Abstract
Recently, online social networks have been popular for election campaigns to monitor public opinion, spread information, and even try to influence the discussion among the platforms. In this study, we focus on the collusion of potential political teams or individuals who attempt to produce discussions and a series of positive/negative comments to support/attack specific candidates. We collect the user behavior in the most extensive online forum in Taiwan before a national election and use statistical analysis to identify such users. We also verify the results by manually reading the published content of the users. From the results, we hope this study can benefit users to identify underneath information manipulation and retain the trustiness of online society.
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- 1.
Wikipedia maintains a series of poll results of the 2018 Taipei mayor election, as shown in this Wiki entry .
- 2.
Here we apply a strict threshold (i.e., 20 min) to avoid false positive identification on cyber armies. Meanwhile, there may be some cyber armies not recognized in the above results.
References
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, pp. 49–62. ACM (2009)
Boutet, A., Kim, H., Yoneki, E.: Whats in twitter, i know what parties are popular and who you are supporting now! Soc. Netw. Anal. Min. 3(4), 1379–1391 (2013)
Guo, D., Lin, F., Chen, C.: User behaviors in an online social network. In: IEEE International Conference on Network Infrastructure and Digital Content, 2009, IC-NIDC 2009, pp. 430–434. IEEE (2009)
Hoang, T.A., Cohen, W.W., Lim, E.P., Pierce, D., Redlawsk, D.P.: Politics, sharing and emotion in icroblogs. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 282–289. ACM (2013)
Jamali, S., Rangwala, H.: Digging digg: comment mining, popularity prediction, and social network analysis. In: International Conference on Web Information Systems and Mining, 2009. WISM 2009, pp. 32–38. IEEE (2009)
Lei, K., et al.: Understanding user behavior in sina weibo online social network: a community approach. IEEE Access 6, 13302–13316 (2018)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 122–129. AAAI Press, Menlo Park, Washington, D.C. (2010)
Priambodo, R., Satria, R.: User behavior pattern of mobile online social network service. In: International Conference on Cloud Computing and Social Networking (ICCCSN), 2012, pp. 1–4. IEEE (2012)
Tsugawa, S., Kimura, K.: Identifying influencers from sampled social networks. Phys. A: Stat. Mech. Its Appl. 507, 294–303 (2018)
Tumasjan, A., Sprenger, T., Sandner, P., Welpe, I.: Predicting elections with Twitter: What 140 characters reveal about political sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185. AAAI Press, Menlo Park, Washington, D.C. (2010)
Wang, M.H., Lei, C.L.: Boosting election prediction accuracy by crowd wisdom on social forums. In: 2016 13th IEEE Annual Consumer Communications and Networking Conference (CCNC), pp. 348–353. IEEE (2016)
Wang, M.H., Lei, C.L.: SocialDNA: a novel approach for distinguishing notable articles and authors through social events. J. Inf. Sci. Eng. 34(6), 1579–1598 (2018)
Wong, F.M.F., Tan, C.W., Sen, S., Chiang, M.: Quantifying political leaning from tweets, retweets, and retweeters. IEEE Trans. Knowl. Data Eng. 28(8), 2158–2172 (2016)
Acknowledgements
This work was supported by Ministry of Science and Technology, Taiwan, under the Grant MOST 107-2218-E-035-009-MY3. We would like to thank reviewers for their valuable comments and suggestions to improve the manuscript.
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Wang, MH., Nguyen, NL., Dow, CR. (2019). Detecting Potential Cyber Armies of Election Campaigns Based on Behavioral Analysis. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_35
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DOI: https://doi.org/10.1007/978-3-030-05414-4_35
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