Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter
Abstract
:1. Introduction
2. Literature
2.1. Emotions and Public Health Emergencies on Social Media
2.2. Emotional Contagion on Social Media
2.3. Social Bots and Public Sentiments Manipulation
2.4. Social Bots and Online Health Communication
- RQ1
- When discussing different topics concerning the COVID-19 pandemic, what are the differences between social bots and human users in terms of their (a) sentiment polarity and (b) sentiment strength?
- RQ2
- For those topics showing negative sentiments, what are the differences between social bots and human users in terms of their expressions of specific negative emotions, including (a) anger, (b) anxiety, and (c) sadness?
- RQ3
- Will (and if so, how) the negative emotions in tweets be transmitted among different actors?
3. Materials and Methods
3.1. Data
3.2. Social Bot Detection
3.3. Sentiment Analysis
3.4. Structural Topic Model
4. Results
5. Discussion
6. Conclusions
Limitations and Future Directions
Author Contributions
Funding
Conflicts of Interest
References
- Tang, L.; Bie, B.; Zhi, D. Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease. Am. J. Infect. Control 2018, 46, 1375–1380. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lazard, A.J.; Scheinfeld, E.; Bernhardt, J.M.; Wilcox, G.B.; Suran, M. Detecting themes of public concern: A text mining analysis of the Centers for Disease Control and Prevention’s Ebola live Twitter chat. Am. J. Infect. Control 2015, 43, 1109–1111. [Google Scholar] [CrossRef] [PubMed]
- Mollema, L.; Harmsen, I.A.; Broekhuizen, E.; Clijnk, R.; De Melker, H.; Paulussen, T.W.G.M.; Kok, G.; Ruiter, R.A.C.; Das, E. Disease Detection or Public Opinion Reflection? Content Analysis of Tweets, Other Social Media, and Online Newspapers During the Measles Outbreak in The Netherlands in 2013. J. Med. Internet Res. 2015, 17, e128. [Google Scholar] [CrossRef] [PubMed]
- Gao, J.; Zheng, P.; Jia, Y.; Chen, H.; Mao, Y.; Chen, S.; Wang, Y.; Fu, H.; Dai, J. Mental health problems and social media exposure during COVID-19 outbreak. PLoS ONE 2020, 15, e0231924. [Google Scholar] [CrossRef]
- Ahmed, W.; Bath, P.A.; Sbaffi, L.; DeMartini, G. Novel insights into views towards H1N1 during the 2009 Pandemic: A thematic analysis of Twitter data. Health Inf. Libr. J. 2019, 36, 60–72. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Harris, R.B.; Paradice, D.B. An Investigation of the Computer-mediated Communication of Emotions. J. Appl. Sci. Res. 2007, 3, 2081–2090. [Google Scholar]
- Coviello, L.; Sohn, Y.; Kramer, A.D.I.; Marlow, C.; Franceschetti, M.; Christakis, N.A.; Fowler, J.H. Detecting Emotional Contagion in Massive Social Networks. PLoS ONE 2014, 9, e90315. [Google Scholar] [CrossRef] [Green Version]
- Jo, W.; Jaeho, L.; Junli, P.; Yeol, K. Online Information Exchange and Anxiety Spread in the Early Stage of Novel Coronavirus Outbreak in South Korea. J. Med. Internet Res. 2020, 22, e19455. [Google Scholar] [CrossRef]
- Vogels, E.A. From Virtual Parties to Ordering Food, How Americans Are Using the Internet during COVID-19. PewResearch Center. 3 April 2020. Available online: https://www.pewresearch.org/fact-tank/2020/04/30/from-virtual-parties-to-ordering-food-how-americans-are-using-the-internet-during-covid-19/ (accessed on 10 September 2020).
- Mander, J. Coronavirus: How Consumers Are Actually Reacting. Global WebIndex. 12 March 2020. Available online: https://blog.globalwebindex.com/trends/coronavirus-and-consumers/ (accessed on 10 September 2020).
- Medford, R.J.; Saleh, S.N.; Sumarsono, A.; Perl, T.M.; Lehmann, C.U. An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the COVID-19 Outbreak 2020. Open Forum Infect. Dis. 2020, 7, ofaa258. [Google Scholar] [CrossRef]
- Barkur, G.; Kamath, G.B. Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian J. Psychiatry 2020, 51, 102089. [Google Scholar] [CrossRef]
- Pastor, C.K. Sentiment Analysis of Filipinos and Effects of Extreme Community Quarantine Due to Coronavirus (COVID-19) Pandemic. SSRN Electron. J. 2020, 7, 91–95. [Google Scholar] [CrossRef]
- Dubey, A.D.; Tripathi, S. Analysing the Sentiments towards Work-From-Home Experience during COVID-19 Pandemic. J. Innov. Manag. 2020, 8, 13–19. [Google Scholar] [CrossRef]
- Singh, L.; Bansal, S.; Bode, L.; Budak, C.; Chi, G.; Kawintiranon, K.; Padden, C.; Vanarsdall, R.; Vraga, E.; Wang, Y. A First Look at COVID-19 Information and Misinformation Sharing on Twitter. Available online: https://arxiv.org/pdf/2003.13907.pdf (accessed on 10 September 2020).
- Schild, L.; Ling, C.; Blackburn, J.; Stringhini, G.; Zhang, Y.; Zannettou, S. “Go Eat a Bat, Chang!” An Early Look on the Emergence of Sinophobic Behavior on Web Communities in the Face of Covid-19. Available online: https://arxiv.org/pdf/2004.04046.pdf (accessed on 10 September 2020).
- Chen, L.; Lyu, H.; Yang, T.; Wang, Y.; Luo, J. In the Eyes of the Beholder: Sentiment and Topic Analyses on Social Media Use of Neutral and Controversial Terms for Covid-19. Available online: https://arxiv.org/pdf/2004.10225.pdf (accessed on 10 September 2020).
- Ni, M.Y.; Yang, L.; Leung, C.M.C.; Li, N.; Yao, X.; Wang, Y.; Leung, G.M.; Cowling, B.J.; Liao, Q. Mental Health, Risk Factors, and Social Media Use During the COVID-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China: Cross-Sectional Survey. JMIR Ment. Health 2020, 7, e19009. [Google Scholar] [CrossRef] [PubMed]
- Ferrara, E.; Varol, O.; Davis, C.; Menczer, F.; Flammini, A. The rise of social bots. Commun. ACM 2016, 59, 96–104. [Google Scholar] [CrossRef] [Green Version]
- Varol, O.; Ferrara, E.; Davis, C.A.; Menczer, F.; Flammini, A. Online Human-Bot Interactions: Detection, Estimation, and Characterization. Available online: https://arxiv.org/pdf/1703.03107.pdf (accessed on 10 September 2020).
- Stella, M.; Ferrara, E.; De Domenico, M. Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. USA 2018, 115, 12435–12440. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kušen, E.; Strembeck, M. Why so Emotional? An Analysis of Emotional Bot-generated Content on Twitter. In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk, Madeira, Portugal, 20–21 March 2018; pp. 13–22. [Google Scholar]
- Ferrara, E. #Covid-19 on Twitter: Bots, Conspiracies, and Social Media Activism. Available online: https://arxiv.org/vc/arxiv/papers/2004/2004.09531v1.pdf (accessed on 10 September 2020).
- Broniatowski, D.A.; Jamison, A.M.; Qi, S.; Alkulaib, L.; Chen, T.; Benton, A.; Quinn, S.C.; Dredze, M. Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate. Am. J. Public Health 2018, 108, 1378–1384. [Google Scholar] [CrossRef] [PubMed]
- Allem, J.-P.; Escobedo, P.; Dharmapuri, L. Cannabis Surveillance With Twitter Data: Emerging Topics and Social Bots. Am. J. Public Health 2020, 110, 357–362. [Google Scholar] [CrossRef]
- Samuel, J.; Rahman, M.; Ali, G.G.M.N.; Samuel, Y.; Pelaez, A. Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. Available online: https://arxiv.org/pdf/2005.10961.pdf (accessed on 10 September 2020).
- Hassnain, S.; Omar, N. How COVID-19 is Affecting Apprentices. Biomedica 2020, 36, 251–255. [Google Scholar]
- Kleinberg, B.; van der Vegt, I.; Mozes, M. Measuring Emotions in the COVID-19 Real World Worry Dataset. 2020. Available online: https://www.aclweb.org/anthology/2020.nlpcovid19-acl.11.pdf (accessed on 10 September 2020).
- Steinert, S. Corona and value change. The role of social media and emotional contagion. Ethic- Inf. Technol. 2020, 2020. [Google Scholar] [CrossRef]
- Hung, M.; Lauren, E.; Hon, E.S.; Birmingham, W.C.; Xu, J.; Su, S.; Hon, S.D.; Park, J.; Dang, P.; Lipsky, M.S. Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence. J. Med. Internet Res. 2020, 22, e22590. [Google Scholar] [CrossRef]
- Sánchez, P.P.I.; Witt, G.F.V.; Cabrera, F.E.; Maldonado, C.J. The Contagion of Sentiments during the COVID-19 Pandemic Crisis: The Case of Isolation in Spain. Int. J. Environ. Res. Public Health 2020, 17, 5918. [Google Scholar] [CrossRef] [PubMed]
- Salathé, M.; Khandelwal, S. Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control. PLoS Comput. Biol. 2011, 7, e1002199. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chew, C.; Eysenbach, G. Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak. PLoS ONE 2010, 5, e14118. [Google Scholar] [CrossRef] [PubMed]
- Fung, I.C.-H.; Tse, Z.T.H.; Cheung, C.-N.; Miu, A.S.; Fu, K.-W. Ebola and the social media. Lancet 2014, 384, 2207. [Google Scholar] [CrossRef]
- Liu, B.F.; Kim, S. How organizations framed the 2009 H1N1 pandemic via social and traditional media: Implications for U.S. health communicators. Public Relat. Rev. 2011, 37, 233–244. [Google Scholar] [CrossRef]
- Keeling, D.I.; Laing, A.W.; Newholm, T. Health Communities as Permissible Space: Supporting Negotiation to Balance Asymmetries. Psychol. Mark. 2015, 32, 303–318. [Google Scholar] [CrossRef] [Green Version]
- Pitt, C.; Mulvey, M.; Kietzmann, J. Quantitative insights from online qualitative data: An example from the health care sector. Psychol. Mark. 2018, 35, 1010–1017. [Google Scholar] [CrossRef]
- Dubey, A.D. Twitter Sentiment Analysis during COVID19 Outbreak. SSRN Electron. J. 2020. [Google Scholar] [CrossRef]
- Li, S.; Wang, Y.; Xue, J.; Zhao, N.; Zhu, T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health 2020, 17, 2032. [Google Scholar] [CrossRef] [Green Version]
- Ferrara, E.; Yang, Z. Measuring Emotional Contagion in Social Media. PLoS ONE 2015, 10, e0142390. [Google Scholar] [CrossRef] [Green Version]
- Ntika, M.; Sakellariou, I.; Kefalas, P.; Stamatopoulou, I. Experiments with Emotion Contagion in Emergency Evacuation Simulation. In Proceedings of the 4th International Conference on Theory and Practice of Electronic Governance, Beijing, China, 27–30 October 2014. [Google Scholar]
- Hatfield, E.; Cacioppo, J.T.; Rapson, R.L. Emotional Contagion. Curr. Dir. Psychol. Sci. 1993, 2, 96–100. [Google Scholar] [CrossRef]
- Fan, R.; Xu, K.; Zhao, J. An agent-based model for emotion contagion and competition in online social media. Phys. A Stat. Mech. Appl. 2018, 495, 245–259. [Google Scholar] [CrossRef] [Green Version]
- Kramer, A.D.I.; Guillory, J.E.; Hancock, J.T. Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. USA 2014, 111, 8788–8790. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yu, C.-E. Emotional Contagion in Human-Robot Interaction. e-Rev. Tour. Res. 2020, 17, 793–798. [Google Scholar]
- Goldenberg, A.; Gross, J.J. Digital Emotion Contagion. Trends Cogn. Sci. 2020, 24, 316–328. [Google Scholar] [CrossRef]
- Warton, K.A. Coronavirus: How Emotional Contagion Exacts a Toll. Knowledge@Wharton. 10 March 2020. Available online: https://knowledge.wharton.upenn.edu/article/coronavirus-how-emotional-contagion-exacts-a-toll/ (accessed on 12 September 2020).
- Liu, X. A big data approach to examining social bots on Twitter. J. Serv. Mark. 2019, 33, 369–379. [Google Scholar] [CrossRef]
- Kearney, M.; Selvan, P.; Hauer, M.K.; Leader, A.E.; Massey, P.M. Characterizing HPV Vaccine Sentiments and Content on Instagram. Health Educ. Behav. 2019, 46, 37S–48S. [Google Scholar] [CrossRef] [Green Version]
- Bessi, A.; Ferrara, E. Social bots distort the 2016 U.S. Presidential election online discussion. First Monday 2016, 21, 1–14. [Google Scholar] [CrossRef]
- Freitas, C.; Benevenuto, F.; Ghosh, S.; Veloso, A. Reverse Engineering Socialbot Infiltration Strategies in Twitter. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, France, 25–28 August 2015. [Google Scholar]
- Bradshaw, S.; Howard, P.; Kollanyi, B.; Neudert, L.-M. Sourcing and Automation of Political News and Information over Social Media in the United States, 2016–2018. Polit. Commun. 2019, 37, 173–193. [Google Scholar] [CrossRef]
- Ozer, M.; Yildirim, M.Y.; Davulcu, H. Negative Link Prediction and Its Applications in Online Political Networks. In Proceedings of the Proceedings of the 28th ACM Conference on Hypertext and Social Media, Prague, Czech Republic, 4–7 July 2017. [Google Scholar]
- Vosoughi, S.; Roy, D.; Aral, S. The spread of true and false news online. Science 2018, 359, 1146–1151. [Google Scholar] [CrossRef]
- Dickerson, J.P.; Kagan, V.; Subrahmanian, V. Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Beijing, China, 17–20 August 2014. [Google Scholar]
- Stieglitz, S.; Dang-Xuan, L. Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. J. Manag. Inf. Syst. 2013, 29, 217–248. [Google Scholar] [CrossRef]
- Ferrara, E. Measuring Social Spam and the Effect of Bots on Information Diffusion in Social Media. In Complex Spreading Phenomena in Social Systems; Springer: Berlin/Heidelberg, Germany, 2018; pp. 229–255. [Google Scholar]
- Aiello, L.M.; Deplano, M.; Schifanella, R.; Ruffo, G. People are strange when you′re a stranger: Impact and influence of bots on social networks. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland, 4–7 June 2012. [Google Scholar]
- Feil-Seifer, D.; Mataric, M.J. Defining socially assistive robotics. In Proceedings of the 9th International Conference on Rehabilitation Robotics, Chicago, IL, USA, 28 June–1 July 2005. [Google Scholar]
- Deb, A.; Majmundar, A.; Seo, S.; Matsui, A.; Tandon, R.; Yan, S.; Allem, J.-P.; Ferrara, E. Social Bots for Online Public Health Interventions. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain, 28–31 August 2018. [Google Scholar]
- Henkemans, O.A.B.; Bierman, B.P.; Janssen, J.; Neerincx, M.A.; Looije, R.; Van Der Bosch, H.; Van Der Giessen, J.A. Using a robot to personalise health education for children with diabetes type 1: A pilot study. Patient Educ. Couns. 2013, 92, 174–181. [Google Scholar] [CrossRef] [PubMed]
- Miner, A.S.; Laranjo, L.; Kocaballi, A.B. Chatbots in the fight against the COVID-19 pandemic. NPJ Digit. Med. 2020, 3, 1–4. [Google Scholar] [CrossRef] [PubMed]
- Yuan, X.; Schuchard, R.J.; Crooks, A.T. Examining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter. Soc. Media Soc. 2019, 5, 2056305119865465. [Google Scholar] [CrossRef] [Green Version]
- Rabello, E.T.; Matta, G.; Silva, T. Visualising Engagement on Zika Epidemic. In Proceedings of the SMART Data Sprint: Interpreters of Platform Data, Lisboa, Portugal, 29 January–2 February 2018; Available online: https://smart.inovamedialab.org/smart-2018/project-reports/visualising-engagement-on-zika-epidemic (accessed on 10 September 2020).
- Kim, A. Nearly Half of the Twitter Accounts Discussing ′Reopening America′ May Be Bots, Researchers Say. CNN. 22 May 2020. Available online: https://edition.cnn.com/2020/05/22/tech/twitter-bots-trnd/index.html (accessed on 10 September 2020).
- Gallotti, R.; Valle, F.; Castaldo, N.; Sacco, P.; De Domenico, M. Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics. Nat. Hum. Behav. 2020, 1–9. [Google Scholar] [CrossRef]
- Memon, S.A.; Carley, K.M. Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset. Available online: https://arxiv.org/pdf/2008.00791.pdf (accessed on 10 September 2020).
- Howard, P.N.; Kollanyi, B.; Woolley, S. Bots and Automation over Twitter during the US Election. Available online: http://blogs.oii.ox.ac.uk/politicalbots/wp-content/uploads/sites/89/2016/11/Data-Memo-US-Election.pdf (accessed on 10 September 2020).
- Luceri, L.; Deb, A.; Badawy, A.; Ferrara, E. Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior. In Proceedings of the Companion Proceedings of The World Wide Web Conference, Association for Computing Machinery, San Francisco, CA, USA, 13–17 May 2019. [Google Scholar]
- WHO. Archived: WHO Timeline—COVID-19; World Health Organisation: Geneva, Switzerland, 2020. [Google Scholar]
- Cao, Q.; Yang, X.; Yu, J.; Palow, C. Uncovering Large Groups of Active Malicious Accounts in Online Social Networks. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, AZ, USA, 3–7 November 2014. [Google Scholar]
- Wang, G.; Mohanlal, M.; Wilson, C.; Wang, X.; Metzger, M.; Zheng, H.; Zhao, B.Y. Social Turing Tests: Crowdsourcing Sybil Detection. Available online: https://arxiv.org/pdf/1205.3856.pdf (accessed on 10 September 2020).
- Badawy, A.; Lerman, K.; Ferrara, E. Who Falls for Online Political Manipulation? In Proceedings of the Companion Proceedings of The 2019 World Wide Web Conference, Association for Computing Machinery, San Francisco, CA, USA, 13–17 May 2019. [Google Scholar]
- Ferrara, E. Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election. Available online: https://arxiv.org/ftp/arxiv/papers/1707/1707.00086.pdf (accessed on 10 September 2020).
- Luceri, L.; Deb, A.; Giordano, S.; Ferrara, E. Evolution of bot and human behavior during elections. First Monday 2019, 24. [Google Scholar] [CrossRef]
- Shao, C.; Ciampaglia, G.L.; Varol, O.; Yang, K.-C.; Flammini, A.; Menczer, F. The spread of low-credibility content by social bots. Nat. Commun. 2018, 9, 1–9. [Google Scholar] [CrossRef] [Green Version]
- Subrahmanian, V.S.; Azaria, A.; Durst, S.; Kagan, V.; Galstyan, A.; Lerman, K.; Zhu, L.; Ferrara, E.; Flammini, A.; Menczer, F. The DARPA Twitter Bot Challenge. Computer 2016, 49, 38–46. [Google Scholar] [CrossRef] [Green Version]
- Davis, C.A.; Varol, O.; Ferrara, E.; Flammini, A.; Menczer, F. BotOrNot: A System to Evaluate Social Bots. Available online: https://arxiv.org/pdf/1602.00975.pdf (accessed on 10 September 2020).
- Yang, K.; Varol, O.; Davis, C.A.; Ferrara, E.; Flammini, A.; Menczer, F. Arming the public with artificial intelligence to counter social bots. Hum. Behav. Emerg. Technol. 2019, 1, 48–61. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Rao, Y.; Zhan, X.; Chen, H.; Luo, M.; Yin, J. Sentiment and emotion classification over noisy labels. Knowl. Based Syst. 2016, 111, 207–216. [Google Scholar] [CrossRef]
- Munezero, M.; Montero, C.S.; Sutinen, E.; Pajunen, J. Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text. IEEE Trans. Affect. Comput. 2014, 5, 101–111. [Google Scholar] [CrossRef]
- Li, Z.; Fan, Y.; Jiang, B.; Lei, T.; Liu, W. A survey on sentiment analysis and opinion mining for social multimedia. Multimedia Tools Appl. 2018, 78, 6939–6967. [Google Scholar] [CrossRef]
- Amalarethinam, D.G.; Nirmal, V.J. Sentiment and Emotion Analysis for Context Sensitive Information Retrieval of Social Networking Sites: A Survey. Int. J. Comput. Appl. 2014, 100, 47–58. [Google Scholar]
- Pennebaker, J.W.; Booth, R.J.; Francis, M.E. Linguistic Inquiry and Word Count; Erlabaum Publisher: Mahwah, NJ, USA, 2001. [Google Scholar]
- Pennebaker, J.W.; Boyd, R.L.; Jordan, K.; Blackburn, K. The Development and Psychometric Properties of LIWC2015; University of Texas: Austin, TX, USA, 2015. [Google Scholar]
- Cohn, M.A.; Mehl, M.R.; Pennebaker, J.W. Linguistic Markers of Psychological Change Surrounding September 11, 2001. Psychol. Sci. 2004, 15, 687–693. [Google Scholar] [CrossRef]
- Peña, J.; Pan, W. Words of advice: Exposure to website model pictures and online persuasive messages affects the linguistic content and style of Women’s weight-related social support messages. Comput. Hum. Behav. 2016, 63, 208–217. [Google Scholar] [CrossRef]
- Hen, R.; Sakamoto, Y.; Chen, R.S.; Sakamoto, Y. Feelings and Perspective Matter: Sharing of Crisis Information in Social Media. In Proceedings of the 47th Hawaii International Conference on System Sciences, Institute of Electrical and Electronics Engineers, Waikoloa, HI, USA, 6–9 January 2014. [Google Scholar]
- Godbole, N.; Srinivasaiah, M.; Skiena, S. Large-Scale Sentiment Analysis for News and Blogs. In international conference on weblogs and social media. In Proceedings of the International Conference on Weblogs and Social Media, Boulder, CO, USA, 26–28 March 2007. [Google Scholar]
- Blei, D.M. Probabilistic topic models. Commun. ACM 2012, 55, 77–84. [Google Scholar] [CrossRef] [Green Version]
- Wesslen, R. Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond. Available online: https://arxiv.org/pdf/1803.11045.pdf (accessed on 10 September 2020).
- Roberts, M.E.; Tingley, D.; Stewart, B.M.; Airoldi, E.M. The structural topic model and applied social science. In Proceedings of the Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation, Harrah’s Lake Tahoe, Stateline, NV, USA, 10 December 2013. [Google Scholar]
- Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent dirichlet allocation. J. Mach. Learn. Res. 2003, 3, 993–1022. [Google Scholar]
- Roberts, M.E.; Stewart, B.M.; Airoldi, E. A Model of Text for Experimentation in the Social Sciences. J. Am. Stat. Assoc. 2016, 111, 988–1003. [Google Scholar] [CrossRef]
- Li, M.; Luo, Z. The ‘bad women drivers’ myth: The overrepresentation of female drivers and gender bias in China’s media. Inf. Commun. Soc. 2020, 23, 776–793. [Google Scholar] [CrossRef]
- Grimmer, J.; Stewart, B.M. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Polit. Anal. 2013, 21, 267–297. [Google Scholar] [CrossRef]
- Roberts, M.E.; Stewart, B.M.; Tingley, D. stm: An R Package for Structural Topic Models. J. Stat. Softw. 2019, 91, 1–40. [Google Scholar] [CrossRef] [Green Version]
- Edwards, C.; Edwards, A.P.; Spence, P.R.; Shelton, A.K. Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter. Comput. Hum. Behav. 2014, 33, 372–376. [Google Scholar] [CrossRef]
- Cheng, C.; Luo, Y.; Yu, C. Dynamic mechanism of social bots interfering with public opinion in network. Phys. A Stat. Mech. Its Appl. 2020, 551, 124163. [Google Scholar] [CrossRef]
- Grimme, C.; Preuss, M.; Adam, L.; Trautmann, H. Social Bots: Human-Like by Means of Human Control? Big Data 2017, 5, 279–293. [Google Scholar] [CrossRef] [PubMed]
- Kahan, D.M.; Jamieson, K.H.; Landrum, A.; Winneg, K. Culturally antagonistic memes and the Zika virus: An experimental test. J. Risk Res. 2016, 20, 1–40. [Google Scholar] [CrossRef] [Green Version]
- Allem, J.-P.; Ferrara, E. Could Social Bots Pose a Threat to Public Health? Am. J. Public Health 2018, 108, 1005–1006. [Google Scholar] [CrossRef]
- Sutton, J. Health Communication Trolls and Bots Versus Public Health Agencies’ Trusted Voices. Am. J. Public Health 2018, 108, 1281–1282. [Google Scholar] [CrossRef]
- Jamison, A.M.; Broniatowski, D.A.; Quinn, S.C. Malicious Actors on Twitter: A Guide for Public Health Researchers. Am. J. Public Health 2019, 109, 688–692. [Google Scholar] [CrossRef]
- Allem, J.-P.; Ferrara, E.; Uppu, S.P.; Cruz, T.B.; Unger, J.B. E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends. JMIR Public Heath. Surveill. 2017, 3, e98. [Google Scholar] [CrossRef] [Green Version]
Name | Category | Proportion | High Probability Words | Example Tweet |
---|---|---|---|---|
Confirmed cases & deaths | Health-related | 0.16 | coronavirus, case, health, Italy, death, confirmed, state, emergency, number, update, breaking, country, positive, public, total | Colorado reports 16 new cases of #CoronaVirus: Breakdown: Arapahoe County: 3 + 1 Jefferson County: 3 + 2 Pitkin County: +9 Larimer County: 1Gunnison County: 2 + 1 Denver County: 6 + 4 (plus 1 indeterminate) Douglas County: 3 Eagle County: 4 + 1 El Paso County: 1 Summit County: 1 |
Disease prevention | Health-related | 0.15 | coronavirus, virus, corona, hand, don’t, stay, keep, wash, safe, mask, face, hope, protect, avoid, kill | # coronavirus #prevention # coronavirus #prevention #HealthyLiving “Cover your mouth and nose with a tissue when you cough or sneeze, then throw the tissue in the bin and wash your hands. If you do not have a tissue to hand, cough or sneeze into your elbow rather than your hands. |
Economic impacts | Health-unrelated | 0.12 | coronavirus, work, business, sick, crisis, plan, government, online, market, student, budget, economy, class, employee, impact | The Chancellor @RishiSunak sets out a £30 bn #coronavirus plan: Sick pay for self-employed + help on UC £500 m hardship fund No cost of sick pay to SMEs £1 bn of working capital loans. No Rates for small hospitality biz £3000 grant to small businesses #Budget2020 |
COVID-19 in the US | Health-related | 0.11 | coronavirus, trump, cant, real Donald Trump, american, toilet, going, paper, president, house, america, word, medium, ready, thread | When you have nothing else. IDENTITY POLITICS! You & your party are divisive & insane. #CoronaVirus #BLEXIT #WalkAway #MAGA #WWG1WGA #TRUMP #TheGreatAwakening #TRUTH #DNC #Democrat #MSM #IdentityPolitics #FearMonger |
COVID-19 in China | Health-related | 0.09 | china, coronavirus, spread, Wuhan, virus, travel, outbreak, country, quarantine, Chinese, measure, infected, hospital, flight, city | After the spread of a new #Coronavirus, the #UK is taking precautionary measures by monitoring all flights arriving from China. The measures will be applicable on flights from Wuhan to London Heathrow, where aircraft will land in an isolated part of Terminal 4. |
Events canceled & postponed | Health-unrelated | 0.09 | coronavirus, event, canceled, year, game, going, cancel, march, concern, fan, big, canceled, postponed, decision, closed | Big West Basketball No spectators will attend tournament due to #coronavirus concerns. Honda Center Games (no fans) Thu—Men’s quarterfinal games Fri—Men’s and women’s semifinals Sat—Both championship games #AnaheimSports @HondaCenter |
News/Q&A | Health-related | 0.09 | coronavirus, news, read, help, great, question, community, latest, expert, watch, situation, free, advice, outbreak, video | Tonight at 8 p.m. ET on Tonight at 8 p.m. ET on @NBCNewsNOW: @DrJohnTorres hosts special coverage to answer questions about #coronavirus. Stream it live tonight on @Roku, @amazonfiretv, @AppleTV and http://nbcnews.com/NOW |
COVID-19 outbreak | Health-related | 0.06 | coronavirus, Covid, coronavirus outbreak, well, real, feel, worse, Corona virus UK, officially, Coronavid, corona virus USA, deal, coviduk, corona outbreak, epidemic | From the air, hunger, fire and war, save us, Lord “Pray for Italy & the World! Today at 8 p.m. #everyday, the supplications will also be sung, begging for the protection against the #coronavirus epidemic |
Healthcare system | Health-related | 0.05 | pandemic, coronavirus, Corona virus update, testing, test, disease, CDC, healthcare, system, vaccine, patient, classifies, spread, action, outbreak | #Coronavirus Pandemic: Declared as #pandemic by World Health Organization WHO deeply concerned by alarming levels of spread & severity, and alarming levels of inaction In US, slow rollout of testing (flawed kits) and limited testing capacity crippled response to #COVID19 |
Health risks | Health-related | 0.03 | coronavirus, care, risk, panic, better, lot, family, life, serious, bad, person, friend, told, symptom, ill | The virus can remain intact at 4 degrees (39 degrees Fahrenheit) or 10 degrees (50 F) for a longer period of time” Nicholls said, referring to Celsius measurements, according to the transcript. “But at 30 degrees (86 degrees F) then you get inactivation #Coronavirus |
Impacts on public life | Health-unrelated | 0.03 | coronavirus, day, school, week, close, spreading, fast, social, shut, hour, conference, control, ago, open, closing | Close the schools in areas effected by the #coronavirus. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close the schools. Close them now |
Else | Else | 0.02 | people, time, good, flu, thing, today, month, start, call, coming, thought, best, long, seriously, story | #coronavirus freestyle @officialnairam1 @olamide_YBNL @DONJAZZY @davido @Tecknoofficial @iamkissdaniel @lilkeshofficial @mayoku |
Sentiments Polarity | Sentiments Strength | |||||
---|---|---|---|---|---|---|
All Users | Social Bot | Humans | All Users | Social Bot | Humans | |
COVID-19 in the US | −2.15 | −2.22 | −2.15 | 4.88 | 4.95 | 4.88 |
Health risks | −1.84 | −1.79 | −1.84 | 5.44 | 5.36 | 5.44 |
COVID-19 in China | −0.72 | −0.95 | −0.69 | 2.77 | 2.7 | 2.78 |
Economic impacts | −0.07 | −0.28 | −0.04 | 3.4 | 3.31 | 3.41 |
Healthcare system | 0.28 | 0 | 0.3 | 2.61 | 2.66 | 2.61 |
Else | 0.42 | 0.87 | 0.4 | 4.73 | 4.58 | 4.74 |
Confirmed cases and deaths | 0.46 | 0.26 | 0.49 | 1.63 | 1.13 | 1.69 |
Impacts on public life | 0.51 | 1.87 | 0.24 | 2.36 | 2.05 | 2.43 |
Events canceled & postponed | 0.76 | 0.95 | 0.75 | 3.26 | 2.86 | 3.29 |
Disease prevention | 0.97 | 1.47 | 0.9 | 4.9 | 4.78 | 4.92 |
COVID-19 outbreak | 1.21 | 0.65 | 1.31 | 2.93 | 2.44 | 3.02 |
News/Q&A | 3.28 | 2.69 | 3.34 | 3.63 | 3.41 | 3.66 |
Topics | Sadness | Anger | Anxiety | |||
---|---|---|---|---|---|---|
Bot | Human | Bot | Human | Bot | Human | |
COVID-19 in the US | 0.82 | 0.78 | 2.3 | 2.12 | 1.42 | 0.78 |
Health risks | 1.08 | 0.78 | 0.5 | 0.74 | 2.83 | 0.78 |
COVID-19 in China | 0.4 | 0.46 | 1.14 | 1.02 | 0.79 | 0.46 |
Economic impacts | 0.49 | 0.58 | 0.62 | 0.59 | 1.14 | 0.58 |
Topics | Emotions | Bot-To-Human | Human-To-Bot | Bot-To-Bot | Human-To-Human | Total |
---|---|---|---|---|---|---|
COVID-19 in the US | sadness | 0.12 | 0.12 | 0.03 | 0.73 | 1 |
anger | 0.12 | 0.1 | 0.03 | 0.74 | 1 | |
anxiety | 0.07 | 0.12 | 0.02 | 0.79 | 1 | |
Health risks | sadness | 0 | 0.06 | 0 | 0.94 | 1 |
anger | 0 | 0.05 | 0 | 0.95 | 1 | |
anxiety | 0.05 | 0.06 | 0.01 | 0.88 | 1 | |
COVID-19 in China | sadness | 0.06 | 0.08 | 0.02 | 0.84 | 1 |
anger | 0.03 | 0.12 | 0.01 | 0.84 | 1 | |
anxiety | 0.01 | 0.12 | 0 | 0.87 | 1 | |
Economic impacts | sadness | 0.02 | 0.09 | 0.01 | 0.89 | 1 |
anger | 0.06 | 0.1 | 0.01 | 0.83 | 1 | |
anxiety | 0.04 | 0.11 | 0.01 | 0.84 | 1 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Shi, W.; Liu, D.; Yang, J.; Zhang, J.; Wen, S.; Su, J. Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter. Int. J. Environ. Res. Public Health 2020, 17, 8701. https://doi.org/10.3390/ijerph17228701
Shi W, Liu D, Yang J, Zhang J, Wen S, Su J. Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health. 2020; 17(22):8701. https://doi.org/10.3390/ijerph17228701
Chicago/Turabian StyleShi, Wen, Diyi Liu, Jing Yang, Jing Zhang, Sanmei Wen, and Jing Su. 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter" International Journal of Environmental Research and Public Health 17, no. 22: 8701. https://doi.org/10.3390/ijerph17228701
APA StyleShi, W., Liu, D., Yang, J., Zhang, J., Wen, S., & Su, J. (2020). Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health, 17(22), 8701. https://doi.org/10.3390/ijerph17228701