprehensive analysis of people's viewpoints on a particular topic. This study applied social media mining to shed light on traffic safety culture in the state of Washington. To this end, the researchers collected traffic safety–related tweets over the past 4 years in Washington based on a set of keywords. After cleaning and reprocessing, the collected tweets were used in sentiment analysis using Linguistic Inquiry and Word Count (LIWC) to measure the public's beliefs and attitudes toward the importance of traffic safety, possibility of zero fatalities, usefulness of traffic law enforcement, and six types of high-risk behaviors, including impaired driving, speeding, distracted driving, unrestrained vehicle occupants, teenage drivers, and older drivers. Next, the topic modeling technique was applied to discover important latent topics related to traffic safety culture. This research, which capitalizes on social media mining, overcomes the limitations of the conventional survey method, which are time-consuming and costly. The generated information may facilitate understanding of the barriers to preventing fatal traffic accidents in Washington and around the country and developing solutions to overcome them. [ABSTRACT FROM AUTHOR]

• Up-to-date literature review of basic research and application domains in social networks. • Definition of a new set of metrics to measure the capacity of SNA frameworks and tools. • Quantitative analysis of social network analysis tools and frameworks (SNA). • Evaluation of 20 popular SNA software tools according to the new set of metrics. • SNA software technology assessment. Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA); (2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability , and Visualization , which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area. [ABSTRACT FROM AUTHOR]