Predicting Future Rumours

QIN Yumeng; Dominik Wurzer; TANG Cunchen

doi:10.1049/cje.2018.03.008

Volume 27 Issue 3

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QIN Yumeng, Dominik Wurzer, TANG Cunchen, “Predicting Future Rumours,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 514-520, 2018, doi: 10.1049/cje.2018.03.008

Citation:

QIN Yumeng, Dominik Wurzer, TANG Cunchen, “Predicting Future Rumours,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 514-520, 2018, doi: 10.1049/cje.2018.03.008

QIN Yumeng, Dominik Wurzer, TANG Cunchen, “Predicting Future Rumours,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 514-520, 2018, doi: 10.1049/cje.2018.03.008

Citation:

QIN Yumeng, Dominik Wurzer, TANG Cunchen, “Predicting Future Rumours,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 514-520, 2018, doi: 10.1049/cje.2018.03.008

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Predicting Future Rumours

doi: 10.1049/cje.2018.03.008

1.
School of Computer Science(International School of Software), Wuhan University, Wuhan 430072, China;
2.
School of Information Management, Wuhan University, Wuhan 430072, China;
3.
AI Machina Technologies, Vienna A-1010, Austria

Received Date: 2017-05-26
Rev Recd Date: 2017-11-10
Publish Date: 2018-05-10

Abstract

Abstract

Recent uproar of fake news and misinformation on social media platforms has sparked the interest in the scientific community to automatically detect and refute them. The most popular research task to counteract misinformation, Rumour detection, requires repeated signals to reach adequate detection accurate. Consequently, rumour detection recognizes rumours only when they have started spreading and causing harm. We introduce a new task called "rumour prediction" that assesses the possibility of a document arriving from a social media stream becoming a rumour in the future. Note that rumour prediction differentiates itself from rumour detection through instant decision making. This allows refuting misinformation before it spreads and causes harm. Our approach to rumour prediction harnesses content based features in combination with novelty based features and pseudo feedback. Our experiments show that we are able to accurately predict, whether a document will become a rumour in the future. Additionally, we show how rumour prediction can significantly improve the accuracy of state-of-the-art Rumour detection systems.
- Real-time rumour prediction,
- Social media,
- Data stream,
- Information retrieval,
- K-term hashing,
- Pseudo feedback

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References(25)

References

S. Petrovic, M. Osborne, R. McCreadie, et al., "Can Twitter replace Newswire for breaking news?", Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media., Massachusetts, USA, pp.713-716, 2013.

Z. Zhao, P. Resnick and Q. Mei, "Enquiring minds:Early detection of rumors in social media from enquiry posts", Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp.1395-1405, 2015.

G. Cai, H. Wu and R. Lv, "Rumours detection in Chinese via crowd responses", IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Beijing, China, pp.912-917, 2014.

S. Kwon, M. Cha, K. Jung, et al.,"Prominent features of rumor propagation in online social media", 13th International Conference on Data Mining (ICDM), IEEE, Dallas, TX, USA, pp.1103-1108, 2013.

V. Qazvinian, E. Rosengren, D.R. Radev, et al., "Rumour has it:Identifying misinformation in Microblogs", Proceedings of the Conference on Empirical Methods in Natural Language Processing, Edinburgh, UK, pp.1589-1599, 2011.

X. Liu, A. Nourbakhsh, Q. Li, R. Fang, et al., "Real-time rumor debunking on twitter", Proceedings of the 24th ACM International Conference on Information and Knowledge Management, ACM, Melbourne, Australia, pp.1867-1870, 2015.

Fan Yang, Xiaohui Yu, Yang Liu, et al., "Automatic detection of rumor on Sina Weibo", Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, Beijing, China, Page 13, 2012.

X. Zhou, J. Cao, Z. Jin, et al., "Realtime news certification system on sina weibo", Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp.983-988, 2015.

S. Muthukrishnan, "Data streams:Algorithms and applications". Foundations and Trends in Theoretical Computer Science, Vol.1, No.2, pp.117-236, 2005.

K. Wu, S. Yang and K. Zhu, "False rumours detection on Sina Weibo by propagation structures", 2015 IEEE 31st International Conference on Data Engineering, Seoul, South Korea, pp.651-662, 2015.

Shihan Wang and Takao Terano, "Detecting rumour patterns in streaming social media", IEEE International Conference on Big Data, Guimi, Santa Clara, CA, USA, pp.2709-2715, 2015.

C. Castillo, M. Mendoza and B. Poblete, "Information credibility on Twitter", The 20th International Conference on World Wide Web, Hyderabad, India, pp.675-684, 2011.

M. Mendoza, Poblete B and Castillo C, "Twitter under crisis:Can we trust what we RT?", The 1st Workshop on Social Media Analytics, SOMA, pp.71-79, 2010.

Shah D and Zaman T, "Rumors in a network:Who's the culprit?", IEEE Transactions on Information Theory, Vol.57, No.8, pp.5163-5181, 2011.

Y. Matsubara, Y. Sakurai, B. Prakash, et al., "Rise and fall patterns of information diffusion:Model and implications", Proceedings of the International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp.6-14, 2012.

S. Sun, H. Liu, J. He, et al., "Detecting event rumours on Sina Weibo automatically", In Asia-Pacific Web Conference, Sydney, NSW, Australia, pp.120-131, 2013.

Yumeng Qin, Dominik Wurzer, Victor Lavrenko, et al., "Counteracting novelty decay in first story detection", European Conference on Information Retrieval, Lecture Notes in Computer Science, Springer, Aberdeen, UK, Vol.10193, pp.555-560, 2017.

Dominik Wurzer, Victor Lavrenko and Miles Osborne, "Tracking unbounded Topic Streams", Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics, ACL, Beijing, China, Vol.1, pp.1765-1773, 2015.

Dominik Wurzer, Victor Lavrenko and Miles Osborne, "Twitter-scale new event detection via K-term hashing", Proceedings of the Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp.2584-2589, 2015.

S. Petrovic, M. Osborne and V. Lavrenko, "RT to win! predicting message propagation in twitter", The International Conference on Weblogs and Social Media, Barcelona, Spain, pp.586-589, 2011.

B. Suh, L. Hong, P. Pirolli, et al., "Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network", IEEE Second International Conference on Social Computing, Minneapolis, MN, USA, pp.177-184, 2010.

T.R. Zaman, R. Herbrich, van J. Gael, et al., "Predicting information spreading in twitter", Workshop on Computational Social Science and the Wisdom of Crowds, Vol.104, No.45, pp.17599-601, 2010.

James Allan, Victor Lavrenko and Hubert Jin, "First story detection in TDT is hard", Proceedings of the Ninth International Conference on Information and Knowledge Management, ACM, McLean, Virginia, USA, pp.374-381, 2000.

Jonathan G. Fiscus and G R. Doddington, "Topic detection and tracking evaluation overview", The Information Retrieval Series, Vol.12, pp.17-31, 2002.

James Allan, Topic Detection and Tracking:Event-Based Information Organization, Kluwer Academic Publishers, Norwell, 2002.

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