Emotions in Microblogs and Information Diffusion: Evidence of a Curvilinear Relationship

How is emotional content shared on microblogging platforms? Prior work has proposed that emotionally charged content is diffused more than emotionally neutral content because it can evoke physiological arousal in platform users. Drawing on recent research in IS, we argue that the real relationship between emotions and Information Diffusion is an inverse U-shaped relationship; moderately strong emotions lead to optimal diffusion. We further theorize that this relationship is moderated by discourse context and valence of emotions. We test these hypotheses by testing a Twitter dataset that includes tweets collected from multiple conversation contexts. Results show broad support for our hypotheses and extend prior work on emotional content in microblogging

Quantifying the Effect of Sentiment on Information Diffusion in Social Media

Social media have become the main vehicle of information production and consumption online. Millions of users every day log on their Facebook or Twitter accounts to get updates and news, read about their topics of interest, and become exposed to new opportunities and interactions. Although recent studies suggest that the contents users produce will affect the emotions of their readers, we still lack a rigorous understanding of the role and effects of contents sentiment on the dynamics of information diffusion. This work aims at quantifying the effect of sentiment on information diffusion, to understand: (i) whether positive conversations spread faster and/or broader than negative ones (or vice-versa); (ii) what kind of emotions are more typical of popular conversations on social media; and, (iii) what type of sentiment is expressed in conversations characterized by different temporal dynamics. Our findings show that, at the level of contents, negative messages spread faster than positive ones, but positive ones reach larger audiences, suggesting that people are more inclined to share and favorite positive contents, the so-called positive bias. As for the entire conversations, we highlight how different temporal dynamics exhibit different sentiment patterns: for example, positive sentiment builds up for highly-anticipated events, while unexpected events are mainly characterized by negative sentiment. Our contribution is a milestone to understand how the emotions expressed in short texts affect their spreading in online social ecosystems, and may help to craft effective policies and strategies for content generation and diffusion.Comment: 10 pages, 5 figure

The Dynamic Effects of Perceptions of Dread Risk and Unknown Risk on SNS Sharing Behavior During Emerging Infectious Disease Events: Do Crisis Stages Matter?

In response to the increasing prevalence of emerging infectious disease (EID) threats, individuals are turning to social media platforms to share relevant information in ever greater numbers. In this study, we examine whether risk perceptions related to user-generated content have dynamic impacts on social networking site (SNS) sharing behavior in different crisis stages. To answer this question, we applied psychometric analysis to evaluate how dread risk and unknown risk can characterize EID threats. Drawing broadly on the literature of risk perceptions, self-perception theory, and crisis stages, we relied on microblogs collected from Sina Weibo, utilizing the vector autoregression model to analyze dynamic relationships. We found that perceptions of dread risk have a dominant and immediate impact on SNS sharing behavior in the buildup, breakout, and termination stages of EID events. Perceptions of unknown risk have a dominant and persistent impact on sharing behavior in the abatement stage. The joint effect of these two types of risk perception reveal an antagonism impact on SNS sharing behavior, and perceptions of dread- and unknown risk have interaction effects from the buildup to termination stages of EID events. To check robustness, we analyzed keywords related to perceptions of dread- and unknown risk. The results of this study support the empirical application of Slovic’s risk perception framework for understanding the characteristics of EID threats and provide a picture of how perceptions of dread- and unknown risk exert differential time-varying effects on SNS sharing behavior during EID events. We also discuss theoretical and practical implications for the crisis management of EID threats. This study is among the first that uses user-generated content in social media to investigate dynamic risk perceptions and their relationship to SNS sharing behavior, which may help provide a basis for timely and efficient risk communication

HOW DO EXPLICITLY EXPRESSED EMOTIONS INFLUENCE INTERPERSONAL COMMUNICATION AND INFORMATION DISSEMINATION? A FIELD STUDY OF EMOJI’S EFFECTS ON COMMENTING AND RETWEETING ON A MICROBLOG PLATFORM

The proliferation of microblogs greatly facilitated interpersonal communication and information diffusion. Prior studies mainly examined effects of user and network characteristics on information diffusion. In this study, we examine how explicitly expressed emotions through emojis influence commenting and retweeting, two types of interactions enabled by microblogging platforms. While existent research largely focused on retweeting, we also take commenting into consideration. A distinction is made between commenting and retweeting, since commenting is more related to interpersonal communication, and retweeting is more related to information diffusion. Hypotheses are tested using data from a leading microblogging platform in China. The results show clear differences between emoji’s effects on commenting and retweeting. Overall speaking, messages with more emojis receive more comments but less retweets. Specifically, positive emojis increase the number of comments, but decrease the numbers of retweets. Similarly, negative emojis increase the number of comments, but decrease the numbers of retweets. Our findings suggest explicitly expressed emotions have different influences on interpersonal communication and information diffusion. Hence, the use of emojis in social media communication shall be catered in order to achieve desired effects

Sentiment Diffusion of Social Inequality in Microblogs: A Case Study of “Migrant Worker” in Sina Weibo

Migrant workers constitute the main city workforce in China. However, they are the victims of social inequality. Sina Weibo is one of the most important channels for people to share information and public opinions. In order to study into the sentiment diffusion of social inequality over Sina Weibo, we collected a huge number of root microblogs and reposts based on the search query “Migrant Worker”. With applying the sentiment tendency analysis tool provided by Baidu AI, we were able to capture the sentiment flipping process. We found that most microblog users tended to follow the previous users’ sentiment polarity. But the intensity of the sentiment polarity would always get weaken

Effectiveness of Corporate Social Media Activities to Increase Relational Outcomes

This study applies social media analytics to investigate the impact of different corporate social media activities on user word of mouth and attitudinal loyalty. We conduct a multilevel analysis of approximately 5 million tweets regarding the main Twitter accounts of 28 large global companies. We empirically identify different social media activities in terms of social media management strategies (using social media management tools or the web-frontend client), account types (broadcasting or receiving information), and communicative approaches (conversational or disseminative). We find positive effects of social media management tools, broadcasting accounts, and conversational communication on public perception

Emotion Dynamics of Public Opinions on Twitter

[EN] Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users' opinions and attempt to understand (i) changing characteristics of users' emotions toward a social issue over time, (ii) influence of public emotions on individuals' emotions, (iii) cause of changing opinion by social factors, and so on. We study users' emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.The work described in this article was carried out in the OSiNT Lab (https://www.iitg.ac.in/cseweb/osint/), Indian Institute of Technology Guwahati, India. The creation of the dataset used in this study was partly supported by the Ministry of Information and Electronic Technology, Government of India.Naskar, D.; Singh, SR.; Kumar, D.; Nandi, S.; Onaindia De La Rivaherrera, E. (2020). Emotion Dynamics of Public Opinions on Twitter. ACM Transactions on Information Systems. 38(2):1-24. https://doi.org/10.1145/3379340124382Ahmed, S., Jaidka, K., & Cho, J. (2016). Tweeting India’s Nirbhaya protest: a study of emotional dynamics in an online social movement. Social Movement Studies, 16(4), 447-465. doi:10.1080/14742837.2016.1192457Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003). Machine Learning, 50(1/2), 5-43. doi:10.1023/a:1020281327116Araujo, T., Neijens, P., & Vliegenthart, R. (2016). Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands. International Journal of Advertising, 36(3), 496-513. doi:10.1080/02650487.2016.1173765Berger, J. (2011). Arousal Increases Social Transmission of Information. Psychological Science, 22(7), 891-893. doi:10.1177/0956797611413294Bi, B., Tian, Y., Sismanis, Y., Balmin, A., & Cho, J. (2014). Scalable topic-specific influence analysis on microblogs. Proceedings of the 7th ACM international conference on Web search and data mining. doi:10.1145/2556195.2556229Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8. doi:10.1016/j.jocs.2010.12.007Chen, W., Wang, C., & Wang, Y. (2010). Scalable influence maximization for prevalent viral marketing in large-scale social networks. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’10. doi:10.1145/1835804.1835934Ding, Z., Jia, Y., Zhou, B., Zhang, J., Han, Y., & Yu, C. (2013). An Influence Strength Measurement via Time-Aware Probabilistic Generative Model for Microblogs. Lecture Notes in Computer Science, 372-383. doi:10.1007/978-3-642-37401-2_38Ding, Z., Wang, H., Guo, L., Qiao, F., Cao, J., & Shen, D. (2015). Finding Influential Users and Popular Contents on Twitter. Web Information Systems Engineering – WISE 2015, 267-275. doi:10.1007/978-3-319-26187-4_23Feldman Barrett, L., & Russell, J. A. (1998). Independence and bipolarity in the structure of current affect. Journal of Personality and Social Psychology, 74(4), 967-984. doi:10.1037/0022-3514.74.4.967Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), e0142390. doi:10.1371/journal.pone.0142390Hillmann, R., & Trier, M. (2012). Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. doi:10.1109/asonam.2012.88Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceedings of the 19th international conference on World wide web - WWW ’10. doi:10.1145/1772690.1772751Myers, S. A., Zhu, C., & Leskovec, J. (2012). Information diffusion and external influence in networks. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’12. doi:10.1145/2339530.2339540Nguyen, H. T., Ghosh, P., Mayo, M. L., & Dinh, T. N. (2017). Social Influence Spectrum at Scale. ACM Transactions on Information Systems, 36(2), 1-26. doi:10.1145/3086700Pal, A., & Counts, S. (2011). Identifying topical authorities in microblogs. Proceedings of the fourth ACM international conference on Web search and data mining - WSDM ’11. doi:10.1145/1935826.1935843Peng, S., Wang, G., & Xie, D. (2017). Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges. IEEE Network, 31(1), 11-17. doi:10.1109/mnet.2016.1500104nmRussell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161-1178. doi:10.1037/h0077714Shi, J., Hu, P., Lai, K. K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites. Internet Research, 28(2), 393-418. doi:10.1108/intr-01-2017-0038Silva, A., Guimarães, S., Meira, W., & Zaki, M. (2013). ProfileRank. Proceedings of the 7th Workshop on Social Network Mining and Analysis - SNAKDD ’13. doi:10.1145/2501025.2501033Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. Journal of Management Information Systems, 29(4), 217-248. doi:10.2753/mis0742-1222290408Vardasbi, A., Faili, H., & Asadpour, M. (2017). SWIM. ACM Transactions on Information Systems, 36(1), 1-33. doi:10.1145/3072652Wang, Y., Li, Y., Fan, J., & Tan, K.-L. (2018). Location-aware Influence Maximization over Dynamic Social Streams. ACM Transactions on Information Systems, 36(4), 1-35. doi:10.1145/3230871Watts, D. J., & Dodds, P. S. (2007). Influentials, Networks, and Public Opinion Formation. Journal of Consumer Research, 34(4), 441-458. doi:10.1086/518527Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010). TwitterRank. Proceedings of the third ACM international conference on Web search and data mining - WSDM ’10. doi:10.1145/1718487.1718520Wolfsfeld, G., Segev, E., & Sheafer, T. (2013). Social Media and the Arab Spring. The International Journal of Press/Politics, 18(2), 115-137. doi:10.1177/1940161212471716Yik, M. S. M., Russell, J. A., & Barrett, L. F. (1999). Structure of self-reported current affect: Integration and beyond. Journal of Personality and Social Psychology, 77(3), 600-619. doi:10.1037/0022-3514.77.3.600Zhang, J., Zhang, R., Sun, J., Zhang, Y., & Zhang, C. (2016). TrueTop: A Sybil-Resilient System for User Influence Measurement on Twitter. IEEE/ACM Transactions on Networking, 24(5), 2834-2846. doi:10.1109/tnet.2015.2494059Zhang, Y., Moe, W. W., & Schweidel, D. A. (2017). Modeling the role of message content and influencers in social media rebroadcasting. International Journal of Research in Marketing, 34(1), 100-119. doi:10.1016/j.ijresmar.2016.07.003Ziegler, C.-N., & Lausen, G. (2005). Propagation Models for Trust and Distrust in Social Networks. Information Systems Frontiers, 7(4-5), 337-358. doi:10.1007/s10796-005-4807-

Analysis and Research on Factors Affecting Information Dissemination of Emergencies in Social Media Environment

We examine whether emotional expression (joy, anger, sadness, fear, disgust) occurring in social media content is associated with a user’s information sharing behavior. Our research is in the context of Hurricane Irma and Tweets associated with that event. We find that negative emotions play an important role in the communication of information, among which, fear has the most significant effect. Meanwhile, the initial stages of the information life cycle have the most prominent influence on the information dissemination

Diffusion of Falsehoods on Social Media

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher for fake news, novel tweets, and tweets with negative sentiment and lower lexical structure. In addition, our results show that the impacts of sentiment are opposite for fake news versus real news. We also find that tweets on the environment have a lower retweet count than the baseline religious news and real social news tweets are shared more often than fake social news. Furthermore, our studies show the counter intuitive nature of current correction endeavors by FEMA and other fact checking organizations in combating falsehoods. Specifically, we show that even though fake news causes an increase in correction messages, they influenced the propagation of falsehoods. Finally our empirical results reveal that correction messages, positive tweets and emotionally charged tweets morph faster. Furthermore, we show that tweets with positive sentiment or are emotionally charged morph faster over time. Word count and past morphing history also positively affect morphing behavior

Measuring Emotional Contagion in Social Media

Social media are used as main discussion channels by millions of individuals every day. The content individuals produce in daily social-media-based micro-communications, and the emotions therein expressed, may impact the emotional states of others. A recent experiment performed on Facebook hypothesized that emotions spread online, even in absence of non-verbal cues typical of in-person interactions, and that individuals are more likely to adopt positive or negative emotions if these are over-expressed in their social network. Experiments of this type, however, raise ethical concerns, as they require massive-scale content manipulation with unknown consequences for the individuals therein involved. Here, we study the dynamics of emotional contagion using Twitter. Rather than manipulating content, we devise a null model that discounts some confounding factors (including the effect of emotional contagion). We measure the emotional valence of content the users are exposed to before posting their own tweets. We determine that on average a negative post follows an over-exposure to 4.34% more negative content than baseline, while positive posts occur after an average over-exposure to 4.50% more positive contents. We highlight the presence of a linear relationship between the average emotional valence of the stimuli users are exposed to, and that of the responses they produce. We also identify two different classes of individuals: highly and scarcely susceptible to emotional contagion. Highly susceptible users are significantly less inclined to adopt negative emotions than the scarcely susceptible ones, but equally likely to adopt positive emotions. In general, the likelihood of adopting positive emotions is much greater than that of negative emotions.Comment: 10 pages, 5 figure