A Model for Identifying Misinformation in Online Social Networks

  • Sotirios Antoniadis
  • Iouliana LitouEmail author
  • Vana Kalogeraki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9415)


Online Social Networks (OSNs) have become increasingly popular means of information sharing among users. The spread of news regarding emergency events is common in OSNs and so is the spread of misinformation related to the event. We define as misinformation any false or inaccurate information that is spread either intentionally or unintentionally. In this paper we study the problem of misinformation identification in OSNs, and we focus in particular on the Twitter social network. Based on user and tweets characteristics, we build a misinformation detection model that identifies suspicious behavioral patterns and exploits supervised learning techniques to detect misinformation. Our extensive experimental results on 80294 unique tweets and 59660 users illustrate that our approach effectively identifies misinformation during emergencies. Furthermore, our model manages to timely identify misinformation, a feature that can be used to limit the spread of the misinformation.


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  1. 1.
    Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: Internet Measurement Conference, pp. 243–258 (2011)Google Scholar
  2. 2.
    Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: ACM Conference on Computer and Communications Security, pp. 681–683 (2010)Google Scholar
  3. 3.
    Zubiaga, A., Ji, H.: Tweet, but verify: Epistemic study of information verification on twitter (2013). CoRR, vol. abs/1312.5297Google Scholar
  4. 4.
    Gupta, A., Lamba, H., Kumaraguru, P., Joshi, A.: Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In: Ser. WWW 2013 Companion (2013)Google Scholar
  5. 5.
    Castillo, C., Mendoza, M., Poblete, B.: Predicting information credibility in time-sensitive social media. Internet Research 23(5), 560–588 (2013)CrossRefGoogle Scholar
  6. 6.
    Xia, X., Yang, X., Wu, C., Li, S., Bao, L.: Information credibility on twitter in emergency situation. In: Chau, M., Wang, G.A., Yue, W.T., Chen, H. (eds.) PAISI 2012. LNCS, vol. 7299, pp. 45–59. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  7. 7.
    Bagrow, J.P., Wang, D., Barabasi, A.-L.: Collective response of human populations to large-scale emergencies (2011). CoRR, vol. abs/1106.0560Google Scholar
  8. 8.
    Guy, M., Earle, P., Ostrum, C., Gruchalla, K., Horvath, S.: Integration and dissemination of citizen reported and seismically derived earthquake information via social network technologies. In: Cohen, P.R., Adams, N.M., Berthold, M.R. (eds.) IDA 2010. LNCS, vol. 6065, pp. 42–53. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment in short strength detection informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  11. 11.
    Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: ACSAC, pp. 1–9 (2010)Google Scholar
  12. 12.
    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) Google Scholar
  13. 13.
    Bosma, M., Meij, E., Weerkamp, W.: A framework for unsupervised spam detection in social networking sites. In: Baeza-Yates, R., de Vries, A.P., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 364–375. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  14. 14.
    Anagnostopoulos, A., Bessi, A., Caldarelli, G., Vicario, M.D., Petroni, F., Scala, A., Zollo, F., Quattrociocchi, W.: Viral misinformation: The role of homophily and polarization (2014). CoRR, vol. abs/1411.2893Google Scholar
  15. 15.
    McCord, M., Chuah, M.: Spam detection on twitter using traditional classifiers. In: Calero, J.M.A., Yang, L.T., Mármol, F.G., García Villalba, L.J., Li, A.X., Wang, Y. (eds.) ATC 2011. LNCS, vol. 6906, pp. 175–186. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  16. 16.
    Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: CEAS (2010)Google Scholar
  17. 17.
    Budak, C., Agrawal, D.: Abbadi, A.E.: Limiting the spread of misinformation in social networks. In: WWW, pp. 665–674 (2011)Google Scholar
  18. 18.
    Faloutsos, M.: Detecting malware with graph-based methods: traffic classification, botnets, and facebook scams. In: WWW (Companion Volume), pp. 495–496 (2013)Google Scholar
  19. 19.
    Ghosh, S., Viswanath, B., Kooti, F., Sharma, N.K., Korlam, G., Benevenuto, F., Ganguly, N., Gummadi, P.K.: Understanding and combating link farming in the twitter social network. In: WWW, pp. 61–70 (2012)Google Scholar
  20. 20.
    Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we rt? In: Proceedings of the First Workshop on Social Media Analytics, ser. SOMA 2010, pp. 71–79. ACM, New York (2010)Google Scholar
  21. 21.
    Liu, Y., Wu, B., Wang, B., Li, G.: Sdhm: a hybrid model for spammer detection in weibo. In: 2014 IEEE/ACM International Conference on ASONAM, pp. 942–947, August 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sotirios Antoniadis
    • 1
  • Iouliana Litou
    • 2
    Email author
  • Vana Kalogeraki
    • 2
  1. 1.Nokia Solutions and Networks Hellas A.E.AthensGreece
  2. 2.Deptartment of InformaticsAthens University of Economics and BusinessAthensGreece

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