Summary of Biasscanner: Automatic Detection and Classification Of News Bias to Strengthen Democracy, by Tim Menzner and Jochen L. Leidner
BiasScanner: Automatic Detection and Classification of News Bias to Strengthen Democracy
by Tim Menzner, Jochen L. Leidner
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The BiasScanner application is designed to help strengthen democracy by scrutinizing news articles online. It uses a server-side pre-trained large language model to identify biased sentences, and a front-end browser plug-in to highlight likely biased text. The system can classify over two dozen types of media bias at the sentence level, making it the most fine-grained model of its kind. BiasScanner also provides explanations for each classification decision and a summary analysis for each news article. This technology addresses the issue of disinformation, biased reporting, hate speech, and other unwanted Web content that has increased with online news consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BiasScanner is an app that helps people read news online by checking if what they’re reading is true or not. It uses a special computer program to find out if sentences in news articles are trying to be biased or unfair. The app can even explain why it thinks something is biased! It’s like having your own personal fact-checker. |
Keywords
» Artificial intelligence » Classification » Large language model
