Using Back-Propagation based Highlights as an Explanation in Human Fake News Detection
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2020-11-01
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en
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Abstract
Few clicks and one can publish a piece of text online, and such ease has
allowed Fake news to spread quickly. It can wreak havoc, even to the extent of
wiping billions of dollars from the stock market or instigating the lynching of
innocent people. Past research shows that humans are marginally slightly better
than chance at detecting Fake news. Therefore, researchers are actively exploring
the prospect of using using black-box models to detect Fake news, but this could
result in unnecessary censorship as we cannot directly interrogate a black-box
model to ask why it made a particular decision. There are existing techniques
that allow inspecting the output of the model, but little or no research has been
done on their applicability. Therefore, this thesis examines the usability of
explanation developed by O’brien, Latessa, Evangelopoulos, and Boix (2018) with
a between-subject experiment where average accuracy of fake news detection was
compared between groups that received such explanations against group that did
not receive any explanations. The result shows that providing any explanation
improves the mean accuracy but there is no statistically significant difference in
accuracy between groups that received explanations and groups that did not
receive any explanations.
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Faculteit der Sociale Wetenschappen