Newsalyze: enabling news consumers to understand media bias
| Autor: | Karsten Donnay, Felix Hamborg, Bela Gipp, Anastasia Zhukova |
|---|---|
| Přispěvatelé: | University of Zurich, Association for Computing Machinery, ACM |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science 11476 Digital Society Initiative 05 social sciences 0507 social and economic geography Word choice Advertising Media bias computer.software_genre 0506 political science News aggregator Computer Science - Computers and Society Core (game theory) Computers and Society (cs.CY) 320 Political science 050602 political science & public administration 2200 General Engineering 10113 Institute of Political Science Fake news Language model 050703 geography computer |
| DOI: | 10.5167/uzh-195485 |
| Popis: | News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view). |
| Databáze: | OpenAIRE |
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