Investigating the Effect of Recommender System Algorithms on the Amplification of Disinformation
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2022-07-09
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en
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Abstract
Recommender systems are used by many services that everyone encounters
in their daily life. These include platforms such as Google, Netflix,
TikTok etc. These platforms use recommender systems to recommend the
items to users that it expects the user will prefer. They often allow its
users to add their own items to the input of its recommender system e.g.
by posting a video on TikTok. This makes these platforms vulnerable to
disinformation. Disinformation is information that is deliberately created
to mislead its target audience [3]. A person with malicious intent could use
those recommender systems to spread disinformation. This can be done by
adding an item that was created with the intention to mislead the users of
the recommender system. This thesis will investigate the amplification of
disinformation in recommender systems. This project will focus on several
issues that might influence the amount of disinformation that is recommended.
Such as a comparison of various recommender system algorithms,
changes in the popularity of fake items and changes in the characteristics
of the input data. This will be done by creating a simulation of the spread
of disinformation. In this project fake items will be created and added to
an original dataset. These fake items will represent disinformation. This
is then used as the input for several recommender systems. The resulting
recommendations will then be used to determine if there were fake items
recommended and if so how many. This could give some insight into how
disinformation and its characteristics affect the recommendations that are
produced by recommender systems.
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Faculteit der Sociale Wetenschappen