Fake News Detection in Social Networks via Crowd Signals

TL;DR

This paper introduces DETECTIVE, a Bayesian algorithm that efficiently detects fake news in social networks by learning user flagging accuracy and actively balancing detection speed with information gain.

Contribution

The paper presents a novel Bayesian inference algorithm that jointly learns user flagging accuracy and detects fake news, improving detection speed and accuracy.

Findings

Effective detection of fake news using crowd signals

Joint learning of user accuracy enhances detection performance

Active exploration improves detection speed

Abstract

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 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, DETECTIVE, 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 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 for fake news detection.