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arXiv · 1702.06492

Automated Assistants to Identify and Prompt Action on Visual News Bias

Vishwajeet Narwal·Mohamed Hashim Salih·Jose Angel Lopez·Angel Ortega·John O'Donovan·Tobias Höllerer·Saiph Savage
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Abstract

Bias is a common problem in today's media, appearing frequently in text and in visual imagery. Users on social media websites such as Twitter need better methods for identifying bias. Additionally, activists --those who are motivated to effect change related to some topic, need better methods to identify and counteract bias that is contrary to their mission. With both of these use cases in mind, in this paper we propose a novel tool called UnbiasedCrowd that supports identification of, and action on bias in visual news media. In particular, it addresses the following key challenges (1) identification of bias; (2) aggregation and presentation of evidence to users; (3) enabling activists to inform the public of bias and take action by engaging people in conversation with bots. We describe a preliminary study on the Twitter platform that explores the impressions that activists had of our tool, and how people reacted and engaged with online bots that exposed visual bias. We conclude by discussing design and implication of our findings for creating future systems to identify and counteract the effects of news bias.

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Vishwajeet Narwal, Mohamed Hashim Salih, Jose Angel Lopez, Angel Ortega, John O'Donovan, Tobias Höllerer, Saiph Savage. 2017-02-21. Automated Assistants to Identify and Prompt Action on Visual News Bias. https://doi.org/10.1145/3027063.3053227

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