- Fact Checking from Text Xin Luna Dong, Christos Faloutsos, Xian Li, Subhabrata Mukherjee, Prashant Shiralkar
- Are You Fake News? A realtime media bias meter - Zach Estela, Data Scientist
- Detection and Resolution of Rumors and Misinformation with NLP
- The Duke Reporters’ Lab - Maintains a database of global fact-checking sites
- List of fake news websites - Wikipedia
- Top 100 World News Websites & Influencers in 2021 (feedspot.com) - Reliable news site
- List of satirical news websites - Wikipedia - list of satire news
- Wikipedia:Reliable sources/Perennial sources - Wikipedia
- Questionable Sources - Media Bias Fact Check
- False, Misleading, Clickbait-y, and Satirical “News” Sources - Opensource.co (Google Docs)
- zivepstein/fake-news-list (github.com)
- projectnews/fake-news-sites-list (github.com) - Fake news site ( Exactly similar to opensource ) but provided in csv format
- Aloisius/fake-news: Lists of fake news sites (github.com) - Fake news list in text file ( need to explore )
- OpenSourcesGroup/opensources: Curated lists of credible and non-credible online sources, available for public use (github.com)
- Top 50 Online News Entities - TOP ONLINE NEWS SITES - Washington and Lee University LibGuides at Washington and Lee University (wlu.edu)
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MieszkoMakuch/fakenews-detector: University project: News article and domain analyzer written in python 3 to help user detect fake news content. (github.com) - This repository is used only for getting the fake-news site information. further, we added fake and reliable news sites from Wikipedia and fact-checker websites.
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Veronica Perez-Rosas, Bennett Kleinberg, Alexandra Lefevre1, Rada Mihalcea1, “Automatic Detection of Fake News”, Department of Psychology, University of Amsterdam, 2017 - 1708.07104.pdf (arxiv.org).
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Ray Oshikawa, Jing Qian, William Yang Wang, “A Survey on Natural Language Processing for Fake News Detection” , Department of Computer Science, University of California, Santa Barbara, 2020 - 1811.00770.pdf (arxiv.org)
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William Yang Wang, ““Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection”, University of California, Santa Barbara, 2017 - 1705.00648.pdf (arxiv.org).
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Isabelle Augenstein Christina Lioma Dongsheng Wang et al.,"MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims", University of Copenhagen, 2019 - 1909.03242.pdf (arxiv.org).
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Saarthak Sangamnerkar, R. Srinivasan, Christhuraj M.R, Rajeev Sukumaran "An Ensemble Technique to Detect Fabricated News Article Using Machine Learning and Natural”, Indian Institute of Technology, Chennai, India, 2020 - An Ensemble Technique to Detect Fabricated News Article Using Machine Learning and Natural Language Processing Techniques (sci-hub.do).
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Natali Ruchansky, Sungyong Seo, Yan Liu "CSI: A Hybrid Deep Model for Fake News Detection", University of Southern California, 2017 -https://dl.acm.org/doi/pdf/10.1145/3132847.3132877.
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Benjamin D. Horne and Sibel Adal, "This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News", Rensselaer Polytechnic Institute, 2017 - https://arxiv.org/pdf/1703.09398.pdf.
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XINYI ZHOU, REZA ZAFARANI, "A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities", Syracuse University, 2020 - https://arxiv.org/pdf/1812.00315.pdf.
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Sahil Chopra, Saachi Jain, and John Merriman Sholar. "Towards Automatic Identification of Fake News: Headline Article Stance Detection with LSTM Attention Models", 2017 - https://johnsholar.com/pdf/CS224NPaper.pdf.
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Hammed Ali and Timothy Levine. , “The language of truthful and deceptive denials and confessions. Communication Reports” 21, 2 (2008), - https://www.researchgate.net/publication/233456318_The_Language_of_Truthful_and_Deceptive_Denials_and_Confessions.
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Jiawei Zhang, Bowen Dong, Philip S. Yu "FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network", 2019, https://arxiv.org/pdf/1805.08751.pdf.
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Simon Lorent, Ashwin Itoo "Fake News Detection Using Machine Learning", 2019- https://matheo.uliege.be/bitstream/2268.2/8416/1/s134450_fake_news_detection_using_machine_learning.pdf .
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Gisel Bastidas Guacho, Sara Abdali, Neil Shah, Evangelos E. Papalexakis, "Semi-supervised content-based detection of misinformation via tensor embeddings", UC Riverside,2018 : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8508241 .
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Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee and Huan Liu "FakeNewsNet: A data repository with news content,social context and dynamic information for studying fake news on social media", Arizona and Penn State University, 2018 - https://arxiv.org/pdf/1809.01286.pdf .
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Changhe Song, Cunchao Tu, Cheng Yang, Zhiyuan Liu, Maosong Sun, "Credible early detection of social media rumors", Tsinghua University, 2018, https://arxiv.org/abs/1811.04175 .
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Feng Yu1, Qiang Liu1, Shu Wu, Liang Wang, Tieniu Tan, "A convolutional approach for misinformation identification", University of Chinese Academy of Sciences, 2017, https://www.ijcai.org/proceedings/2017/0545.pdf .
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Victoria L. Rubin, Yimin Chen and Niall J. Conroy , "Automatic Deception Detection: Methods for Finding Fake News", University of Western Ontario, 2016, https://asistdl.onlinelibrary.wiley.com/doi/epdf/10.1002/pra2.2015.145052010082.
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Rohit Kumar Kaliyar, "Fake News Detection Using A Deep Neural Network", Bennett University, 2018, https://ieeexplore.ieee.org/document/8777343 .
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Viveksinh Solanki, "Fake News Detection: Using An Ensemble Approach", Stevens Institute Of Technology", 2019, https://viveksolanki.com/data/ML_project_report_spring2019_viveksinh.pdf.
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Waleed Ahmed, Saif Ali Khan, Haris Jamil, Mansoor Naseer, "Automatic Fake News Detection System", Ghulam Ishaq Khan Institute of Engineering Sciences andTechnology - https://www.scribd.com/document/384424621/Fake-News-Detection.
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Nicole O’Brien, "Machine Learning for Detection of Fake News", Massachusetts Institute of Technology, 2018 - https://dspace.mit.edu/bitstream/handle/1721.1/119727/1078649610-MIT.pdf.
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Ayush Gupta, Rohan Sukumaran, Kevin John, and Sundeep Teki, "Hostility Detection and Covid-19 Fake News Detection in Social Media", Indian Institute of Information Technology, Sri City, India, 2021 - https://arxiv.org/pdf/2101.05953.pdf.
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Apurva Wani, Isha Joshi, Snehal Khandve, Vedangi Wagh, andRaviraj Joshi, "Evaluating Deep Learning Approaches for Covid19 Fake News Detection",Pune Institute of Computer Technology, Pune, Indian Institute of Technology Madras, Chennai, 2021 - https://arxiv.org/pdf/2101.04012.pdf.
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Sunil Gundapu and Radhika Mamidi, "Transformer based Automatic COVID-19 Fake News Detection System", International Institute of Information Technology, Hyderabad, 2021 - https://arxiv.org/pdf/2101.00180.pdf.
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Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova,"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", Google AI Language, 2019 - https://arxiv.org/pdf/1810.04805.pdf
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Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V, "RoBERTa: A Robustly Optimized BERT Pretraining Approach", 2019 - ]https://arxiv.org/pdf/1907.11692](https://arxiv.org/pdf/1907.11692)
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Jeffrey Pennington, Richard Socher, Christopher D. Manning GloVe: Global Vectors for Word Representation" Computer Science Department, Stanford University, Stanford, 2014 - https://nlp.stanford.edu/pubs/glove.pdf
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Victor Sanh, Lysandre Debut, Julien Chaumond, Thomas Wolf "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter", Hugging Face, 2020 - https://arxiv.org/pdf/1910.01108.pdf
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Valeriya Slovikovskaya, Giuseppe Attard, "Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task, University of Pisa, 2019 - https://arxiv.org/pdf/1910.14353
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Galbraith, B., Iqbal, H., van Veen, H., Rao, D., Thorne, J., and Pan, Y. (2016). "Baseline FNC implementation". https://github.com/FakeNewsChallenge/fnc-1-baseline.
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Yuxi Pan, Doug Sibley, Sean Baird, "Talos Targets Disinformation with Fake News Challenge Victory", https://blog.talosintelligence.com/2017/06/talos-fake-news-challenge.html
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A. Aziz Sharfuddin, M. Nafis Tihami and M. Saiful Islam, "A Deep Recurrent Neural Network with BiLSTM model for Sentiment Classification"** 2018 International Conference on Bangla Speech and Language Processing (ICBSLP), 2018, pp. 1-4, doi: 10.1109/ICB-SLP.2018.8554396.