This repository contains recent research on fake news. Inspired from other 'awesome' github pages like awesome-deep-learning.
If you would like your published paper/data to be added here, please message me.
Table of content:
2021,Singhal, Shivangi, Rajiv Ratn Shah, and Ponnurangam Kumaraguru. Factorization of Fact-Checks for Low Resource Indian Languages.
2021, Shivangi Aneja, Chris Bregler, Matthias Niessner, COSMOS: Catching Out-of-Context Misinformation using Self-Supervised Learning
2020, Shan Jiang, Miriam Metzger, Andrew Flanagin, Christo Wilson, Modeling and Measuring Expressed (Dis)belief in (Mis)information, ICWSM, 2020
2020, Julio C. S. Reis, Philipe Melo, Kiran Garimella, Jussara M. Almeida, Dean Eckles, Fabrício Benevenuto, A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections, ICWSM, 2020
2020, Enyan Dai, Yiwei Sun, Suhang Wang, Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository, ICWSM, 2020
2020, Fatma Arslan, Naeemul Hassan, Chengkai Li, Mark Tremayne, A Benchmark Dataset of Check-Worthy Factual Claims, ICWSM, 2020
2019, Nørregaard, J., Horne, B. D., & Adalı, S. (2019). NELA-GT-2018: A Large Multi-Labelled News Dataset for the Study of Misinformation in News Articles. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 630-638.
2019, Abu Salem, F. K., Al Feel, R., Elbassuoni, S., Jaber, M., & Farah, M. (2019). FA-KES: A Fake News Dataset around the Syrian War. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 573-582.
2019 ACL Paper: Archita Pathak, and Rohini K. Srihari BREAKING! Presenting Fake News Corpus For Automated Fact Checking Proceedings of the ACL 2019, Student Research Workshop, pages 357–362 Florence, Italy, July 28th-August 2nd, 2019
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
Fake News Copus : https://github.com/several27/FakeNewsCorpus
2021,H. Saleh, A. Alharbi and S. H. Alsamhi,OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection
2021,H. Ali et al., All Your Fake Detector are Belong to Us: Evaluating Adversarial Robustness of Fake-News Detectors Under Black-Box Settings
2021,Verma, Pawan Kumar, WELFake: word embedding over linguistic features for fake news detection.
2021, Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie, Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multimodal Data, AAAI 2021
2021, Amila Silva, Yi Han, Ling Luo, Shanika Karunasekera, Christopher Leckie, Propagation2Vec: Embedding partial propagation networks for explainable fake news early detection, Information Processing and Management Journal, 2021
2021, Dou, Yingtong, Kai Shu, Congying Xia, Philip S. Yu, and Lichao Sun. "User Preference-aware Fake News Detection." ACM SIGIR, 2021.
2020, Wang, Yaqing, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, and Jing Gao. "Weak supervision for fake news detection via reinforcement learning." In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 516-523. 2020.
2020, Vo, N., & Lee, K. (2020, November). Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 7717-7731).
2020, Tan, R., Plummer, B., & Saenko, K. (2020, November). Detecting Cross-Modal Inconsistency to Defend against Neural Fake News. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 2081-2106).
2020, Nguyen Vo, and Kyumin Lee. Where Are the Facts: Search for Fact-checked Information to Alleviate the Spread of Fake News.. EMNLP 2020.
2020, Thai Le, Suhang Wang, and Dongwon Lee. 2020. MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models. 20th IEEE International Conference on Data Mining (ICDM).
2020, Mingxi Cheng, Shahin Nazarian, and Paul Bogdan. 2020. VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text. In Proceedings of The Web Conference 2020 (WWW ’20). Association for Computing Machinery, New York, NY, USA, 2892–2898. DOI:https://doi.org/10.1145/3366423.3380054
2020, Khoo, Ling Min Serena, Hai Leong Chieu, Zhong Qian, and Jing Jiang. "Interpretable Rumor Detection in Microblogs by Attending to User Interactions." AAAI (2020).
2020, N Rosenfeld, A Szanto, DC Parkes, A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone - Proceedings of The Web Conference 2020, 2020
2020,Rakesh Bal, Sayan Sinha, Swastika Dutta, Risabh Joshi, Sayan Ghosh, Ritam Dutt Analysing the Extent of Misinformation in Cancer Related Tweets, ICWSM, 2020
2020, Lia Bozarth, Aparajita Saraf, Ceren Budak, Higher Ground? How Groundtruth Labeling Impacts Our Understanding of Fake News about the 2016 U.S. Presidential Nominees, ICWSM, 2020
2020, Lia Bozarth, Ceren Budak, Toward a Better Performance Evaluation Framework for Fake News Classification, ICWSM, 2020
2020, Kai Shu, Deepak Mahudeswaran, Suhang Wang, Huan Liu, Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation, ICWSM, 2020
2020, Allen, Jennifer, Baird Howland, Markus Mobius, David Rothschild, and Duncan J. Watts. "Evaluating the fake news problem at the scale of the information ecosystem." Science Advances 6, no. 14 (2020): eaay3539.
2019, Michela Del Vicario, Walter Quattrociocchi, Antonio Scala, and Fabiana Zollo. 2019. Polarization and Fake News: Early Warning of Potential Misinformation Targets. ACM Trans. Web 13, 2, Article 10 (March 2019), 22 pages. https://doi.org/10.1145/3316809
2019, Dietram A. Scheufele, Nicole M. Krause, Science audiences, misinformation, and fake news Proceedings of the National Academy of Sciences Apr 2019, 116 (16) 7662-7669; DOI: 10.1073/pnas.1805871115
2019, Aral, Sinan, and Dean Eckles. "Protecting elections from social media manipulation." Science 365, no. 6456 (2019): 858-861.
2019, Horne, B. D., Nørregaard, J., & Adalı, S. (2019). Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 257-266.
2019, Santia, G. C., Mujib, M. I., & Williams, J. R. (2019). Detecting Social Bots on Facebook in an Information Veracity Context. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 463-472
2019, Jiang, S., Robertson, R. E., & Wilson, C. (2019). Bias Misperceived:The Role of Partisanship and Misinformation in YouTube Comment Moderation. Proceedings of the International AAAI Conference on Web and Social Media, 13(01), 278-289.
2019, Reza Zafarani, Xinyi Zhou, Kai Shu, and Huan Liu. 2019. Fake News Research: Theories, Detection Strategies, and Open Problems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '19). ACM, New York, NY, USA, 3207-3208. DOI: https://doi.org/10.1145/3292500.3332287
2019, Sumeet Kumar, and Kathleen M. Carley Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5047–5058, Florence, Italy, July 28 - August 2, 2019.
2019, Quanzhi Li, Qiong Zhang, and Luo Si Rumor Detection By Exploiting User Credibility Information, Attention and Multi-task Learning, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1173–1179, Florence, Italy, July 28 - August 2, 2019
2019, Dhruv Khattar, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. MVAE: Multimodal Variational Autoencoder for Fake News Detection. In The World Wide Web Conference (WWW '19), Ling Liu and Ryen White (Eds.). ACM, New York, NY, USA, 2915-2921. DOI: https://doi.org/10.1145/3308558.3313552
2019, Ceren Budak. 2019. What happened? The Spread of Fake News Publisher Content During the 2016 U.S. Presidential Election. In The World Wide Web Conference (WWW '19), Ling Liu and Ryen White (Eds.). ACM, New York, NY, USA, 139-150. DOI: https://doi.org/10.1145/3308558.3313721
2019, Qiang Zhang, Aldo Lipani, Shangsong Liang, and Emine Yilmaz. 2019. Reply-Aided Detection of Misinformation via Bayesian Deep Learning. In The World Wide Web Conference (WWW '19), Ling Liu and Ryen White (Eds.). ACM, New York, NY, USA, 2333-2343. DOI: https://doi.org/10.1145/3308558.3313718
2019, Kai Shu, Suhang Wang, and Huan Liu. 2019. Beyond News Contents: The Role of Social Context for Fake News Detection. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19). ACM, New York, NY, USA, 312-320. DOI: https://doi.org/10.1145/3289600.3290994
2019, Ruths D. The misinformation machine. Science. 2019 Jan 25;363(6425):348-.
2019, Nir Grinberg, Kenneth Joseph, Lisa Friedland1, Briony Swire-Thompson, David Lazer, Fake news on Twitter during the 2016 U.S. presidential election, Science, http://science.sciencemag.org/content/363/6425/374
2019, Bovet, Alexandre, and Hernán A. Makse. "Influence of fake news in Twitter during the 2016 US presidential election." Nature communications 10, no. 1 (2019): 7.https://www.nature.com/articles/s41467-018-07761-2.pdf
2018, Babcock, Matthew and Cox, Ramon Alfonso Villa and Kumar, Sumeet, Diffusion of pro- and anti-false information tweets: the Black Panther movie case, Jouranl of Computational and Mathematical Organization Theory, Nov, 2018, DOI: https://doi.org/10.1007/s10588-018-09286-x
2018, Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter. 2018. Detection and Resolution of Rumours in Social Media: A Survey. ACM Comput. Surv. 51, 2, Article 32 (February 2018), 36 pages. DOI: https://doi.org/10.1145/3161603
2018, Jingbo Shang, Jiaming Shen, Tianhang Sun, Xingbang Liu, Anja Gruenheid, Flip Korn, Adam D. Lelkes, Cong Yu, and Jiawei Han. 2018. Investigating Rumor News Using Agreement-Aware Search. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM '18). ACM, New York, NY, USA, 2117-2125. DOI: https://doi.org/10.1145/3269206.3272020
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, Adam Fourney, Miklos Z. Racz, Gireeja Ranade, Markus Mobius, and Eric Horvitz. 2017. Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM '17). ACM, New York, NY, USA, 2071-2074. DOI: https://doi.org/10.1145/3132847.3133147
2017, Rashkin, Hannah, et al. "Truth of varying shades: Analyzing language in fake news and political fact-checking." Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017.
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"
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, Sampson, Justin, Fred Morstatter, Liang Wu, and Huan 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, pp. 2377-2382. ACM, 2016.
2016, Ma, Jing, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. "Detecting Rumors from Microblogs with Recurrent Neural Networks." In IJCAI, pp. 3818-3824. 2016.
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, Liu, Xiaomo, Armineh Nourbakhsh, Quanzhi Li, Rui Fang, and Sameena Shah. "Real-time rumor debunking on twitter." In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1867-1870. ACM, 2015.
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.
[Pheme Project](https://www.pheme.eu/software-downloads/ [Pheme Project)
Analysis of fake news dataset with Machine Learning
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