Search

Abstract Nowadays, social networks have taken on an irreplaceable role as sources of information. Millions of people use them daily to find out about the issues of the moment. This success has meant that the amount of content present in social networks is unmanageable and, in many cases, fake or non-credible. Therefore, a correct pre-processing of the data is necessary if we want to obtain knowledge and value from these data sets. In this paper, we propose a new data pre-processing technique based on Big Data that seeks to solve two of the key concepts of the Big Data paradigm, data validity and credibility of the data and volume. The system is a Spark-based filter that allows us to flexibly select credible users related to a given topic under analysis, reducing the volume of data and keeping only valid data for the problem under study. The proposed system uses the power of word embeddings in conjunction with other text mining and natural language processing techniques. The system has been validated using three real-world use cases.

Sentiment analysis measures user experience on social media. With the emergence of pre-trained models, text classification tasks have become homogeneous, without a significant improvement in accuracy. Therefore, we present a hybrid model called PLASA to classify the sentiment polarity of short comments, particularly barrages. PLASA introduces a collaborative attention module that integrates information about relative position and knowledge from summarized lexicons to better adjust the relationship between word representations. Our model is evaluated using three new curated sentiment analysis datasets: SentiTikTok-2023 (4613 items), SentiBilibili-2023 (7755 items), and SentiWeibo-2023 (5614 items). Although the comment length varies across datasets, all maintain a consistent punctuation percentage at approximately 13%. Consequently, PLASA with the optimal combination demonstrates notable performance improvements compared to both the baseline and commonly used models. It achieves micro-F1 scores of 93.94%, 90.34%, and 88.79% on the respective datasets. We also observed that the representation capacity of the pre-trained model decreases as the text length increases. Moreover, the proposed collaborative attention module effectively addresses this limitation, as confirmed by our ablation study.

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. Our system is also available at http://scaiweb.cs.ucla.edu/covidsurveiller/. This article is part of the theme issue 'Data science approachs to infectious disease surveillance'.

This document is an editorial from the journal "Frontiers in Research Metrics & Analytics" titled "Text mining-based mental health research." It discusses the use of text mining and data analytics in the field of mental health research. The editorial highlights the importance of analyzing scientific literature datasets, social media user-generated content datasets, and the Bipolar Reddit community to gain insights into mental health conditions. The authors emphasize the need for interdisciplinary collaboration and technological advancements to address mental health issues effectively. They also acknowledge the potential challenges and risks involved in analyzing sentiments and topics related to mental health on social media platforms. [Extracted from the article]

Alter ego is a condition of someone who creates a new character with a conscious state. Original character role play is a game to create new imaginary characters that is used as research material for identification alter ego accounts. The negative effects of playing alter ego are stress, depression, and multiple personalities. Current research only focuses on the phenomenon and impacts of a role-playing game. We propose a new method to detect accounts of alter ego players in social media, especially Twitter. We develop an application to analyze the characteristics of alter ego accounts. Psychologists can use this application to discover the characteristics of alter ego accounts that are useful for analyzing personality so that the results can be used to appropriately handle alter ego players. Most user profiles, tweets, and platforms are used to detect account Twitter. This research proposes a new method using bio features as input data. We crawled and collected 565 bios from Twitter for one month. We observe the data to search for unique words and collect them into a classification dictionary. In this research, we use the cosine similarity method because this method is popular for detecting text and has a good performance in many cases. This research could identify alter ego accounts and other types of Twitter accounts. From the detection results of alter ego accounts, it is possible to analyze the characteristics of Twitter accounts. We use a sampling technique that takes 30% of the data as testing data. According to the results of the experiment cosine similarity obtained an accuracy of 0.95.

Analyzing community response has always played an important role in marketing and development of various products ranging from services to manufacturing. Analyzing the responses and interpreting them helps improve the quality of the product. This task is traditionally done by community managers using surveys and focus groups. One other hands-off solution would be using the technology to analyze sentiments from users' digital interactions in various online communities. Sentiment Analysis is the use of Natural Language Processing to process text to identify and retrieve sentiments behind the textual data. Sentiment Analysis can be approached either by supervised classification where a machine learning model is trained using labeled data or lexicon-based unsupervised analysis where a sentiment dictionary is used to find the overall sentiment. The idea is to build a system which presents community sentiment results on discussion threads from reddit on demand with the end user not to concern about data processing. We propose a streamlined pipeline architecture for a web application with several closely connected components in the backend that allows users to simply send the reddit discussion thread's URL, which the backend receives and scrapes the comments using the Reddit API, creates a dataset, cleans it and performs sentiment analysis using a RNNs. We used a Long Short-Term Memory (LSTM) a variant of RNN like Bidirectional RNNs and Multi-layered RNNs are also used to get better results. [ABSTRACT FROM AUTHOR]

The ability to accelerate the discovery of product innovation ideas is critical for manufacturers with short life cycles, such as smartphones. Companies with short product lifespans require innovative strategies to improve their products based on customer needs. Three-dimensional concurrent engineering (3DCE) has the potential to speed up the process of developing new products. On the other side, the availability of online data, such as social media and intellectual property, has the potential to identify and prioritize product, technology, and supplier opportunities in a cost-effective, quick, and real-time manner. This study proposes an online data-driven concurrent product, process, and supply chain design (3DCE) for linking customer requirements to potential technology and supplier opportunities in early-stage new product development. Three case studies on the leading smartphone industry demonstrate the proposed method's feasibility and effectiveness. To identify customer requirements, opinions from social media data are collected. Product opportunities are developed using latent Dirichlet allocation to identify product topics and sentiment analysis to assess satisfaction levels. Then, a novel approach by association rule mining is developed to mine specific customer requirements for each promising topic. In the 3DCE approach, the potential technology and supplier opportunities are tailored to specific customer requirements based on intellectual property mining. Finally, this study discovers improvement ideas and potential alternative solutions for product development with an unsupervised approach. The proposed method has also been validated and aligns with successful innovation products by Apple, Samsung, and Huawei.

In the age of digital economy, customers actively share their experiences and issues about products via online product reviews. Mining potential product improvement ideas from customer needs could provide valuable insights into new functionality expected by the markets. Numerous studies have attempted to identify customer needs using these reviews, but they paid less attention to the customer’s specific context in which the product was used. This study provides a novel approach for identifying customer needs based on both context information and product functions of target products. The context information and product functions are derived from online product reviews through linguistic pattern mining, whereby the customer needs are determined by the combination of extracted context information and product functions using a semantic embedding method and a clustering approach. A case study on the Amazon-Echo series was conducted to verify the applicability of the proposed approach. Consequently, we identified 1430 different customer needs, which could be used as an input for improving product design. This study is one of the first attempts to integrate context information for identifying customer needs. The proposed approach can be useful in the idea creation process for future product planning and is expected to add new empirical perspective for the e-commerce industry.

Topic trending is a popular research topic in recent years since, there are massive participations in social web sites, countless number of updates, news, opinions and product reviews are being constantly posted every day. The identification of popular topics discussed or posted on social media platforms is becoming more important as the new knowledge can be extracted from these findings. In this work, a novel method is proposed to extract popular topics from social media and determine the topic trending based on timeline using the Apriori algorithm. The approach uses Twitter's tweets as the dataset. The data is then pre-processed by undergoing several processes that include stop words removal, stemming, tokenization and Term Frequency-Inverse Document Frequency (TF - IDF) weighting. The k-means clustering is then performed to cluster each data that consists of processed keywords and collected every day. The popular topics will be then extracted from the clusters and the topic trends will be determined based on the observed frequent patterns and correlation between keywords by using the association rules. The performance of the proposed method is evaluated based on the similarity of the results with the current trends obtained from the Twitter site. The result from the findings shows that the proposed method is able to produce more enriched trends that are similar to current initial trends. [ABSTRACT FROM AUTHOR]

The number of Twitter users in Iraq has increased significantly in recent years. Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users. Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors. Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD). The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation. Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm. Various performance measures were calculated. The obtained accuracy, 78 %, indicates a promising potential application.

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]

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]

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.

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]

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]

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.

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.

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]

Sentiment analysis measures user experience on social media. With the emergence of pre-trained models, text classification tasks have become homogeneous, without a significant improvement in accuracy. Therefore, we present a hybrid model called PLASA to classify the sentiment polarity of short comments, particularly barrages. PLASA introduces a collaborative attention module that integrates information about relative position and knowledge from summarized lexicons to better adjust the relationship between word representations. Our model is evaluated using three new curated sentiment analysis datasets: SentiTikTok-2023 (4613 items), SentiBilibili-2023 (7755 items), and SentiWeibo-2023 (5614 items). Although the comment length varies across datasets, all maintain a consistent punctuation percentage at approximately 13%. Consequently, PLASA with the optimal combination demonstrates notable performance improvements compared to both the baseline and commonly used models. It achieves micro-F1 scores of 93.94%, 90.34%, and 88.79% on the respective datasets. We also observed that the representation capacity of the pre-trained model decreases as the text length increases. Moreover, the proposed collaborative attention module effectively addresses this limitation, as confirmed by our ablation study. [ABSTRACT FROM AUTHOR]

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.

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]

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]

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.

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.

As an emerging voice of the customer (VOC) containing feedback, such as opinions and expectations about products, social media data have the potential use for product improvement and new product development. However, most prior studies have focused on determining customer concerns, while neglecting to incorporate them into a systematic approach to identify product opportunities. In response, this paper suggests an approach to identify product opportunities from customer reviews in social media. This approach employs topic modeling to identify the product topics discussed by customers from largescale review posts related to a given product. A keygraph is then constructed based on the co-occurrences among the topics contained in each post. The chance discovery theory is then applied to generate new product opportunities from the chance nodes obtained from the keygraph. Our approach contributes to the systematic ideation process for product opportunity analysis based on large-scale and real-time VOC.

This study explores the interactive characteristics of the public, referencing existing data mining methods. This research attempts to develop a community data mining and integration technology to investigate the trends of global retail chain brands. Using social media mining and ensemble learning, it examines key image cues to highlight the various reasons motivating participation by fans. Further, it expands the discussion on image and marketing cues to explore how various social brands induce public participation and the evaluation of information efficiency. This study integrates random decision forests, extreme gradient boost, and adaboost for statistical verification. From 1 January 2011 to 31 December 2019, the studied brands published a total of 25,538 posts. The study combines community information and participation in its research framework. The samples are divided into three categories: retail food brand, retail home improvement brand, and retail warehouse club brand. This research draws on brand image and information cue theory to design the theoretical framework, and then uses behavior response factors for the theoretical integration. This study contributes a model that classifies brand community posts and mines related data to analyze public needs and preferences. More specifically, it proposes a framework with supervised and ensemble learning to classify information users′ behavioral characteristics.

In the ever-connected social networking era, terrorists exploit social media platforms via sophisticated approaches. To curb these activities, a rich collection of computational methods was developed. This article surveys the use of social media by terrorists, followed by a temporal classification framework that overviews computational countermeasures at four major stages, including inception of an attack, immediately before an attack, onset of an attack, and after an attack. The literature surveyed was organized around the four temporal stages. The resulting survey is summarized in a table with the main technology used in each stage based on the time of the attack. [ABSTRACT FROM AUTHOR]

Social media stores a significant amount of information which can be used for extraction of specific knowledge. A variety of topics that arise there concerns a lot of everyday life aspects, including urban-related problems. In this work, we demonstrate the way of using the texts from social media on the topic of housing and utility problems, such as litter on the streets, graffiti on a public building or noisy neighbours. Our aim is to develop an approach based on machine learning to automatically filter such citizen messages and classify them into several categories. To achieve this, we solve the classification problem with an almost unlimited number of negative categories using the One-Class approach and combine data from several resources to construct proper text embedding by combining results from the guided topic model and deep neural pretrained BERT method. Comparison with statistics taken from the official site indicates that the distributions of posts on each problem category are similar for districts of Saint-Petersburg [ABSTRACT FROM AUTHOR]

The huge volume of user-generated data on social media is the result of the aggregation of users’ personal backgrounds, past experiences, and daily activities. This huge size of the generated data, the so-called “big data,” has been studied and investigated intensively during the past few years. In spite of the impression one may get from the media, a great deal of data processing has not been uncovered by existing techniques of data engineering and processing. However, very few scholars have tried to do so, especially from the perspective of multiple-criteria decision-making (MCDM). These MCDM methods can derive influence relationships and weights associated with aspects and criteria, which can hardly be achieved by traditional data analytics and statistical approaches. Therefore, in this paper, we aim to propose an analytic framework to mine social networks, feed the meaningful information via MCDM methods based on a theoretical framework, derive causal relationships among the aspects of the theoretical framework, and finally compare the causal relationships with a social theory. Latent Dirichlet allocation (LDA) will be adopted to derive topic models based on the data retrieved from social media. By clustering the topics into aspects of the social theory, the probability associated with each aspect will be normalized and then transformed to a Likert-type 5-point scale. Afterwards, for every topic, the feature importance of all other topics will be derived using the random forest (RF) algorithm. The feature importance matrix will be transformed to the initial influence matrix of the decision-making trial and evaluation laboratory (DEMATEL). The influence relationships among the aspects and criteria and influence weights can then be derived by using the DEMATEL-based analytic network process (DANP). The influence weight versus each criterion can be derived by using DANP. To verify the feasibility of the proposed framework, Taiwanese users’ attitudes toward air pollution will be analyzed based on the value–belief–norm (VBN) theory by using social media data retrieved from Dcard (dcard.tw). Based on the analytic results, the causal relationships are fully consistent with the VBN framework. Further, the mutual influences derived in this work that were seldom discussed by earlier works, i.e., the mutual influences between altruistic concerns and egoistic concerns, as well as those between altruistic concerns and biosphere concerns, are worth further investigation in future.

Abstract Background It is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to ‘engage’ social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement. Methods We analyze over 45,000 Facebook posts from 72 Facebook accounts belonging to 24 health agencies. Account usage, user activity, sentiment and content of these posts are studied. We use the hurdle regression model to identify factors associated with the level of engagement and Cox proportional hazards model to identify factors associated with duration of engagement. Results In our analysis we find that agencies and accounts vary widely in their usage of social media and activity they generate. Statistical analysis shows, for instance, that Facebook posts with more visual cues such as photos or videos or those which express positive sentiment generate more engagement. We further find that posts on certain topics such as occupation or organizations negatively affect the duration of engagement. Conclusions We present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual's personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users' profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.

This research proposes a framework for the fashion brand community to explore public participation behaviors triggered by brand information and to understand the importance of key image cues and brand positioning. In addition, it reviews different participation responses (likes, comments, and shares) to build systematic image and theme modules that detail planning requirements for community information. The sample includes luxury fashion brands (Chanel, Hermès, and Louis Vuitton) and fast fashion brands (Adidas, Nike, and Zara). Using a web crawler, a total of 21,670 posts made from 2011 to 2019 are obtained. A fashion brand image model is constructed to determine key image cues in posts by each brand. Drawing on the findings of the ensemble analysis, this research divides cues used by the six major fashion brands into two modules, image cue module and image and theme cue module, to understand participation responses in the form of likes, comments, and shares. The results of the systematic image and theme module serve as a critical reference for admins exploring the characteristics of public participation for each brand and the main factors motivating public participation.

Background: Although birth defects are the leading cause of infant mortality in the United States, methods for observing human pregnancies with birth defect outcomes are limited.Objective: The primary objectives of this study were (i) to assess whether rare health-related events-in this case, birth defects-are reported on social media, (ii) to design and deploy a natural language processing (NLP) approach for collecting such sparse data from social media, and (iii) to utilize the collected data to discover a cohort of women whose pregnancies with birth defect outcomes could be observed on social media for epidemiological analysis.Methods: To assess whether birth defects are mentioned on social media, we mined 432 million tweets posted by 112,647 users who were automatically identified via their public announcements of pregnancy on Twitter. To retrieve tweets that mention birth defects, we developed a rule-based, bootstrapping approach, which relies on a lexicon, lexical variants generated from the lexicon entries, regular expressions, post-processing, and manual analysis guided by distributional properties. To identify users whose pregnancies with birth defect outcomes could be observed for epidemiological analysis, inclusion criteria were (i) tweets indicating that the user's child has a birth defect, and (ii) accessibility to the user's tweets during pregnancy. We conducted a semi-automatic evaluation to estimate the recall of the tweet-collection approach, and performed a preliminary assessment of the prevalence of selected birth defects among the pregnancy cohort derived from Twitter.Results: We manually annotated 16,822 retrieved tweets, distinguishing tweets indicating that the user's child has a birth defect (true positives) from tweets that merely mention birth defects (false positives). Inter-annotator agreement was substantial: κ = 0.79 (Cohen's kappa). Analyzing the timelines of the 646 users whose tweets were true positives resulted in the discovery of 195 users that met the inclusion criteria. Congenital heart defects are the most common type of birth defect reported on Twitter, consistent with findings in the general population. Based on an evaluation of 4169 tweets retrieved using alternative text mining methods, the recall of the tweet-collection approach was 0.95.Conclusions: Our contributions include (i) evidence that rare health-related events are indeed reported on Twitter, (ii) a generalizable, systematic NLP approach for collecting sparse tweets, (iii) a semi-automatic method to identify undetected tweets (false negatives), and (iv) a collection of publicly available tweets by pregnant users with birth defect outcomes, which could be used for future epidemiological analysis. In future work, the annotated tweets could be used to train machine learning algorithms to automatically identify users reporting birth defect outcomes, enabling the large-scale use of social media mining as a complementary method for such epidemiological research. [ABSTRACT FROM AUTHOR]

Embracing sustainability in the 21st century entails developing environmental identity, so that attitudes towards energy sustainability result from the core values of one’s individual and social identity. This study aims to explore the shift in the formation of environmental identity and attitudes towards energy sustainability throughout the course of the two-year study period (2018–2020). A dataset of 8,677,961 tweets, Facebook posts and comments and 325,228 news articles was collected to carry out quantitative analysis of the distribution of the posts, likes, and comments. A correlation with media coverage of energy and green topics was sought to establish the impact of the media on public debate. A qualitative analysis of posts and tweets was carried out to establish dominant themes. The findings of the study reveal that both positive attitudes towards energy sustainability and environmental identity have been consolidated throughout the two-year study period. Social media users are not only increasingly interested in green issues but also produce more reactions towards posts related to sustainability topics. The results also suggest that sustainable values and green behavior are independent from the media coverage of current events and the perceived threat to one’s health from COVID-19. Social networking sites provide a context in which users not only reinforce their beliefs and values, but also mimic the behavior of other users, which leads to the formation of a social media identity bubble that reinforces shared identity—in this case, environmental identity. This study offers a multidisciplinary perspective on sustainable development that will be able to drive equitable energy security and environmental security.

The ability to accelerate the discovery of product innovation ideas is critical for manufacturers with short life cycles, such as smartphones. Companies with short product lifespans require innovative strategies to improve their products based on customer needs. Three-dimensional concurrent engineering (3DCE) has the potential to speed up the process of developing new products. On the other side, the availability of online data, such as social media and intellectual property, has the potential to identify and prioritize product, technology, and supplier opportunities in a cost-effective, quick, and real-time manner. This study proposes an online data-driven concurrent product, process, and supply chain design (3DCE) for linking customer requirements to potential technology and supplier opportunities in early-stage new product development. Three case studies on the leading smartphone industry demonstrate the proposed method's feasibility and effectiveness. To identify customer requirements, opinions from social media data are collected. Product opportunities are developed using latent Dirichlet allocation to identify product topics and sentiment analysis to assess satisfaction levels. Then, a novel approach by association rule mining is developed to mine specific customer requirements for each promising topic. In the 3DCE approach, the potential technology and supplier opportunities are tailored to specific customer requirements based on intellectual property mining. Finally, this study discovers improvement ideas and potential alternative solutions for product development with an unsupervised approach. The proposed method has also been validated and aligns with successful innovation products by Apple, Samsung, and Huawei. [ABSTRACT FROM AUTHOR]

Due to growing concerns about electronic waste (e-waste), manufacturers are constantly pressured to adopt sustainable product development. In this regard, a product-service system (PSS) is a circular economy business model that offers a potential solution. Consumers are considered to play a vital role as key stakeholders in waste management and PSS. Although a substantial amount of research has been conducted on the consumer-facing sustainability aspects of PSS, most studies have neglected waste management in product-oriented PSS. On the other hand, research has either discussed sustainability theoretically or used traditional methods such as surveys to obtain consumers' opinions regarding the functionality of PSSs and the sustainability of their programs. Consequently, in the present study, the success of product-oriented PSSs in various aspects of waste management is evaluated utilizing a method centered around social media as the primary source of consumer feedback through machine learning (ML) algorithms and artificial intelligence (AI) methods. The novelty of this study stems from its determination of the significance and level of consumer satisfaction over an extended period to identify major topics among consumer opinions on social media. The proposed method was used in Waste Electrical and Electronic Equipment (WEEE) to evaluate leading corporations, such as Apple, Samsung, and Google. In addition, the Delphi method was implemented, and the reasons behind extracted trends are discussed based on experts' opinions. The results indicate that 'cost,' 'trust,' 'convenience,' 'awareness and advertisement,' 'benefits,' and 'data privacy and security' were the major topics identified. Moreover, 'cost' was the most important factor in determining whether consumers participate in waste management programs. Finally, this study can provide businesses with a six-year guide on how consumers have responded to their programs and concepts regarding WEEE. [ABSTRACT FROM AUTHOR]