Liang Wu
Fred Morstatter
Huan Liu
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Abstract
The widespread dissemination of misinformation in social media has recently received a lot of attention in academia. While the problem of misinformation in social media has been intensively studied, there are seemingly different definitions for the same problem, and inconsistent results in different studies. In this survey, we aim to consolidate the observations, and investigate how an optimal method can be selected given specific conditions and contexts. To this end, we first introduce a definition for misinformation in social media and we examine the difference between misinformation detection and classic supervised learning. Second, we describe the diffusion of misinformation and introduce how spreaders propagate misinformation in social networks. Third, we explain characteristics of individual methods of misinformation detection, and provide commentary on their advantages and pitfalls. By reflecting applicability of different methods, we hope to enable the intensive research in this area to be conveniently reused in real-world applications and open up potential directions for future studies.
- L. Akoglu, H. Tong, and D. Koutra. Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3):626--688, 2015.Google Scholar
Digital Library
- M. Alfifi, P. Kaghazgaran, J. Caverlee, and F. Morstatter. Measuring the impact of isis social media strategy, 2018.Google Scholar
- F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), volume 6, page 12, 2010.Google Scholar
- D. M. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77--84, 2012.Google ScholarDigital Library
- J. Bollen, H. Mao, and A. Pepe. Determining the public mood state by analysis of microblogging posts. In ALIFE, pages 667--668, 2010.Google Scholar
- C. Castillo, M. Mendoza, and B. Poblete. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web, pages 675--684. ACM, 2011.Google ScholarDigital Library
- C. Castillo, M. Mendoza, and B. Poblete. Predicting information credibility in time-sensitive social media. Internet Research, 23(5):560--588, 2013.Google ScholarCross Ref
- Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia. Who is tweeting on twitter: human, bot, or cyborg? In Proceedings of the 26th annual computer security applications conference, pages 21--30. ACM, 2010.Google ScholarDigital Library
- A. Friggeri, L. A. Adamic, D. Eckles, and J. Cheng. Rumor cascades. In ICWSM, 2014.Google Scholar
- J. Gao, F. Liang, W. Fan, C. Wang, Y. Sun, and J. Han. On community outliers and their efficient detection in information networks. In SIGKDD, pages 813-- 822. ACM, 2010.Google ScholarDigital Library
- G. B. Guacho, S. Abdali, N. Shah, and E. E. Papalexakis. Semi-supervised content-based detection of misinformation via tensor embeddings. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 322-- 325. IEEE, 2018.Google ScholarCross Ref
- A. Gupta and P. Kumaraguru. Credibility ranking of tweets during high impact events. In Proceedings of the 1st workshop on privacy and security in online social media, page 2. ACM, 2012.Google Scholar
- S. Hosseinimotlagh and E. E. Papalexakis. Unsupervised content-based identification of fake news articles with tensor decomposition ensembles. 2018.Google Scholar
- X. Hu, J. Tang, Y. Zhang, and H. Liu. Social spammer detection in microblogging. In IJCAI, volume 13, pages 2633--2639, 2013.Google ScholarDigital Library
- Z. Jin, J. Cao, H. Guo, Y. Zhang, Y. Wang, and J. Luo. Detection and analysis of 2016 us presidential election related rumors on twitter. In International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, pages 14--24. Springer, 2017.Google ScholarCross Ref
- P. Kaghazgaran, J. Caverlee, and A. Squicciarini. Combating crowdsourced review manipulators: A neighborhood-based approach. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 306--314. ACM, 2018.Google ScholarDigital Library
- J. Kim, D. Kim, and A. Oh. Homogeneity-based transmissive process to model true and false news in social networks. arXiv preprint arXiv:1811.09702, 2018.Google Scholar
- E. M. Knorr and R. T. Ng. A unified notion of outliers: Properties and computation. In KDD, pages 219--222, 1997.Google Scholar
- S. Kwon and M. Cha. Modeling bursty temporal pattern of rumors. In ICWSM, 2014.Google Scholar
- K. Lee, J. Caverlee, and S. Webb. Uncovering social spammers: social honeypots+ machine learning. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 435--442. ACM, 2010.Google ScholarDigital Library
- K. Lee, B. D. Eoff, and J. Caverlee. Seven months with the devils: A long-term study of content polluters on twitter. In ICWSM. Citeseer, 2011. SIGKDD Explorations Volume 21, Issue 1 Page 89Google Scholar
- S. Lee and J. Kim. Early filtering of ephemeral malicious accounts on twitter. Computer Communications, 54:48--57, 2014.Google ScholarDigital Library
- C. Li, W. Jiang, and X. Zou. Botnet: Survey and case study. In Innovative Computing, Information and Control (ICICIC), 2009 Fourth International Conference on, pages 1184--1187. IEEE, 2009.Google ScholarDigital Library
- H. Li, Z. Chen, A. Mukherjee, B. Liu, and J. Shao. Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In Ninth International AAAI Conference on Web and Social Media, 2015.Google Scholar
- E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw. Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 939--948. ACM, 2010.Google ScholarDigital Library
- M. Mccord and M. Chuah. Spam detection on twitter using traditional classifiers. In international conference on Autonomic and trusted computing, pages 175--186. Springer, 2011.Google ScholarDigital Library
- M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, pages 415--444, 2001.Google Scholar
- J. M. Ponte and W. B. Croft. A language modeling approach to information retrieval. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 275--281. ACM, 1998.Google ScholarDigital Library
- 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, pages 1589-- 1599. Association for Computational Linguistics, 2011.Google ScholarDigital Library
- J. Ratkiewicz, M. Conover, M. Meiss, B. Gon¸calves, S. Patil, A. Flammini, and F. Menczer. Truthy: mapping the spread of astroturf in microblog streams. In Proceedings of the 20th international conference companion on World wide web, pages 249--252. ACM, 2011.Google ScholarDigital Library
- J. Ratkiewicz, M. Conover, M. Meiss, B. Gonalves, A. Flammini, , and F. Menczer. Detecting and tracking political abuse in social media. In ICWSM, 2011.Google Scholar
- S. Rayana and L. Akoglu. Collective opinion spam detection: Bridging review networks and metadata. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 985--994. ACM, 2015.Google ScholarDigital Library
- X. Rong. word2vec parameter learning explained. arXiv preprint arXiv:1411.2738, 2014.Google Scholar
- N. Ruchansky, S. Seo, and Y. Liu. Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 797--806. ACM, 2017.Google ScholarDigital Library
- G. Salton and M. J. McGill. Introduction to modern information retrieval. 1986.Google Scholar
- J. Sampson, F. Morstatter, L. Wu, and H. Liu. Leveraging the implicit structure within social media for emergent rumor detection. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pages 2377--2382. ACM, 2016.Google ScholarDigital Library
- S. Sedhai and A. Sun. Hspam14: A collection of 14 million tweets for hashtag-oriented spam research. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 223--232. ACM, 2015.Google ScholarDigital Library
- K. Sharma, F. Qian, H. Jiang, N. Ruchansky, M. Zhang, and Y. Liu. Combating fake news: A survey on identification and mitigation techniques. arXiv preprint arXiv:1901.06437, 2019.Google Scholar
- K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu. Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1):22-- 36, 2017.Google ScholarDigital Library
- K. Starbird, J. Maddock, M. Orand, P. Achterman, and R. M. Mason. Rumors, false flags, and digital vigilantes: Misinformation on twitter after the 2013 boston marathon bombing. IConference 2014 Proceedings, 2014.Google Scholar
- K. Thomas, C. Grier, and V. Paxson. Adapting social spam infrastructure for political censorship. In Proceedings of the 5th USENIX conference on Large-Scale Exploits and Emergent Threats, pages 13--13. USENIX Association, 2012.Google ScholarDigital Library
- N. Vo and K. Lee. The rise of guardians: Fact-checking url recommendation to combat fake news. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 275--284. ACM, 2018.Google ScholarDigital Library
- G. Wang, S. Xie, B. Liu, and P. S. Yu. Review graph based online store review spammer detection. In Data mining (icdm), 2011 ieee 11th international conference on, pages 1242--1247. IEEE, 2011.Google Scholar
- G. Wang, S. Xie, B. Liu, and P. S. Yu. Identify online store review spammers via social review graph. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4):61, 2012.Google Scholar
- S. Webb, J. Caverlee, and C. Pu. Social Honeypots: Making Friends With A Spammer Near You. Conference on Email and Anti-Spam, 2008.Google Scholar
- J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the 3rd ACM conference on WSDM, pages 261-- 270. ACM, 2010.Google ScholarDigital Library
- L. Wu, X. Hu, F. Morstatter, and H. Liu. Adaptive spammer detection with sparse group modeling. In ICWSM, pages 319--326, 2017.Google Scholar
- L. Wu, X. Hu, F. Morstatter, and H. Liu. Detecting camouflaged content polluters. In ICWSM, pages 696-- 699, 2017. SIGKDD Explorations Volume 21, Issue 1 Page 90Google Scholar
- L. Wu, J. Li, X. Hu, and H. Liu. Gleaning wisdom from the past: Early detection of emerging rumors in social media. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 99--107. SIAM, 2017.Google ScholarCross Ref
- L. Wu and H. Liu. Tracing fake-news footprints: Characterizing social media messages by how they propagate. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pages 637--645. ACM, 2018.Google ScholarDigital Library
- L. Wu, F. Morstatter, X. Hu, and H. Liu. Mining misinformation in social media. Big Data in Complex and Social Networks, pages 123--152, 2016.Google Scholar
- S. Wu, Q. Liu, Y. Liu, L. Wang, and T. Tan. Information credibility evaluation on social media. In AAAI, pages 4403--4404, 2016.Google Scholar
- Y. Xie, F. Yu, K. Achan, R. Panigrahy, G. Hulten, and I. Osipkov. Spamming botnets: signatures and characteristics. ACM SIGCOMM Computer Communication Review, 38(4):171--182, 2008.Google ScholarDigital Library
- 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, page 13. ACM, 2012.Google Scholar
- J. Ye and L. Akoglu. Discovering opinion spammer groups by network footprints. In Machine Learning and Knowledge Discovery in Databases, pages 267--282. Springer, 2015.Google ScholarDigital Library
- S. Yu, M. Li, and F. Liu. Rumor identification with maximum entropy in micronet. Complexity, 2017, 2017.Google Scholar
- R. Zafarani and H. Liu. Evaluation without ground truth in social media research. Commun. ACM, 58(6):54--60, 2015.Google ScholarDigital Library
- Q. Zhang, S. Zhang, J. Dong, J. Xiong, and X. Cheng. Automatic detection of rumor on social network. In Natural Language Processing and Chinese Computing, pages 113--122. Springer, 2015.Google ScholarDigital Library
- 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, pages 1395--1405. International World Wide Web Conferences Steering Committee, 2015.Google ScholarDigital Library
- Y. Zhu, X. Wang, E. Zhong, N. N. Liu, H. Li, and Q. Yang. Discovering Spammers in Social Networks. AAAI, 2012.Google ScholarDigital Library
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