Fake News Detection in Social Networks via Crowd Signals
| Autor: | Andreas Krause, Sebastian Tschiatschek, Arpit Merchant, Manuel Gomez Rodriguez, Adish Singla |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Information retrieval Social network Computer science business.industry Computer Science - Social and Information Networks 02 engineering and technology Bayesian inference Value of information Order (exchange) 020204 information systems 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Misinformation Fake news business |
| Zdroj: | WWW (Companion Volume) |
| DOI: | 10.1145/3184558.3188722 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|