miscellaneous-8

Misinformation

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in misinformation-8

Trend

Dataset

Best Model

Actions

NLP4IF-2021--Fighting the COVID-19 Infodemic

Libraries

i

Use these libraries to find misinformation-8 models and implementations

Datasets

Hateful Memes

LIAR

DFDC

Hateful Memes Challenge

CoAID

CLIMATE-FEVER

Subtasks

No subtasks available.

Most implemented papers

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

Isabelle Augenstein, Sebastian Riedel, Georgios P. Spithourakis, B. Riedel•Mon Jul 10 2017

This paper presents their stance detection system, which claimed third place in Stage 1 of the Fake News Challenge, and proposes it as the 'simple but tough-to-beat baseline' for the FakeNews Challenge stance detection task.

250

Content

FakeNewsNet

NELA-GT-2018

UPFD

COVID-19 Twitter Chatter Dataset

0

Paper Graph

Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment

Ole-Christoffer Granmo, Bimal Bhattarai, Lei Jiao•Tue May 18 2021

A novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine is proposed, utilizing the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text.

51 0

Paper Graph

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

Chuizheng Meng, Sungyong Seo, Yan Liu, Karishma Sharma, Sirisha Rambhatla•Wed Mar 25 2020

A dashboard to track misinformation on popular social media news sharing platform - Twitter and provides analysis of public sentiments on intervention policies such as "#socialdistancing" and "#workfromhome", and tracks topics, and emerging hashtags and sentiments over countries.

122 0

Paper Graph

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

Preslav Nakov, Ivan Koychev, Giovanni Da San Martino, Alex Nikolov•Sun Sep 06 2020

A model for detecting check-worthy tweets about COVID-19 is proposed, which combines deep contextualized text representations with modeling the social context of the tweet, which should be useful for future research.

22 0

Paper Graph

Evidence-based Factual Error Correction

Andreas Vlachos, James Thorne•Wed Dec 30 2020

This paper demonstrates that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction, and achieves better results than existing work which used a pointer copy network and gold evidence.

63 0

Paper Graph

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

M. Nießner, Shivangi Aneja, C. Bregler•Thu Dec 31 2020

A new method that automatically detects out-of-context image and text pairs to leverage the grounding of image with text to distinguish out- of-context scenarios that cannot be disambiguated with language alone is proposed.

61 0

Paper Graph

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

Ryan McConville, Dan Saattrup Smart•Tue Feb 22 2022

A data collection and linking system to build a public misinformation graph dataset (MuMiN), containing rich social media data spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages.

105 0

Paper Graph

Exposing influence campaigns in the age of LLMs: a behavioral-based AI approach to detecting state-sponsored trolls

Emilio Ferrara, Fatima Ezzeddine, Luca Luceri, Omran Ayoub, Ihab Sbeity, Gianluca Nogara, Silvia Giordano•Sun Oct 16 2022

A new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others is proposed, ensuring greater resilience in identifying influence campaigns.

27 0

Paper Graph

CSI: A Hybrid Deep Model for Fake News Detection

Sungyong Seo, Yan Liu, Natali Ruchansky•Sun Mar 19 2017

This work proposes a model called CSI which is composed of three modules: Capture, Score, and Integrate, and incorporates the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.

1010 0

Paper Graph

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

Andrew Yates, G. Weikum, Subhabrata Mukherjee, Kashyap Popat•Sun Sep 16 2018

A neural network model that judiciously aggregates signals from external evidence articles, the language of these articles and the trustworthiness of their sources is presented, which derives informative features for generating user-comprehensible explanations that makes the neural network predictions transparent to the end-user.

337 0

Paper Graph

Adding a benchmark result helps the community track progress.