Showing papers on "Social media published in 2022"

The psychological drivers of misinformation belief and its resistance to correction

Abstract: Misinformation has been identified as a major contributor to various contentious contemporary events ranging from elections and referenda to the response to the COVID-19 pandemic. Not only can belief in misinformation lead to poor judgements and decision-making, it also exerts a lingering influence on people’s reasoning after it has been corrected — an effect known as the continued influence effect. In this Review, we describe the cognitive, social and affective factors that lead people to form or endorse misinformed views, and the psychological barriers to knowledge revision after misinformation has been corrected, including theories of continued influence. We discuss the effectiveness of both pre-emptive (‘prebunking’) and reactive (‘debunking’) interventions to reduce the effects of misinformation, as well as implications for information consumers and practitioners in various areas including journalism, public health, policymaking and education. Misinformation is influential despite unprecedented access to high-quality, factual information. In this Review, Ecker et al. describe the cognitive, social and affective factors that drive sustained belief in misinformation, synthesize the evidence for interventions to reduce its effects and offer recommendations for information consumers and practitioners.

Responsible Conduct of Research

Abstract: Responsible Conduct of Research provides an overview of ethical, legal, and social issues in scientific research for science students, trainees, and professional scientists. Written by two leading scholars in the field of research ethics, one with a background in natural science and the other with a background in philosophy and law, the book incorporates insights from these diverse disciplines throughout the text. The book provides in-depth analyses of a wide array of topics, including ethical theory and decision-making, misconduct, questionable research practices, research record-keeping, data sharing, data auditing, reproducibility, authorship, publication, peer review, intellectual property, conflict of interest, mentoring, safe research environment, animal experimentation, research with human subjects, and social responsibility. The book also includes interesting case studies and provocative questions at the end of each chapter that can serve as a basis for further analysis and discussion. The concluding chapter of the book describes some steps that researchers, institutional officials, government agencies, and scientific organizations can take to promote ethical conduct in scientific research. The 4th edition of Responsible Conduct of Research includes updated references and discussions of new and evolving topics, such as digital image manipulation, text recycling (sometimes called self-plagiarism), retractions, publication on pre-print servers, sexual and other forms of harassment, research with human biological samples, revisions to the Common Rule for research with human subjects, dual use research, the COVID-19 pandemic, providing science advice, and interactions with the media.

A survey on sentiment analysis methods, applications, and challenges

Abstract: The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

[2021 ESC Guidelines on cardiovascular disease prevention in clinical practice].

Abstract: Since 1990, the U.S. News and World Report (USNWR) has been publishing rankings of US adult and children’s hospitals. The aim of this study was to analyze the association between hospital Twitter metrics and the 2020 USNWR hospital cardiology and heart surgery ranking. We collected data on the cardiology and heart surgery overall ranking score and expert opinion. Twitter metrics were obtained on October 20, 2020, and included time on Twitter, number of followers, accounts being followed, total tweets, reach score (difference between followers and followed), and annual tweet rate (total tweets divided by time on Twitter). The final cohort consisted of 463 hospitals (48 of which were top-ranking hospitals). A significant positive relation was observed with Twitter metrics and hospital ranking. On multivariable regression after adjusting for time on Twitter, the overall score was independently associated with annual tweet rate and reach score (β=12.45% and β=0.34% for each 1,000 tweets per year and 10,000 reach score accounts; P<.001). Similarly, expert opinion was independently associated with annual tweet rate and reach score (β=0.025% and β=0.002% for each 1000 tweets per year and 10,000 reach score accounts; P<.001). Our results emphasize how hospital leaders may leverage social media platforms as an important medium to disseminate accomplishments and increase their visibility and reputation, potentially translating to higher USNWR ranking.

Global COVID-19 Vaccine Acceptance: A Systematic Review of Associated Social and Behavioral Factors

Abstract: COVID-19 vaccines have met varying levels of acceptance and hesitancy in different parts of the world, which has implications for eliminating the COVID-19 pandemic. The aim of this systematic review is to examine how and why the rates of COVID-19 vaccine acceptance and hesitancy differ across countries and continents. PubMed, Web of Science, IEEE Xplore and Science Direct were searched between 1 January 2020 and 31 July 2021 using keywords such as “COVID-19 vaccine acceptance”. 81 peer-reviewed publications were found to be eligible for review. The analysis shows that there are global variations in vaccine acceptance among different populations. The vaccine-acceptance rates were the highest amongst adults in Ecuador (97%), Malaysia (94.3%) and Indonesia (93.3%) and the lowest amongst adults in Lebanon (21.0%). The general healthcare workers (HCWs) in China (86.20%) and nurses in Italy (91.50%) had the highest acceptance rates, whereas HCWs in the Democratic Republic of Congo had the lowest acceptance (27.70%). A nonparametric one-way ANOVA showed that the differences in vaccine-acceptance rates were statistically significant (H (49) = 75.302, p = 0.009*) between the analyzed countries. However, the reasons behind vaccine hesitancy and acceptance were similar across the board. Low vaccine acceptance was associated with low levels of education and awareness, and inefficient government efforts and initiatives. Furthermore, poor influenza-vaccination history, as well as conspiracy theories relating to infertility and misinformation about the COVID-19 vaccine on social media also resulted in vaccine hesitancy. Strategies to address these concerns may increase global COVID-19 vaccine acceptance and accelerate our efforts to eliminate this pandemic.

AI-synthesized faces are indistinguishable from real faces and more trustworthy

Abstract: Artificial intelligence (AI)–synthesized text, audio, image, and video are being weaponized for the purposes of nonconsensual intimate imagery, financial fraud, and disinformation campaigns. Our evaluation of the photorealism of AI-synthesized faces indicates that synthesis engines have passed through the uncanny valley and are capable of creating faces that are indistinguishable—and more trustworthy—than real faces.

A Deadly Infodemic: Social Media and the Power of COVID-19 Misinformation

Abstract: COVID-19 is currently the third leading cause of death in the United States, and unvaccinated people continue to die in high numbers. Vaccine hesitancy and vaccine refusal are fueled by COVID-19 misinformation and disinformation on social media platforms. This online COVID-19 infodemic has deadly consequences. In this editorial, the authors examine the roles that social media companies play in the COVID-19 infodemic and their obligations to end it. They describe how fake news about the virus developed on social media and acknowledge the initially muted response by the scientific community to counteract misinformation. The authors then challenge social media companies to better mitigate the COVID-19 infodemic, describing legal and ethical imperatives to do so. They close with recommendations for better partnerships with community influencers and implementation scientists, and they provide the next steps for all readers to consider. This guest editorial accompanies the Journal of Medical Internet Research special theme issue, "Social Media, Ethics, and COVID-19 Misinformation."

A survey on sentiment analysis methods, applications, and challenges

Abstract: The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

Digital Media Use and Adolescents' Mental Health During the Covid-19 Pandemic: A Systematic Review and Meta-Analysis

Abstract: The Covid-19 physical distancing measures had a detrimental effect on adolescents' mental health. Adolescents worldwide alleviated the negative experiences of social distancing by spending more time on digital devices. Through a systematic literature search in eight academic databases (including Eric, Proquest Sociology, Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, Pubmed, and Web of Science), the present systematic review and meta-analysis first summarized the existing evidence from 30 studies, published up to September 2021, on the link between mental health and digital media use in adolescents during Covid-19. Digital media use measures included social media, screen time, and digital media addiction. Mental health measures were grouped into conceptually similar dimensions, such as well-being, ill-being, social well-being, lifestyle habits, and Covid-19-related stress. Results showed that, although most studies reported a positive association between ill-being and social media use (r = 0.171, p = 0.011) and ill-being and media addiction (r = 0.434, p = 0.024), not all types of digital media use had adverse consequences on adolescents' mental health. In particular, one-to-one communication, self-disclosure in the context of mutual online friendship, as well as positive and funny online experiences mitigated feelings of loneliness and stress. Hence, these positive aspects of online activities should be promoted. At the same time, awareness of the detrimental effects of addictive digital media use should be raised: That would include making adolescents more aware of adverse mechanisms such as social comparison, fear of missing out, and exposure to negative contents, which were more likely to happen during social isolation and confinement due to the pandemic.

Social Media Application as a New Paradigm for Business Communication: The Role of COVID-19 Knowledge, Social Distancing, and Preventive Attitudes

Abstract: Business firms and the public have encountered massive consequences of the COVID-19 pandemic. This pandemic has become the most significant challenge and influenced all communities. This research study focuses on exploring the relationship between COVID-19 knowledge, social distancing, individuals' attitudes toward social media use, and practices of using social media amid the COVID-19 crisis. This study examines how attitudes toward social media use mediate the linkage between COVID-19 knowledge, social distancing, and practices for social media use. This survey uses a non-probability convenience sampling approach to collect samples and recruit willing respondents with their consent for data collection. This study recorded the feedback from 348 participants who encountered the indirect/direct effects of nationwide lockdowns, restrictions on social gatherings, and COVID-19 infection. The findings validate the proposed hypotheses for their direct effects and indicate significant β-values, t-statistics, and the p-values at p <0.001. The results validate a relationship between the COVID-19 knowledge of and social distancing practices. Similarly, the results approved a positive link between social distancing and attitudes toward social media use amid COVID-19. The findings validate the relation between social distancing and attitudes toward social media use during COVID-19 challenges (β-value = 0.22 and t-statistics = 3.078). The results show the linkage between attitudes toward social media use and practices of using social media (β-value = 0.41, and t-statistics = 7.175). Individuals' attitude toward social media use during COVID-19 mediates the connection between COVID-19 knowledge and COVID-19 practices of using social media use. The results validate the first mediation at β-value = 0.21 and t-statistic = 5.327. Similarly, the findings approve that attitudes toward social media use in the pandemic have positively mediated the relation between distancing and practices for social media use amid the crisis of COVID-19 (β-value = 0.09 and t-statistic = 2.633). The findings indicate how people have been indulged in social media to pave their business communication needs. The results provide valuable insights for the global business community. This study provides a systematic and holistic research model that helps in exploring the consequences of COVID-19. The generalizability of the findings provides valuable directions for future research related to the current pandemic.

Exploring the challenges of remote work on Twitter users' sentiments: From digital technology development to a post-pandemic era

Abstract: • Stress management is configured as a priority for further research on remote work. • Managers should re-evaluate the benefits of remote work for employees. • 6 opportunities and 5 challenges of teleworking are identified and discussed. • New technologies adoption has a positive attitude on employees’ learning. The boost in the use and development of technology, spurred by COVID-19 pandemic and its consequences, has sped up the adoption of new technologies and digital platforms in companies. Specifically, companies have been forced to change their organizational and work structures. In this context, the present study aims to identify the main opportunities and challenges for remote work through the use of digital technologies and platforms based on the analysis of user-generated content (UGC) in Twitter. Using computer-aided text analysis (CATA) and natural language processing (NLP), in this study, we conduct a sentiment analysis developed with Textblob, which works with machine learning. We then apply a mathematical algorithm for topic modeling known as Latent Dirichlet allocation (LDA) model. Based on the results obtained from these data-mining techniques, we identify 11 topics, of which 3 are negative (Virtual Health, Privacy Concerns and Stress), 4 positive (Work-life balance, Less stress, Future and Engagement), and 3 neutral (New Technologies, Sustainability, and Technology Issues). In addition, we also identify and discussed 6 opportunities and 5 challenges in relation to the use and adoption of digital technologies and platforms for teleworking. Finally, theoretical and practical implications of the study are presented for companies that develop strategies based on teleworking and the adoption of new technologies in which stress management is configured as one of the most relevant indicators for further research on remote work. From the applied perspective, executives and policymakers can use the results of the present study to re-evaluate the benefits of remote work for employees.

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

Abstract: The richness of social media data has opened a new avenue for social science research to gain insights into human behaviors and experiences. In particular, emerging data-driven approaches relying on topic models provide entirely new perspectives on interpreting social phenomena. However, the short, text-heavy, and unstructured nature of social media content often leads to methodological challenges in both data collection and analysis. In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and digital media, this research takes Twitter posts as the reference point and assesses the performance of different algorithms concerning their strengths and weaknesses in a social science context. Based on certain details during the analytical procedures and on quality issues, this research sheds light on the efficacy of using BERTopic and NMF to analyze Twitter data.

Synchronized Action Framework for Detection of Coordination on Social Media

Abstract: The study of the coordinated manipulation of conversations on social media has become more prevalent as social media’s role in amplifying misinformation, hate, and polarization has come under greater scrutiny. We discuss how successful generalized coordination detection algorithms could be used to reinforce existing power imbalances, such as those between marginalized groups and government agencies. We propose an alternative method of identifying manipulation—detecting synchronized actions—which reduces this risk. We further consider how responsible coordination detection may be carried out by analyzing synchronized actions. We propose a synchronized action framework for detecting automated coordination by constructing and analyzing multi-view networks. We validate our framework by examining a large Twitter dataset surrounding the Reopen America conversation from 2020. We first discover three simple coordinated campaigns, and then investigate synchronized actions between users discussing the protests that could be consistent with covert coordination. This task is far more complex than examples evaluated in prior work, which demonstrates the need for our multi-view approach. Next, we identify a cluster of suspicious users and detail the activity of three members. These three users amplify protest messages using the same hashtags at very similar times, though they all focus on different states. This analysis highlights the potential usefulness of coordination detection algorithms in investigating amplification, as well as the need to carefully and responsibly deploy such tools.

Fake news detection based on news content and social contexts: a transformer-based approach

Abstract: Fake news is a real problem in today's world, and it has become more extensive and harder to identify. A major challenge in fake news detection is to detect it in the early phase. Another challenge in fake news detection is the unavailability or the shortage of labelled data for training the detection models. We propose a novel fake news detection framework that can address these challenges. Our proposed framework exploits the information from the news articles and the social contexts to detect fake news. The proposed model is based on a Transformer architecture, which has two parts: the encoder part to learn useful representations from the fake news data and the decoder part that predicts the future behaviour based on past observations. We also incorporate many features from the news content and social contexts into our model to help us classify the news better. In addition, we propose an effective labelling technique to address the label shortage problem. Experimental results on real-world data show that our model can detect fake news with higher accuracy within a few minutes after it propagates (early detection) than the baselines.

Customer engagement and social media: Revisiting the past to inform the future

Abstract: Customer engagement (CE) is a marketing concept of great importance and the rise of social media has further amplified the importance of this concept. Yet, our understanding of the progress of CE research remains limited due to the absence of a one-stop state-of-the-art overview of the concept that considers its manifestation on social media. To address this gap, we review CE research on social media since the beginning of the present millennium using the PRISMA protocol for systematic reviews. The outcome of our review reveals the antecedents, decisions, and outcomes; the theories, contexts, and methods; and the ways forward for advancing knowledge, improving representation, and enhancing rigor with respect to future research on CE and social media.

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Abstract: A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, social media). Traditional recommendation models usually assume that only a single type of interaction exists between user and item, and fail to model the multiplex user-item relationships from multi-typed user behavior data, such as page view, add-to-favourite and purchase. While some recent studies propose to capture the dependencies across different types of behaviors, two important challenges have been less explored: i) Dealing with the sparse supervision signal under target behaviors (e.g., purchase). ii) Capturing the personalized multi-behavior patterns with customized dependency modeling. To tackle the above challenges, we devise a new model CML, Contrastive Meta Learning (CML), to maintain dedicated cross-type behavior dependency for different users. In particular, we propose a multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss. In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users. Extensive experiments on three real-world datasets indicate that our method consistently outperforms various state-of-the-art recommendation methods. Our empirical studies further suggest that the contrastive meta learning paradigm offers great potential for capturing the behavior multiplicity in recommendation. We release our model implementation at: https://github.com/weiwei1206/CML.git.

Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Abstract: : Education sector has witnessed several changes in the recent past. These changes have forced private universities into fierce competition with each other to get more students enrolled. This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands. To get competitive gain, universities must observe and examine the students’ feedback on their own social media sites along with the social media sites of their competitors. This study presents a novel framework which integrates numerous analytical approaches including statistical analysis, sentiment analysis, and text mining to accomplish a competitive analysis of social media sites of the universities. These techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received, major areas for improvement. A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore Pakistan. Experimental results show that diversity of shared content, frequency of posts, and schedule of updates, are the key areas for improvement for the local universities. Based on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university (RIU) Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection

Abstract: COVID-19 (SARS-CoV-2) pandemic has infected hundreds of millions and inflicted millions of deaths around the globe. Fortunately, the introduction of COVID-19 vaccines provided a glimmer of hope and a pathway to recovery. However, owing to misinformation being spread on social media and other platforms, there has been a rise in vaccine hesitancy which can lead to a negative impact on vaccine uptake in the population. The goal of this research is to introduce a novel machine learning-based COVID-19 vaccine misinformation detection framework.We collected and annotated COVID-19 vaccine tweets and trained machine learning algorithms to classify vaccine misinformation.More than 15,000 tweets were annotated as misinformation or general vaccine tweets using reliable sources and validated by medical experts. The classification models explored were XGBoost, LSTM, and BERT transformer model.The best classification performance was obtained using BERT, resulting in 0.98 F1-score on the test set. The precision and recall scores were 0.97 and 0.98, respectively.Machine learning-based models are effective in detecting misinformation regarding COVID-19 vaccines on social media platforms.

Cyberbullying via social media and well-being.

Abstract: In this article, we review research examining cyberbullying via social media among youth and adults and the relationship such behavior has with well-being. We report on several possible predictors of cyberbullying via social media, including indiscreet posting, time spent on social media, and personality traits. We also highlight possible negative effects on well-being that may be linked with cyberbullying via social media, including psychological distress, decreased life satisfaction, and suicidal ideation. We conclude the review with ideas for prevention and intervention, including the use of cyberbullying detection software to encourage users to think twice before posting a cyberbullying message. We also highlight several limitations with the existing research and provide some suggestions for future research opportunities.

Windows of developmental sensitivity to social media

Abstract: The relationship between social media use and life satisfaction changes across adolescent development. Our analyses of two UK datasets comprising 84,011 participants (10-80 years old) find that the cross-sectional relationship between self-reported estimates of social media use and life satisfaction ratings is most negative in younger adolescents. Furthermore, sex differences in this relationship are only present during this time. Longitudinal analyses of 17,409 participants (10-21 years old) suggest distinct developmental windows of sensitivity to social media in adolescence, when higher estimated social media use predicts a decrease in life satisfaction ratings one year later (and vice-versa: lower estimated social media use predicts an increase in life satisfaction ratings). These windows occur at different ages for males (14-15 and 19 years old) and females (11-13 and 19 years old). Decreases in life satisfaction ratings also predicted subsequent increases in estimated social media use, however, these were not associated with age or sex.

A Survey on the Fairness of Recommender Systems

Abstract: Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.

TweetBoost: Influence of Social Media on NFT Valuation

Abstract: NFT or Non-Fungible Token is a token that certifies a digital asset to be unique. A wide range of assets including, digital art, music, tweets, memes, are being sold as NFTs. NFT-related content has been widely shared on social media sites such as Twitter. We aim to understand the dominant factors that influence NFT asset valuation. Towards this objective, we create a first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT marketplace) to capture social media profiles and linked NFT assets. Our dataset contains 245,159 tweets posted by 17,155 unique users, directly linking 62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset publicly available.1 We analyze the growth of NFTs, characterize the Twitter users promoting NFT assets, and gauge the impact of Twitter features on the virality of an NFT. Further, we investigate the effectiveness of different social media and NFT platform features by experimenting with multiple machine learning and deep learning models to predict an asset’s value. We model the problem as a binary classification as well as an ordinal classification task. Our results show that social media features improve the ordinal classification accuracy by 6% over baseline models that use only NFT platform features. Among social media features, count of user membership lists, number of likes and replies are important features. On the other hand, OpenSea features like offer entered, bids withdrawn, bid entered and is presale turn out to be important predictors.

Social media and body image: Recent trends and future directions

Abstract: This review presents recent trends in social media and body image research, with a particular focus on different social media platforms, features unique to social media, and potentially positive content for body image. First, it was found that visual platforms (e.g. Instagram) were more dysfunctional for body image than more textual platforms (e.g. Facebook). Second, taking and editing (but not posting) selfies resulted in negative effects on body image. Positive comments intensified the effects of exposure to idealized content. Third, of the forms of potentially positive content examined in recent research (i.e. fitspiration, disclaimer labels, and body positivity), only body positivity content had a positive effect on body image. Recommendations for future research are offered.