Theory and practice of agenda setting: understanding media, bot, and public agendas in the South Korean presidential election

References

• Ahuja, S., & Dubey, G. (2017, August). Clustering and sentiment analysis on Twitter data. In 2017 2nd International Conference on Telecommunication and Networks (TEL-NET) (pp. 1–5). IEEE.
   Google Scholar
• Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., Santamaría, J., Ouyang, C., Gupta, A., Gu, Y., & Deveci, M. (2023). A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion. Information Fusion.
   Web of Science ®Google Scholar
• Almon, S. (1965). The distributed lag between capital appropriations and expenditures. Econometrica: Journal of the Econometric Society, 178–196. https://doi.org/10.2307/1911894
   Web of Science ®Google Scholar
• Aral, S., & Eckles, D. (2019). Protecting elections from social media manipulation. Science, 365(6456), 858–861. https://doi.org/10.1126/science.aaw8243
   PubMed Web of Science ®Google Scholar
• Armstrong, C. L., & Gao, F. (2010). Now tweet this: How news organizations use Twitter. Electronic News, 4(4), 218–235.
   Google Scholar
• Assenmacher, D., Clever, L., Frischlich, L., Quandt, T., Trautmann, H., & Grimme, C. (2020). Demystifying social bots: On the intelligence of automated social media actors. Social Media + Society, 6(3), 2056305120939264.
   Web of Science ®Google Scholar
• Badawy, A., Lerman, K., & Ferrara, E. (May 2019). 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, 162–168.
   Google Scholar
• Baek, C. (2019). Understanding Windows for global policy: An examination of the Free-Semester Program in Korea. Compare: A Journal of Comparative and International Education, 51(3), 398–415.
   Web of Science ®Google Scholar
• Barber, S., & Hathaway, M. (2022). China’s pet activists: Using moral arguments and epidemic concerns to make space for animal rights. International Review of Environmental History, 8(1), 65–82. https://doi.org/10.22459/IREH.08.01.2022.04
   Google Scholar
• Baumann, H. C., Zheng, P., & McCombs, M. (2018). First and second-level agenda-setting in the 2014 Indian general election: A time-series analysis of party-media relation. Asian Journal of Communication, 28(2), 205–226. https://doi.org/10.1080/01292986.2017.1390773
   Web of Science ®Google Scholar
• Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 US presidential election online discussion. First Monday, 21(11), 11-7.
   Google Scholar
• Borenstein, M. (2022). Comprehensive meta-analysis software. Systematic Reviews in Health Research: Meta-Analysis in Context, 535–548.
   Google Scholar
• Bruns, A., & Burgess, J. (2011). The use of Twitter hashtags in the formation of ad hoc publics. In Proceedings of the 6th European Consortium for Political Research (ECPR) General Conference 2011 (pp. 1–9). The European Consortium for Political Research (ECPR).
   Google Scholar
• Cameron, M. P., Barrett, P., & Stewardson, B. (2016). Can social media predict election results? Evidence from New Zealand. Journal of Political Marketing, 15(4), 416–432. https://doi.org/10.1080/15377857.2014.959690
   Web of Science ®Google Scholar
• Chang, H. C. H., Chen, E., Zhang, M., Muric, G., & Ferrara, E. (2021). Social bots and social media manipulation in 2020: the year in review. arXiv preprint arXiv:2102.08436.
   Google Scholar
• Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H. Y., Olsen, J. M., Pavlin, J. A., Shigematsu, M., Streichert, L. C., Suda, K. J., & Corley, C. D. (2015). Using social media for actionable disease surveillance and outbreak management: A systematic literature review. PLoS ONE, 10(10), 1–12. https://doi.org/10.1371/journal.pone.0139701
   Web of Science ®Google Scholar
• Cho, C. K., Kim, B. Y., & Stoltman, J. P. (2022). Animal identity and space as represented in South Korean geography textbooks. International Research in Geographical and Environmental Education, 31(1), 53–68. https://doi.org/10.1080/10382046.2020.1852787
   Web of Science ®Google Scholar
• Chung, M., Seo, Y. N., Jung, Y., & Lee, D. (2023). Agenda-setting in social TV: How and when user comments influence perceived issue importance. New Media & Society, 25(6), 1394–1411. https://doi.org/10.1177/14614448211020754
   Web of Science ®Google Scholar
• Colomina, C., Margalef, H. S., Youngs, R., & Jones, K. (2021). The impact of disinformation on democratic processes and human rights in the world. Brussels: European Parliament, 1–19.
   Google Scholar
• Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2017, April). The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race. In Proceedings of the 26th international conference on world wide web companion (pp. 963–972).
   Google Scholar
• DalBen, S., & Jurno, A. (2021). More than code: The complex network that involves journalism production in five Brazilian robot initiatives. # ISOJ, 11(1), 111–137.
   Google Scholar
• Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (April 2016). BotOrNot: A system to evaluate social bots. In Proceedings of the 25th International Conference Companion on World Wide Web, Montreal, QC, Canada, 11–15, 273–274. https://doi.org/10.1145/2872518.2889302
   Google Scholar
• Dearing, J. W., Rogers, E. M., & Fei, X. (1988). The agenda-setting process for the issue of AIDS. Mass Communication Division, International Communication Association, New Orleans, 29.
   Google Scholar
• Duan, Z., Li, J., Lukito, J., Yang, K. C., Chen, F., Shah, D. V., & Yang, S. (2022). Algorithmic agents in the hybrid media system: Social bots, selective amplification, and partisan news about COVID-19. Human Communication Research, 48(3), 516–542. https://doi.org/10.1093/hcr/hqac012
   Web of Science ®Google Scholar
• Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57. https://doi.org/10.1080/01969727308546046
   Google Scholar
• Eggleston, J., & Wilson, S. L. (2023). Internet policy in South Korea: Liberal imperialism and paradox. In Handbook on democracy and security. Edward Elgar Publishing Limited.
   Google Scholar
• Elmas, T. (2022). The role of compromised accounts in social media manipulation (No. THESIS). EPFL.
   Google Scholar
• Elmas, T., Overdorf, R., Ozkalay, A. F., & Aberer, K. (2019). Lateral astroturfing attacks on Twitter trending topics. In Conferencia] AMLD EPFL, Suiza, Ecublens, disponible en: https://cutt.ly/4yGaj5L (última consulta en julio de 2022)
   Google Scholar
• Feinberg, M., Kovacheff, C., Teper, R., & Inbar, Y. (2019). Understanding the process of moralization: How eating meat becomes a moral issue. Journal of Personality and Social Psychology, 117(1), 50. https://doi.org/10.1037/pspa0000149
   PubMed Web of Science ®Google Scholar
• Ferrara, E. (2018). Measuring social spam and the effect of bots on information diffusion in social media. In Complex spreading phenomena in social systems: Influence and contagion in real-world social networks (pp. 229–255). Springer. https://doi.org/10.1007/978-3-319-77332-2_13
   Google Scholar
• Ferrara, E. (2020). Bots, elections, and social media: A brief overview. In Disinformation, misinformation, and fake news in social media (pp. 95–114).
   Google Scholar
• Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96–104. https://doi.org/10.1145/2818717
   Web of Science ®Google Scholar
• Funk, M. J., & McCombs, M. (2017). Strangers on a theoretical train: Inter-media agenda setting, community structure, and local news coverage. Journalism Studies, 18(7), 845–865. https://doi.org/10.1080/1461670X.2015.1099460
   Web of Science ®Google Scholar
• Gilardi, F., Gessler, T., Kubli, M., & Müller, S. (2022). Social media and political agenda setting. Political Communication, 39(1), 39–60. https://doi.org/10.1080/10584609.2021.1910390
   Web of Science ®Google Scholar
• Gildersleve, P., Lambiotte, R., & Yasseri, T. (2022). Between news and history: Identifying networked topics of collective attention on Wikipedia. arXiv preprint arXiv:2211.07616.
   Google Scholar
• Guo, L. (2019). Media agenda diversity and intermedia agenda setting in a controlled media environment: A computational analysis of China’s online news. Journalism Studies, 20(16), 2460–2477. https://doi.org/10.1080/1461670X.2019.1601029
   Web of Science ®Google Scholar
• Guo, L., & McCombs, M. (2011, May). Network agenda setting: A third level of media effects. In Annual Conference of the International Communication Association, Boston, MA.
   Google Scholar
• Gurumyen, B. D., Akanle, O., Yikwabs, Y. P., & Nomishan, T. S. (2020). Zootherapy: The use of dog meat for traditional African medicine in Kanke local government area, Plateau State, Nigeria. Journal of Tourism and Heritage Studies, 9(2), 1–14.
   Google Scholar
• Hagen, L., Neely, S., Keller, T. E., Scharf, R., & Vasquez, F. E. (2022). Rise of the machines? Examining the influence of social bots on a political discussion network. Social Science Computer Review, 40(2), 264–287.
   Web of Science ®Google Scholar
• Hajli, N., Saeed, U., Tajvidi, M., & Shirazi, F. (2022). Social bots and the spread of disinformation in social media: The challenges of artificial intelligence. British Journal of Management, 33(3), 1238–1253. https://doi.org/10.1111/1467-8551.12554
   Web of Science ®Google Scholar
• Han, R., Xu, J., & Pan, D. (2022). How media exposure, media trust, and media bias perception influence public evaluation of COVID-19 pandemic in international metropolises. International Journal of Environmental Research and Public Health, 19(7), 3942. https://doi.org/10.3390/ijerph19073942
   PubMed Web of Science ®Google Scholar
• Heidari, M., Jones Jr, J. H., & Uzuner, O. (2022). Online user profiling to detect social bots on Twitter. arXiv preprint arXiv:2203.05966.
   Google Scholar
• Hepp, A. (2020). Artificial companions, social bots and work bots: Communicative robots as research objects of media and communication studies. Media, Culture & Society, 42(7-8), 1410–1426. https://doi.org/10.1177/0163443720916412
   Web of Science ®Google Scholar
• Higuchi, K. (2016). KH coder 3 reference manual. Kioto (Japan). Ritsumeikan University.
   Google Scholar
• Hindman, D. B. (2009). Mass media flow and differential distribution of politically disputed beliefs: The belief gap hypothesis. Journalism & Mass Communication Quarterly, 86(4), 790–808. https://doi.org/10.1177/107769900908600405
   Web of Science ®Google Scholar
• Hinton, P., McMurray, I., & Brownlow, C. (2014). SPSS explained. Routledge.
   Google Scholar
• Jiang, M., Kim, E., & Woo, Y. (2020). The relationship between economic growth and air pollution—a regional comparison between China and South Korea. International Journal of Environmental Research and Public Health, 17(8), 2761. https://doi.org/10.3390/ijerph17082761
   PubMed Web of Science ®Google Scholar
• Jiang, Q., Cheng, Y., & Cho, S. K. (2021). Media coverage and public perceptions of the THAAD event in China, the United States, and South Korea: A cross-national network agenda-setting study. Chinese Journal of Communication, 14(4), 386–408. https://doi.org/10.1080/17544750.2021.1902360
   Web of Science ®Google Scholar
• Jungherr, A., Posegga, O., & An, J. (2019). Discursive power in contemporary media systems: A comparative framework. The International Journal of Press/Politics, 24(4), 404–425. https://doi.org/10.1177/1940161219841543
   Web of Science ®Google Scholar
• Keller, T. R., & Klinger, U. (2019). Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication, 36(1), 171–189. https://doi.org/10.1080/10584609.2018.1526238
   Web of Science ®Google Scholar
• Kim, A. (22 May 2020). Nearly half of the Twitter accounts discussing ‘Reopening America’ may be bots, researchers Say. CNN. Available online:https://edition.cnn.com/2020/05/22/tech/twitter-bots-trnd/index.html
   Google Scholar
• Kim, S. H., Jun, J., Thrasher, J. F., Heo, Y. J., & Cho, Y. J. (2021). News media presentations of heated tobacco products (HTPs): a content analysis of newspaper and television news coverage in South Korea. Journal of Health Communication, 26(5), 299–311. https://doi.org/10.1080/10810730.2021.1931988
   PubMed Web of Science ®Google Scholar
• Langer, A. I., & Gruber, J. B. (2021). Political agenda setting in the hybrid media system: Why legacy media still matter a great deal. The International Journal of Press/Politics, 26(2), 313–340. https://doi.org/10.1177/1940161220925023
   Web of Science ®Google Scholar
• Laužikas, M., & Miliūtė, A. (2020). Human resource management effects on sustainability of high-tech companies: What Lithuania and South Korea can learn from each other. Insights Into Regional Development, 2(2), 562–579. https://doi.org/10.9770/IRD.2020.2.2(5)
   Google Scholar
• Lee, J. H., & Woo, J. (2020). Green new deal policy of South Korea: Policy innovation for a sustainability transition. Sustainability, 12(23), 10191. https://doi.org/10.3390/su122310191
   Web of Science ®Google Scholar
• Lehrer, S., Xie, T., & Zeng, T. (2021). Does high-frequency social media data improve forecasts of low-frequency consumer confidence measures? Journal of Financial Econometrics, 19(5), 910–933. https://doi.org/10.1093/jjfinec/nbz037
   Web of Science ®Google Scholar
• Loy-Benitez, J., Vilela, P., Li, Q., & Yoo, C. (2019). Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks. Ecotoxicology and Environmental Safety, 169, 316–324. https://doi.org/10.1016/j.ecoenv.2018.11.024
   PubMed Web of Science ®Google Scholar
• Lu, S., Chen, W., Li, X., & Zheng, P. (2018). The Chinese smog crisis as media event: Examining Twitter discussion of the documentary under the dome. Policy & Internet, 10(4), 483–508. https://doi.org/10.1002/poi3.191
   Web of Science ®Google Scholar
• Luceri, L., Deb, A., Giordano, S., & Ferrara, E. (2019). Evolution of bot and human behavior during elections. First Monday, 24(1), 1–12. https://doi.org/10.5210/fm.v24i9.10213
   Google Scholar
• Maags, C. (2020). Long-term care insurance adoption in East Asia: Politics, ideas, and institutions. Politics & Policy, 48(1), 69–106. https://doi.org/10.1111/polp.12339
   Web of Science ®Google Scholar
• Mainali, K., Bewick, S., Vecchio-Pagan, B., Karig, D., & Fagan, W. F. (2019). Detecting interaction networks in the human microbiome with conditional Granger causality. PLoS Computational Biology, 15(5), e1007037. https://doi.org/10.1371/journal.pcbi.1007037
   PubMed Web of Science ®Google Scholar
• Majerczak, P., & Strzelecki, A. (2022). Trust, media credibility, social ties, and the intention to share towards information verification in an age of fake news. Behavioral Sciences, 12(2), 51. https://doi.org/10.3390/bs12020051
   Web of Science ®Google Scholar
• Martini, F., Samula, P., Keller, T. R., & Klinger, U. (2021). Bot, or not? Comparing three methods for detecting social bots in five political discourses. Big Data & Society, 8(2), 20539517211033566. https://doi.org/10.1177/20539517211033566
   Web of Science ®Google Scholar
• McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176–187. https://doi.org/10.1086/267990
   Web of Science ®Google Scholar
• Meleo-Erwin, Z., Basch, C., MacLean, S. A., Scheibner, C., & Cadorett, V. (2017). To each his own: Discussions of vaccine decision-making in top parenting blogs. Human Vaccines & Immunotherapeutics, 13(8), 1895–1901. https://doi.org/10.1080/21645515.2017.1321182
   PubMed Web of Science ®Google Scholar
• Meraz, S. (2009). Is there an elite hold? Traditional media to social media agenda setting influence in blog networks. Journal of Computer-Mediated Communication, 14(3), 682–707. https://doi.org/10.1111/j.1083-6101.2009.01458.x
   Web of Science ®Google Scholar
• Metaxas, P. T., & Mustafaraj, E. (2012). Social media and the elections. Science, 338(6106), 472–473. https://doi.org/10.1126/science.1230456
   PubMed Web of Science ®Google Scholar
• Neuman, R. W., Guggenheim, L., Mo Jang, S. A., & Bae, S. Y. (2014). The dynamics of public attention: Agenda-setting theory meets big data. Journal of Communication, 64(2), 193–214. https://doi.org/10.1111/jcom.12088
   Web of Science ®Google Scholar
• Onder, M., & Nyadera, I. N. (2020). The role of non-economic drivers in development planning: The case of South Korea and Turkey. International Journal of Public Administration, 43(4), 283–293. https://doi.org/10.1080/01900692.2019.1628057
   Web of Science ®Google Scholar
• Park, C., McQuaid, R., Lee, J., Kim, S., & Lee, I. (2019). The impact of job retention on continuous growth of engineering and informational technology SMEs in South Korea. Sustainability, 11(18), 5005. https://doi.org/10.3390/su11185005
   Web of Science ®Google Scholar
• Park, C. S. (2021). Impacts of cross-ownership between newspapers and television on viewpoint diversity: Testing one-owner-one-voice thesis. Journalism Studies, 22(13), 1775–1792. https://doi.org/10.1080/1461670X.2021.1965908
   Web of Science ®Google Scholar
• Park, K., & Weible, C. M. (2020). Developing policy theories in South Korea: Lessons from the advocacy coalition framework. In An emerging Asian model of governance and transnational knowledge transfer (pp. 16–30). Routledge.
   Google Scholar
• Pastor-Galindo, J., Zago, M., Nespoli, P., Bernal, S. L., Celdrán, A. H., Pérez, M. G., & Mármol, F. G. (2020). Spotting political social bots in Twitter: A use case of the 2019 Spanish general election. IEEE Transactions on Network and Service Management, 17(4), 2156–2170. https://doi.org/10.1109/TNSM.2020.3031573
   Web of Science ®Google Scholar
• Piao, Y., Lee, D., Park, S., Kim, H. G., & Jin, Y. (2022). Forest fire susceptibility assessment using Google Earth engine in Gangwon-do, Republic of Korea. Geomatics, Natural Hazards and Risk, 13(1), 432–450. https://doi.org/10.1080/19475705.2022.2030808
   Web of Science ®Google Scholar
• Ping, H., & Qin, S. (2018). A social bots detection model based on deep learning algorithm. In 2018 IEEE 18th International Conference on Communication Technology (ICCT) , Chongqing, China (pp. 1435–1439). https://doi.org/10.1109/ICCT.2018.8600029
   Google Scholar
• Published by Nina Jobst, Apr 11, 2022 Social media usage in South Korea – statistics & facts..https://www.statista.com/topics/5274/social-media-usage-in-south-korea/
   Google Scholar
• Qayyum, H., Zhao, B. Z. H., Wood, I. D., Ikram, M., Kaafar, M. A., & Kourtellis, N. (2023). A longitudinal study of the top 1% toxic Twitter profiles. arXiv preprint arXiv:2303.14603.
   Google Scholar
• Robertson, C. T., & Mourão, R. R. (2020). Faking alternative journalism? An analysis of self-presentations of “fake news” sites. Digital Journalism, 8(8), 1011–1029. https://doi.org/10.1080/21670811.2020.1743193
   Web of Science ®Google Scholar
• Russell, A. B., Wexler, A. G., Harding, B. N., Whitney, J. C., Bohn, A. J., Goo, Y. A., Tran, B. Q., Barry, N. A., Zheng, H., Peterson, S. B., Chou, S., Gonen, T., Goodlett, D. R., Goodman, A. L., & Mougous, J. D. (2014). A type VI secretion-related pathway in Bacteroidetes mediates interbacterial antagonism. Cell Host and Microbe, 16(2), 227–236.
   PubMed Web of Science ®Google Scholar
• Salman, A., Mustaffa, N., Mohd Salleh, M. A., & Ali, M. N. S. (2016). Social media and agenda setting: Implications on political agenda. JurnalKomunikasi: Malaysian Journal of Communication, 32(1), 401–414. https://doi.org/10.17576/JKMJC-2016-3201-19
   Google Scholar
• Santini, R. M., Salles, D., & Tucci, G. (2021). When machine behavior targets future voters: The use of social bots to test narratives for political campaigns in Brazil. International Journal of Communication, 15(0), 1220–1223.
   Google Scholar
• Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021). From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets. International Journal of Information Management, 60, 102331. https://doi.org/10.1016/j.ijinfomgt.2021.102331
   Web of Science ®Google Scholar
• Sayyadiharikandeh, M., Varol, O., Yang, K. C., Flammini, A., & Menczer, F. (2020, October). Detection of novel social bots by ensembles of specialized classifiers. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 2725–2732).
   Google Scholar
• Schäfer, F., Evert, S., & Heinrich, P. (2017). Japan’s 2014 general election: Political bots, right-wing internet activism, and prime minister Shinzō Abe’s hidden nationalist agenda. Big Data, 5(4), 294–309. https://doi.org/10.1089/big.2017.0049
   PubMed Web of Science ®Google Scholar
• Schmidt, P., & Waud, R. N. (1973). The almon lag technique and the monetary versus fiscal policy debate. Journal of the American Statistical Association, 68(341), 11–19.
   Web of Science ®Google Scholar
• Sciarini, P., & Tresch, A. (2019). The political agenda-setting power of the media: the Europeanization nexus. Journal of European Public Policy, 26(5), 734–751.
   Web of Science ®Google Scholar
• Shao, C., Ciampaglia, G.L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018). The spread of low-credibility content by social bots. Nature Communications. 9(1), 4787. https://doi.org/10.1038/s41467-018-06930-7
   PubMed Web of Science ®Google Scholar
• Shi, 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
   PubMed Web of Science ®Google Scholar
• Skewes, E. (2018). Time delays are not enough; media must call out lies. Journal of Media Ethics, 33(2), 97–99. https://doi.org/10.1080/23736992.2018.1435498
   Web of Science ®Google Scholar
• Slothuus, R., & Bisgaard, M. (2021). How political parties shape public opinion in the real world. American Journal of Political Science, 65(4), 896–911. https://doi.org/10.1111/ajps.12550
   Web of Science ®Google Scholar
• Smith, K. A. (2016). Newspaper coverage and public concern about community issues. In Agenda setting (pp. 75–87). Routledge.
   Google Scholar
• Su, Y., & Borah, P. (2019). Who is the agenda setter? Examining the intermedia agenda-setting effect between Twitter and newspapers. Journal of Information Technology & Politics, 16(3), 236–249. https://doi.org/10.1080/19331681.2019.1641451
   Web of Science ®Google Scholar
• Tracey, B., & Francesca, G. (Eds.) (2020). Educational research and innovation education in the digital Age healthy and happy children: Healthy and happy children. OECD Publishing.
   Google Scholar
• Vargo, C. J. (2018). Fifty years of agenda-setting research: New directions and challenges for the theory. The Agenda Setting Journal, 2(2), 105–123. https://doi.org/10.1075/asj.18023.var
   Google Scholar
• Vargo, C. J., Guo, L., & Amazeen, M. A. (2018). The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016. New Media & Society, 20(5), 2028–2049. https://doi.org/10.1177/1461444817712086
   Web of Science ®Google Scholar
• Varol, O., Ferrara, E., Ogan, C. L., Menczer, F., & Flammini, A. (2014, June). Evolution of online user behavior during a social upheaval. In Proceedings of the 2014 ACM conference on Web science (pp. 81–90).
   Google Scholar
• Vasilkova, V. V., & Legostaeva, N. I. (2019). Social bots in political communication. RUDN Journal of Sociology, 19(1), 121–133. https://doi.org/10.22363/2313-2272-2019-19-1-121-133
   Google Scholar
• Wells, C., Shah, D., Lukito, J., Pelled, A., Pevehouse, J. C., & Yang, J. (2020). Trump, Twitter, and news media responsiveness: A media systems approach. New Media & Society, 22(4), 659–682. https://doi.org/10.1177/1461444819893987
   Web of Science ®Google Scholar
• Woolley, S. C., & Howard, P. N. (2016). Political communication, computational propaganda, and autonomous agents: Introduction. International Journal of Communication, 10(1), 1–18. Retrieved from https://par.nsf.gov/biblio/10021331
   Google Scholar
• Woolley, S. C., & Howard, P. N. (2018). Computational propaganda: Political parties, politicians, and political manipulation on social media. Oxford University Press.
   Google Scholar
• Yang, F., & Sun, T. (2021). Who has set whose agenda on social media? A dynamic social network analysis of tweets on Paris attack. Communication Quarterly, 69(4), 341–363.
   Web of Science ®Google Scholar
• Yang, K. C., Ferrara, E., & Menczer, F. (2022). Botometer 101: Social bot practicum for computational social scientists. Journal of Computational Social Science, 5(2), 1511–1528. https://doi.org/10.1007/s42001-022-00177-5
   PubMedGoogle Scholar
• Yang, K. C., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2019). Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies, 1(1), 48–61. https://doi.org/10.1002/hbe2.115
   Google Scholar
• Yang, K. C., Varol, O., Hui, P. M., & Menczer, F. (2020, April). Scalable and generalizable social bot detection through data selection. In Proceedings of the AAAI conference on artificial intelligence, 34(01), 1096–1103. https://doi.org/10.1609/aaai.v34i01.5460
   Google Scholar
• Yang, X., Chen, B. C., Maity, M., & Ferrara, E. (2016). Social politics: Agenda setting and political communication on social media. In Social Informatics: 8th International Conference, SocInfo 2016, Bellevue, WA, USA, November 11–14, 2016, Proceedings, Part I 8 (pp. 330–344). Springer International Publishing.
   Google Scholar
• Zhang, M., Chen, Z., Qi, X., & Liu, J. (2022a). Could social bots’ sentiment engagement shape humans’ sentiment on COVID-19 vaccine discussion on Twitter? Sustainability, 14(9), 5566–5585. https://doi.org/10.3390/su14095566
   Web of Science ®Google Scholar
• Zhang, M., Qi, X., Chen, Z., & Liu, J. (2022b). Social bots’ involvement in the COVID-19 vaccine discussions on Twitter. International Journal of Environmental Research and Public Health, 19(3), 1651–1671. https://doi.org/10.3390/ijerph19031651
   PubMed Web of Science ®Google Scholar
• Zhang, Y., Song, W., Shao, J., Abbas, M., Zhang, J., Koura, Y. H., & Su, Y. (2023). Social bots’ role in the COVID-19 pandemic discussion on Twitter.
   Google Scholar