/AwesomeFakeNews

This repository contains recent research on fake news.

GNU General Public License v3.0GPL-3.0

Awesome Fake News

This repository contains recent research on fake news. Inspired from other 'awesome' github pages like awesome-deep-learning.

Table of content:

a) Data repository

SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours

Verifying Multimedia Use at MediaEval 2015 image-verification-corpus

Kaggle dataset: Getting Real about Fake News

FakeNewsChallenge Fake News Challenge 1

BuzzFeedNews Partisan News Analysis

FakeNewsCorpus FakeNewsCorpus - about 10 million news articles classified using opensources.co types

Wikipedia Fact-Checking Dataset FEVER: a large-scale dataset for Fact Extraction and VERification

Data for politifact.com, also check Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection

Some websites sharing fake articles: https://gist.github.com/Criipi/a3a7357466821f2ec62ce42b2529394b

b) Publications

2018, Kashyap Popat, Subhabrata Mukherjee, Andrew Yates,Gerhard Weikum, DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning, EMNLP 2018, http://aclweb.org/anthology/D18-1003

2018, Srijan Kumar, Meng Jiang, Taeho Jung, Roger Jie Luo, and Jure Leskovec. 2018. MIS2: Misinformation and Misbehavior Mining on the Web. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM '18). ACM, New York, NY, USA, 799-800. DOI: https://doi.org/10.1145/3159652.3160597

2018, Shu, K., Bernard, H.R., & Liu, H. (2018). Studying Fake News via Network Analysis: Detection and Mitigation. CoRR, abs/1804.10233.

2018, Vargo, C.J., Guo, L., & Amazeen, M.A. (2018). The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016. New Media & Society, 20, 2028-2049.

2018, Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM, New York, NY, USA, 849-857. (DOI: )[https://doi.org/10.1145/3219819.3219903]

2018, Jooyeon Kim, Behzad Tabibian, Alice Oh, Bernhard Schölkopf, and Manuel Gomez-Rodriguez. 2018. Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation. In Proceedings of WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining, Marina Del Rey, Ca, USA, February 5–9, 2018 (WSDM 2018), 9 pages. https://doi.org/10.1145/3159652.3159734

2018, Potthast, Martin, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. "A stylometric inquiry into hyperpartisan and fake news." arXiv preprint arXiv:1702.05638 (2017). ACL 2018

2018, Nguyen Vo, Kyumin Lee, The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News, SIGIR 2018, https://arxiv.org/pdf/1806.07516.pdf

2018, Yaqing Wang, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

2018, Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, and Andreas Krause. 2018. Fake News Detection in Social Networks via Crowd Signals. In Companion Proceedings of the The Web Conference 2018 (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 517-524. DOI: https://doi.org/10.1145/3184558.3188722

To appear, 2018, Kumar, Srijan, and Neil Shah. False information on web and social media: A survey arXiv preprint arXiv:1804.08559(2018).

March, 2018, Soroush Vosoughi, Deb Roy, Sinan Aral, The spread of true and false news online

March, 2018, David M. J. Lazer, Matthew A. Baum, Yochai Benkler, Adam J. Berinsky, Kelly M. Greenhill, Filippo Menczer, Miriam J. Metzger, Brendan Nyhan, Gordon Pennycook, David Rothschild, Michael Schudson, Steven A. Sloman, Cass R. Sunstein, Emily A. Thorson, Duncan J. Watts, Jonathan L. Zittrain The science of fake news

Feb, 2018, Liang Wu, Huan Liu Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate

2017, Buntain, Cody, and Jennifer Golbeck. "Automatically Identifying Fake News in Popular Twitter Threads." In Smart Cloud (SmartCloud), 2017 IEEE International Conference on, pp. 208-215. IEEE, 2017.

2017, Volkova, Svitlana, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. "Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 647-653. 2017

April, 2017, Eugenio Tacchini, Gabriele Ballarin, Marco L. Della Vedova, Stefano Moret, Luca de Alfaro. ["Some Like it Hoax: Automated Fake News Detection in Social Networks"] (https://arxiv.org/abs/1704.07506)

2017, Rashkin, Hannah, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. "Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2921-2927. 2017.

2017, Wang, William Yang. Liar, Liar Pants on Fire: A New Benchmark Dataset for Fake News Detection

2017, Shu, Kai, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. "Fake News Detection on Social Media: A Data Mining Perspective." ACM SIGKDD Explorations Newsletter 19, no. 1 (2017): 22-36.

2016, Kumar, Srijan, Robert West, and Jure Leskovec. "Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes." In Proceedings of the 25th International Conference on World Wide Web, pp. 591-602. International World Wide Web Conferences Steering Committee, 2016.

2016, Rubin, Victoria, Niall Conroy, Yimin Chen, and Sarah Cornwell. "Fake news or truth? using satirical cues to detect potentially misleading news." In Proceedings of the Second Workshop on Computational Approaches to Deception Detection, pp. 7-17. 2016.

2015, Rubin, Victoria L., Yimin Chen, and Niall J. Conroy. "Deception detection for news: three types of fakes." Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1-4.

2015, Conroy, Niall J., Victoria L. Rubin, and Yimin Chen. "Automatic deception detection: Methods for finding fake news." Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1-4.

2015, Hassan, Naeemul, Chengkai Li, and Mark Tremayne. "Detecting check-worthy factual claims in presidential debates." In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1835-1838. ACM, 2015.

c) Tutorials

demidovakatya/competitions

d) Useful Websites

[Pheme Project](https://www.pheme.eu/software-downloads/ [Pheme Project)

Analysis of fake news dataset with Machine Learning

Fake News Challenge

Politifact

e) Related Work, but not necessarily on Fake-news

2018, Kiran Garimella et al. Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship

2018, Glenski, Maria, Tim Weninger, and Svitlana Volkova. "Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources." arXiv preprint arXiv:1805.12032 (2018).

For Social Media polarization and Echo-chambers, check this github page

For Stance learning, check this github page