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| − | ==Fake news Challenge==
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| − | http://www.fakenewschallenge.org/
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| − | Exploring how artificial intelligence technologies could be leveraged to combat fake news.
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| − | ===Formal Definition===
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| − | * '''Input:''' A headline and a body text - either from the same news article or from two different articles.
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| − | * '''Output:''' Classify the stance of the body text relative to the claim made in the headline into one of four categories:
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| − | ** Agrees: The body text agrees with the headline.
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| − | ** Disagrees: The body text disagrees with the headline.
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| − | ** Discusses: The body text discuss the same topic as the headline, but does not take a position
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| − | ** Unrelated: The body text discusses a different topic than the headline
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| − | ===Stance Detection dataset for FNC1===
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| − | https://github.com/FakeNewsChallenge/fnc-1
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| − | ===Winner team===
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| − |
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| − | ====First place - Team SOLAT in the SWEN====
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| − | https://github.com/Cisco-Talos/fnc-1
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| − | The data provided is (headline, body, stance) instances, where stance is one of {unrelated, discuss, agree, disagree}. The dataset is provided as two CSVs:
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| − | *'''train_bodies.csv''' : This file contains the body text of articles (the articleBody column) with corresponding IDs (Body ID)
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| − |
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| − | *'''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).
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| − |
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| − | <br />
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| − | ===Distribution of the data===
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| − | The distribution of <code>Stance</code> classes in <code>train_stances.csv</code> is as follows:
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| − | {| class="wikitable"
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| − | !rows
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| − | !unrelated
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| − | !discuss
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| − | !agree
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| − | !disagree
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| − | |-
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| − | |49972
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| − | |0.73131
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| − | |0.17828
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| − | |0.0736012
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| − | |0.0168094
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| − | |}
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