Health-related rumour detection on Twitter

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

Volumn 2017-January, Issue , 2017, Pages 1599-1606

  Sicilia, Rosa ^a   Giudice, Stella Lo ^b   Pei, Yulong ^c   Pechenizkiy, Mykola ^c   Soda, Paolo ^a  

^a UNIVERSITY CAMPUS BIO MEDICO   (Italy)

^b 2M Engineering   (Netherlands)

^c EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Health rumour detection; Network and User based Features; Social microblog; Twitter

Indexed keywords

BIOINFORMATICS; HEALTH;

DETECTION SYSTEM; INFORMATION SPREADING; INFORMATION STATEMENTS; MICRO-BLOG; NETWORK CHARACTERISTICS; POTENTIAL THREATS; TWITTER;

SOCIAL NETWORKING (ONLINE);

EID: 85045975327     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2017.8217899     Document Type: Conference Paper

References (24)

1. P. Domm, "False rumor of explosion at White House causes stocks to briefly plunge; AP confirms its Twitter feed was hacked," CNBC.COM, Vol. 23, 2013.
2. W. Guan, H. Gao, M. Yang, Y. Li, H. Ma, W. Qian, Z. Cao, and X. Yang, "Analyzing user behavior of the micro-blogging website Sina Weibo during hot social events," Physica A: Statistical Mechanics and its Applications, Vol. 395, pp. 340-351, 2014.
3. A. Zubiaga, M. Liakata, R. Procter, G. W. S. Hoi, and P. Tolmie, "Analysing how people orient to and spread rumours in social media by looking at conversational threads," PloS one, Vol. 11, No. 3, p. e0150989, 2016.
4. N. DiFonzo and P. Bordia, Rumor psychology: social and organizational approaches. American Psychological Association, 2007.
5. K. Wu, S. Yang, and K. Q. Zhu, "False rumors detection on Sina Weibo by propagation structures," in IEEE 31st International Conference on Data Engineering. IEEE, 2015, pp. 651-662.
6. C. Boyer, V. Baujard, and A. Geissbühler, "Evolution of health web certification through the HONcode experience." in MIE, 2011, pp. 53-57.
7. E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, "Finding high-quality content in social media," in Proceedings of the 2008 international conference on web search and data mining. ACM, 2008, pp. 183-194.
8. O. Alonso, C. Carson, D. Gerster, X. Ji, and S. U. Nabar, "Detecting uninteresting content in text streams," in SIGIR Crowdsourcing for Search Evaluation Workshop, 2010.
9. C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on Twitter," in Proceedings of the 20th international conference on World Wide Web. ACM, 2011, pp. 675-684.
10. A. L. Hughes and L. Palen, "Twitter adoption and use in mass convergence and emergency events," International Journal of Emergency Management, Vol. 6, No. 3-4, pp. 248-260, 2009.
11. S. Kwon and M. Cha, "Modeling Bursty Temporal Pattern of Rumors." in ICWSM, 2014.
12. S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent features of rumor propagation in online social media," in Data Mining (ICDM), 2013 IEEE 13th International Conference on. IEEE, 2013, pp. 1103-1108.
13. J. Ma, W. Gao, Z. Wei, Y. Lu, and K.-F. Wong, "Detect rumors using time series of social context information on microblogging websites," in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015, pp. 1751-1754.
14. V. Qazvinian, E. Rosengren, D. R. Radev, and Q. Mei, "Rumor has it: identifying misinformation in microblogs," in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011, pp. 1589-1599.
15. F. Yang, Y. Liu, X. Yu, and M. Yang, "Automatic detection of rumor on Sina Weibo," in Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. ACM, 2012, p. 13.
16. J. Zhao, N. Cao, Z. Wen, Y. Song, Y.-R. Lin, and C. Collins, "# fluxflow: Visual analysis of anomalous information spreading on social media," IEEE Transactions on Visualization and Computer Graphics, Vol. 20, No. 12, pp. 1773-1782, 2014.
17. Z. Zhao, P. Resnick, and Q. Mei, "Enquiring minds: Early detection of rumors in social media from enquiry posts," in Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015, pp. 1395-1405.
18. P. Analytics, "Twitter study," HYPERLINK" http://web.archive.org/web/20110715062407/www.pearanalytics.com/blog/wpcontent/uploads/2010/05/Twitter-Study-August-2009.pdf, 2009.
19. A. Java, X. Song, T. Finin, and B. Tseng, "Why we Twitter: understanding microblogging usage and communities," in Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on web mining and social network analysis. ACM, 2007, pp. 56-65.
20. K. S. Dave, R. Bhatt, and V. Varma, "Modelling Action Cascades in Social Networks." in ICWSM, 2011.
21. A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of LREC, Vol. 6. Citeseer, 2006, pp. 417-422.
22. L. C. Freeman, "Centrality in social networks conceptual clarification," Social networks, Vol. 1, No. 3, pp. 215-239, 1978.
23. S. H. Huang, "Supervised feature selection: a tutorial," Artificial Intelligence Research, Vol. 4, No. 2, p. 22, 2015.
24. A. FernáNdez, V. LóPez, M. Galar, M. J. Del Jesus, and F. Herrera, "Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches," Knowledgebased systems, Vol. 42, pp. 97-110, 2013.