257 papers with code • 1 benchmarks • 38 datasets

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Most implemented papers

A simple but tough-to-beat baseline for the Fake News Challenge stance detection task

uclmr/fakenewschallenge 11 Jul 2017

Identifying public misinformation is a complicated and challenging task.

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

cair/TsetlinMachine LREC 2022

The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.

COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations

Komal7209/BackUp-Twitter-Sentiment-Analysis- 26 Mar 2020

The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.

Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models

LiamMaclean216/Pytorch-Transfomer 7 Sep 2020

While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic.

Evidence-based Factual Error Correction

j6mes/2021-acl-factual-error-correction 31 Dec 2020

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.

COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning

shivangi-aneja/COSMOS 15 Jan 2021

We propose a self-supervised training strategy where we only need a set of captioned images.

MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset

MuMiN-dataset/mumin-build 23 Feb 2022

Training these machine learning models require datasets of sufficient scale, diversity and quality.

Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls

fatimaezzedinee/exposing-influence-campaigns-in-the-age-of-llms-a-behavioral-based-ai-approach 17 Oct 2022

The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm.

CSI: A Hybrid Deep Model for Fake News Detection

sungyongs/CSI-Code 20 Mar 2017

Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.

DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning

atulkumarin/DeClare EMNLP 2018

Misinformation such as fake news is one of the big challenges of our society.