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Fake news Challenge

http://www.fakenewschallenge.org/

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

Formal Definition

• Input: A headline and a body text - either from the same news article or from two different articles.

• Output: Classify the stance of the body text relative to the claim made in the headline into one of four categories:
  • Agrees: The body text agrees with the headline.
  • Disagrees: The body text disagrees with the headline.
  • Discusses: The body text discuss the same topic as the headline, but does not take a position
  • Unrelated: The body text discusses a different topic than the headline

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).

Distribution of the data

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