Fake News Detection via NLP is Vulnerable to Adversarial Attacks

News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle for the 45th President of the United States. While quite a few detection methods have been proposed to combat fake news since 2015, they focus mainly on linguistic aspects of an article without any fact checking. In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news. Through experiments on Fakebox, a state-of-the-art fake news detector, we show that fact tampering attacks can be effective. To address these weaknesses, we argue that fact checking should be adopted in conjunction with linguistic characteristics analysis, so as to truly separate fake news from real news. A crowdsourced knowledge graph is proposed as a straw man solution to collecting timely facts about news events.

[1]  Sungyong Seo,et al.  CSI: A Hybrid Deep Model for Fake News Detection , 2017, CIKM.

[2]  Haixun Wang,et al.  Probase: a probabilistic taxonomy for text understanding , 2012, SIGMOD Conference.

[3]  Yimin Chen,et al.  Automatic deception detection: Methods for finding fake news , 2015, ASIST.

[4]  Yimin Chen,et al.  Misleading Online Content: Recognizing Clickbait as "False News" , 2015, WMDD@ICMI.

[5]  Dawei Liu,et al.  LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs , 2014, AAAI.

[6]  Lora Aroyo,et al.  Crowdsourcing Ground Truth for Medical Relation Extraction , 2017, ACM Trans. Interact. Intell. Syst..

[7]  Chu-Ren Huang,et al.  Fake News Detection Through Multi-Perspective Speaker Profiles , 2017, IJCNLP.

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

[9]  Filippo Menczer,et al.  Hoaxy: A Platform for Tracking Online Misinformation , 2016, WWW.

[10]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[11]  Mykhailo Granik,et al.  Fake news detection using naive Bayes classifier , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[12]  Demetrios Zeinalipour-Yazti,et al.  Crowdsourcing with Smartphones , 2012, IEEE Internet Computing.

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

[14]  Svitlana Volkova,et al.  Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter , 2017, ACL.

[15]  Yimin Chen,et al.  Deception detection for news: Three types of fakes , 2015, ASIST.

[16]  Le Song,et al.  Fake News Mitigation via Point Process Based Intervention , 2017, ICML.

[17]  Eugenio Tacchini,et al.  Some Like it Hoax: Automated Fake News Detection in Social Networks , 2017, ArXiv.

[18]  Sibel Adali,et al.  This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News , 2017, Proceedings of the International AAAI Conference on Web and Social Media.

[19]  Yuanzhuo Wang,et al.  Locally Adaptive Translation for Knowledge Graph Embedding , 2015, AAAI.

[20]  Piet Van Mieghem.  Human Psychology of Common Appraisal: The Reddit Score , 2011, IEEE Trans. Multim..

[21]  Eunsol Choi,et al.  Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking , 2017, EMNLP.

[22]  Jeff Z. Pan,et al.  Content Based Fake News Detection Using Knowledge Graphs , 2018, SEMWEB.

[23]  Yuanzhuo Wang,et al.  OpenKN: An open knowledge computational engine for network big data , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[24]  Georg Rehm,et al.  From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles , 2017, NLPmJ@EMNLP.