Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media. The wide spread of disinformation and fake news can cause detrimental societal effects. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation. The goal of this chapter is to pave the way for appreciating the challenges and advancements via: (1) introducing the types of information disorder on social media and examine their differences and connections; (2) describing important and emerging tasks to combat disinformation for characterization, detection and attribution; and (3) discussing a weak supervision approach to detect disinformation with limited labeled data. We then provide an overview of the chapters in this book that represent the recent advancements in three related parts: (1) user engagements in the dissemination of information disorder; (2) techniques on detecting and mitigating disinformation; and (3) trending issues such as ethics, blockchain, clickbaits, etc. We hope this book to be a convenient entry point for researchers, practitioners, and students to understand the problems and challenges, learn state-of-the-art solutions for their specific needs, and quickly identify new research problems in their domains.

[1]  Lantao Yu,et al.  SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient , 2016, AAAI.

[2]  Shuo Yang,et al.  Unsupervised Fake News Detection on Social Media: A Generative Approach , 2019, AAAI.

[3]  Yongdong Zhang,et al.  News Verification by Exploiting Conflicting Social Viewpoints in Microblogs , 2016, AAAI.

[4]  Fenglong Ma,et al.  EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection , 2018, KDD.

[5]  Yoshua Bengio,et al.  Maximum-Likelihood Augmented Discrete Generative Adversarial Networks , 2017, ArXiv.

[6]  Yan Liu,et al.  Neural User Response Generator: Fake News Detection with Collective User Intelligence , 2018, IJCAI.

[7]  Huan Liu,et al.  Detecting Fake News on Social Media , 2019, Synthesis Lectures on Data Mining and Knowledge Discovery.

[8]  Jing Qian,et al.  A Survey on Natural Language Processing for Fake News Detection , 2018, LREC.

[9]  E. Papalexakis.  Unsupervised Content-Based Identification of Fake News Articles with Tensor Decomposition Ensembles , 2018 .

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

[11]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[12]  Lav R. Varshney,et al.  CTRL: A Conditional Transformer Language Model for Controllable Generation , 2019, ArXiv.

[13]  Ali Farhadi,et al.  Defending Against Neural Fake News , 2019, NeurIPS.

[14]  Huan Liu,et al.  Trust in social computing , 2014, WWW.

[15]  Ke Wang,et al.  SentiGAN: Generating Sentimental Texts via Mixture Adversarial Networks , 2018, IJCAI.

[16]  Dirk Hovy,et al.  The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study , 2016, ACL.

[17]  Huan Liu,et al.  Seeking provenance of information using social media , 2013, CIKM.

[18]  Fred Morstatter,et al.  Misinformation in Social Media: Definition, Manipulation, and Detection , 2019, SKDD.

[19]  Kai Shu.  Beyond News Contents: The Role of Social Context for Fake News Detection , 2018 .

[20]  Yejin Choi,et al.  COMET: Commonsense Transformers for Automatic Knowledge Graph Construction , 2019, ACL.

[21]  Andrew M. Dai,et al.  MaskGAN: Better Text Generation via Filling in the ______ , 2018, ICLR.

[22]  Reza Zafarani,et al.  Fake News: A Survey of Research, Detection Methods, and Opportunities , 2018, ArXiv.

[23]  Huan Liu,et al.  Provenance Data in Social Media , 2013, Synthesis Lectures on Data Mining and Knowledge Discovery.

[24]  Mohammad Ali Abbasi,et al.  Measuring User Credibility in Social Media , 2013, SBP.

[25]  Jason Yosinski,et al.  Plug and Play Language Models: A Simple Approach to Controlled Text Generation , 2020, ICLR.

[26]  Kathleen McKeown,et al.  Persuasive Influence Detection: The Role of Argument Sequencing , 2018, AAAI.

[27]  Huan Liu,et al.  Understanding User Profiles on Social Media for Fake News Detection , 2018, 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[28]  Daniel F. Stone,et al.  Media Bias in the Marketplace: Theory , 2014 .

[29]  Yong Yu,et al.  Long Text Generation via Adversarial Training with Leaked Information , 2017, AAAI.

[30]  Suhang Wang,et al.  SAME: Sentiment-Aware Multi-Modal Embedding for Detecting Fake News , 2019, 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[31]  James T. Kwok,et al.  Generalizing from a Few Examples , 2019, ACM Comput. Surv..

[32]  H. Russell Bernard,et al.  Studying Fake News via Network Analysis: Detection and Mitigation , 2018, Lecture Notes in Social Networks.

[33]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[34]  Jintao Li,et al.  Automatic Rumor Detection on Microblogs: A Survey , 2018, ArXiv.

[35]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[36]  Huan Liu,et al.  Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation , 2019, ICWSM.

[37]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[38]  Reza Zafarani,et al.  Fake News: Fundamental Theories, Detection Strategies and Challenges , 2019, WSDM.

[39]  Krishna P. Gummadi,et al.  Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media , 2017, CSCW.

[40]  Quanming Yao,et al.  Few-shot Learning: A Survey , 2019, ArXiv.

[41]  Alec Radford,et al.  Release Strategies and the Social Impacts of Language Models , 2019, ArXiv.

[42]  Kathy McKeown,et al.  Identifying Causal Relations Using Parallel Wikipedia Articles , 2016, ACL.

[43]  Huan Liu,et al.  dEFEND: Explainable Fake News Detection , 2019, KDD.