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Fake news Challenge

http://www.fakenewschallenge.org/

Exploring how artificial intelligence technologies could be leveraged to combat fake news.

Stance Detection dataset for FNC1

https://github.com/FakeNewsChallenge/fnc-1

Winner team

First place - Team SOLAT in the SWEN

https://github.com/Cisco-Talos/fnc-1

The data provided is (headline, body, stance) instances, where stance is one of {unrelated, discuss, agree, disagree}. The dataset is provided as two CSVs:

• train_bodies.csv : This file contains the body text of articles (the articleBody column) with corresponding IDs (Body ID)

• train_stances.csv : This file contains the labeled stances (the Stance column) for pairs of article headlines (Headline) and article bodies (Body ID, referring to entries in train_bodies.csv).

The distribution of Stance classes in train_stances.csv is as follows:

rows unrelated discuss agree disagree 49972 0.73131 0.17828 0.0736012 0.0168094