Graph Mining Meets Fake News Detection

Nowadays, the diversified services on social media make news diffused at higher rate and larger volumes, which poses unique challenges in terms of the efficiency, scalability, and accuracy on the fake news detection. To solve these issues, graph mining, as a promising direction of data mining, has successfully attracted attentions of recent studies. In this chapter, we present a comprehensive study on recent graph-based fake news detection approaches and show how graph mining enables the whole task. We first introduce different kinds of information related to fake news, then divide the existing graph-based approaches into two scenarios, where various graphs and graph patterns are introduced to model the information on social media and characterize features of the fake news, respectively.

[1]  Yang Liu,et al.  Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks , 2018, AAAI.

[2]  Venkatesan Guruswami,et al.  CopyCatch: stopping group attacks by spotting lockstep behavior in social networks , 2013, WWW.

[3]  Hiroki Arimura,et al.  LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets , 2004, FIMI.

[4]  Sara Cohen,et al.  Efficient Enumeration of Maximal k-Plexes , 2015, SIGMOD Conference.

[5]  Tanmoy Chakraborty,et al.  Unsupervised Fake News Detection: A Graph-based Approach , 2020, HT.

[6]  William Yang Wang.  “Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection , 2017, ACL.

[7]  K. Yamato,et al.  Transcription factor DUO1 generated by neo-functionalization is associated with evolution of sperm differentiation in plants , 2018, Nature Communications.

[8]  Lu Qin,et al.  Mining Bursting Communities in Temporal Graphs , 2019, ArXiv.

[9]  Yun Zhang,et al.  On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types , 2013, BMC Bioinformatics.

[10]  Reynold Cheng,et al.  Efficient Algorithms for Densest Subgraph Discovery , 2019, Proc. VLDB Endow..

[11]  Lusheng Wang,et al.  Modeling Protein Interacting Groups by Quasi-Bicliques: Complexity, Algorithm, and Application , 2010, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[12]  Charalampos E. Tsourakakis.  The K-clique Densest Subgraph Problem , 2015, WWW.

[13]  Celso C. Ribeiro,et al.  An exact algorithm for the maximum quasi-clique problem , 2019, Int. Trans. Oper. Res..

[14]  Anil Vullikanti,et al.  SubGraph2Vec: Highly-Vectorized Tree-like Subgraph Counting , 2019, 2019 IEEE International Conference on Big Data (Big Data).

[15]  Huan Liu,et al.  Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate , 2018, WSDM.

[16]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[17]  Giovanni Luca Ciampaglia,et al.  The spread of low-credibility content by social bots , 2017, Nature Communications.

[18]  Benno Stein,et al.  A Stylometric Inquiry into Hyperpartisan and Fake News , 2017, ACL.

[19]  Christos Faloutsos,et al.  Netprobe: a fast and scalable system for fraud detection in online auction networks , 2007, WWW '07.

[20]  Christos Faloutsos,et al.  TellTail: Fast Scoring and Detection of Dense Subgraphs , 2020, AAAI.

[21]  Christos Faloutsos,et al.  Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms , 2016, IEEE Transactions on Knowledge and Data Engineering.

[22]  Matthieu Latapy,et al.  Computing maximal cliques in link streams , 2015, Theor. Comput. Sci..

[23]  Jinyan Li,et al.  Mining maximal quasi‐bicliques: Novel algorithm and applications in the stock market and protein networks , 2009, Stat. Anal. Data Min..

[24]  Shuai Ma,et al.  An Efficient Approach to Finding Dense Temporal Subgraphs , 2020, IEEE Transactions on Knowledge and Data Engineering.

[25]  Lu Qin,et al.  Mining Periodic Cliques in Temporal Networks , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[26]  Christos Faloutsos,et al.  Patterns and anomalies in k-cores of real-world graphs with applications , 2018, Knowledge and Information Systems.

[27]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[28]  Jeffrey Xu Yu,et al.  Persistent Community Search in Temporal Networks , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[29]  Jakub W. Pachocki,et al.  Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling , 2015, KDD.

[30]  Teresa J. Feo,et al.  Structural absorption by barbule microstructures of super black bird of paradise feathers , 2018, Nature Communications.

[31]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[32]  Gerhard Weikum,et al.  DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning , 2018, EMNLP.

[33]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[34]  Andrew V. Goldberg,et al.  Finding a Maximum Density Subgraph , 1984 .

[35]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[36]  G. Caldarelli,et al.  The spreading of misinformation online , 2016, Proceedings of the National Academy of Sciences.

[37]  Robert E. Tarjan,et al.  A Fast Parametric Maximum Flow Algorithm and Applications , 1989, SIAM J. Comput..

[38]  Wei Gao,et al.  Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning , 2017, ACL.

[39]  Christos Faloutsos,et al.  oddball: Spotting Anomalies in Weighted Graphs , 2010, PAKDD.

[40]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[41]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[42]  Guimei Liu,et al.  Prequential analysis of complex data with adaptive model reselection , 2009 .

[43]  Iryna Gurevych,et al.  A Retrospective Analysis of the Fake News Challenge Stance-Detection Task , 2018, COLING.

[44]  Christos Faloutsos,et al.  Catching Synchronized Behaviors in Large Networks , 2016, ACM Trans. Knowl. Discov. Data.

[45]  Hyun Ah Song,et al.  FRAUDAR: Bounding Graph Fraud in the Face of Camouflage , 2016, KDD.

[46]  Christos Faloutsos,et al.  EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs , 2010, PAKDD.

[47]  Stefano Ceri,et al.  False News On Social Media: A Data-Driven Survey , 2019, SGMD.