ausblenden:
Schlagwörter:
cs.SI
Zusammenfassung:
Our work considers leveraging crowd signals for detecting fake news and is
motivated by tools recently introduced by Facebook that enable users to flag
fake news. By aggregating users' flags, our goal is to select a small subset of
news every day, send them to an expert (e.g., via a third-party fact-checking
organization), and stop the spread of news identified as fake by an expert. The
main objective of our work is to minimize \emph{the spread of misinformation}
by stopping the propagation of fake news in the network. It is especially
challenging to achieve this objective as it requires detecting fake news with
high-confidence as quickly as possible. We show that in order to leverage
users' flags efficiently, it is crucial to learn about users' flagging
accuracy. We develop a novel algorithm, \algo, that performs Bayesian inference
for detecting fake news and jointly learns about users' flagging accuracy over
time. Our algorithm employs posterior sampling to actively trade off
exploitation (selecting news that directly maximize the objective value at a
given epoch) and exploration (selecting news that maximize the value of
information towards learning about users' flagging accuracy). We demonstrate
the effectiveness of our approach via extensive experiments and show the power
of leveraging community signals.
