Extreme User and Political Rumor Detection on Twitter

详细信息    查看全文

• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10086
• 期：1
• 页码：751-763
• 全文大小：927 KB
• 参考文献：1.Al-Ali, A., Zualkernan, I., Aloul, F.: A mobile GPRS-sensors array for air pollution monitoring. IEEE Sensors J. 10(10), 1666–1671 (2010)CrossRef
  2.Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International World Wide Web Conference, pp. 675–684 (2011)
  3.Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, p. 2. ACM (2012)
  4.Gupta, A., Kumaraguru, P., Castillo, C., Meier, P.: TweetCred: real-time credibility assessment of content on twitter. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 228–243. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-13734-6_​16
  5.Jaho, E., Tzoannos, E., Papadopoulos, A., Sarris, N.: Alethiometer: a framework for assessing trustworthiness and content validity in social media. In: Proceedings of the 23rd International Conference on World Wide Web Companion, pp. 749–752 (2014)
  6.Kakol, M., Jankowski-Lorek, M., Abramczuk, K., Wierzbicki, A., Catasta, M.: On the subjectivity and bias of web content credibility evaluations. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 1131–1136 (2013)
  7.Lee, K., Caverlee, J., Kamath, K.Y., Cheng, Z.: Detecting collective attention spam. In: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality, pp. 48–55. ACM (2012)
  8.Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the ACM SIGMOD International Conference on Management of data, pp. 1155–1158 (2010)
  9.Metaxas, P.T., Finn, S., Mustafaraj, E.: Using twittertrails.com to investigate rumor propagation. In: Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing, pp. 69–72. ACM (2015)
  10.Miyabe, M., Nadamoto, A., Aramaki, E.: How do rumors spread during a crisis? Analysis of rumor expansion and disaffirmation on twitter after 3.11 in Japan. Int. J. Web Inf. Syst. 10(4), 394–412 (2014)CrossRef
  11.Morris, M.R., Counts, S., Roseway, A., Hoff, A., Schwarz, J.: Tweeting is believing?: understanding microblog credibility perceptions. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 441–450 (2012)
  12.Popescu, A.-M., Pennacchiotti, M.: Detecting controversial events from twitter. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1873–1876 (2010)
  13.Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599. Association for Computational Linguistics (2011)
  14.Unankard, S., Li, X., Sharaf, M.A.: Emerging event detection in social networks with location sensitivity. World Wide Web 18(5), 1393–1417 (2015)CrossRef
  15.Zhang, Y., Szabo, C., Sheng, Q.Z., Fang, X.S.: Classifying perspectives on twitter: immediate observation, affection, and speculation. In: Proceedings of the 16th International Conference on Web Information Systems Engineering, Part I, pp. 493–507 (2015)
  
• 作者单位：Cheng Chang (18)
  Yihong Zhang (18)
  Claudia Szabo (18)
  Quan Z. Sheng (18)
  
  18. School of Computer Science, The University of Adelaide, Adelaide, SA, 5005, Australia
  
• 丛书名：Advanced Data Mining and Applications
• ISBN：978-3-319-49586-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10086

文摘

Twitter, as a popular social networking tool that allows its users to conveniently propagate information, has been widely used by politicians and political campaigners worldwide. In the past years, Twitter has come under scrutiny due to its lack of filtering mechanisms, which lead to the propagation of trolling, bullying, and other unsocial behaviors. Rumors can also be easily created on Twitter, e.g., by extreme political campaigners, and widely spread by readers who cannot judge their truthfulness. Current work on Twitter message assessment, however, focuses on credibility, which is subjective and can be affected by assessor’s bias. In this paper, we focus on the actual message truthfulness, and propose a rule-based method for detecting political rumors on Twitter based on identifying extreme users. We employ clustering methods to identify news tweets. In contrast with other methods that focus on the content of tweets, our unsupervised classification method employs five structural and timeline features for the detection of extreme users. We show with extensive experiments that certain rules in our rule set provide accurate rumor detection with precision and recall both above 80 %, while some other rules provide 100 % precision, although with lower recalls.