Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 2020
DOI: 10.1145/3383583.3398561
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Newsalyze: Enabling News Consumers to Understand Media Bias

Abstract: 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 cor… Show more

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Cited by 11 publications
(8 citation statements)
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“…We find that participants become aware of bias when presented with bias-aware visualizations (C3): visualizing annotations stemming from a manual content analysis are most, followed by highlighting targets as to their sentiment. In the future, we plan to devise individual visualization components that are either aware or agnostic of bias [16]. We also plan to check differences between asking for bias directly and indirectly.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…We find that participants become aware of bias when presented with bias-aware visualizations (C3): visualizing annotations stemming from a manual content analysis are most, followed by highlighting targets as to their sentiment. In the future, we plan to devise individual visualization components that are either aware or agnostic of bias [16]. We also plan to check differences between asking for bias directly and indirectly.…”
Section: Discussionmentioning
confidence: 99%
“…Variant 3 aims to represent what the state-of-the-art in automated bias identification is able to detect (cf. [15][16][17]), e.g., target-dependent sentiment classification, a basic yet effective way to catch the effects of biased coverage. Since we want to test the visualization effectiveness but not the underlying detection technique, the variant 3 instances also stem from a manual annotation with six coders.…”
Section: Methodsmentioning
confidence: 99%
“…Iyyer et al (2014) use a bag-ofwords and Logistic Regression system as well, but improve over this with a Recursive Neural Network setup, working on the Convote data set (Thomas et al, 2006) and the Ideological Book Corpus 6 . Hamborg et al (2020) use BERT for sentiment analysis after finding Named Entities first, in order to find descriptions of entities that suggest either a left-wing or a right-wing bias (e. g., using either "freedom fighters" or "terrorists" to denote the same target entity or group). Salminen et al (2020) work on hate speech classification.…”
Section: Modelsmentioning
confidence: 99%
“…Iyyer et al (2014) use a bag-ofwords and Logistic Regression system as well, but improve over this with a Recursive Neural Network setup, working on the Convote data set (Thomas et al, 2006) and the Ideological Book Corpus 6 . Hamborg et al (2020) use BERT for sentiment analysis after finding Named Entities first, in order to find descriptions of entities that suggest either a left-wing or a right-wing bias (e. g., using either "freedom fighters" or "terrorists" to denote the same target entity or group). Salminen et al (2020) work on hate speech classification.…”
Section: Modelsmentioning
confidence: 99%