Automated Assistants to Identify and Prompt Action on Visual News Bias
Open accessAbstract
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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The speed and volume at which news appears makes it difficult to scale manual fact-checking processes, and drives a need for automated support in verifying content.
“The speed and volume at which news appears makes it difficult to scale manual fact-checking processes, and drives a need for automated support in verifying content [7,22,25].”
From Falling for Fake News: Investigating the Consumption of News via Social Media (2018) · cites this paper as [22]
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Falling for Fake News: Investigating the Consumption of News via Social Media
Flintham, M., Karner, C., Bachour, K., Creswick, H., Gupta, N., & Moran, S. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173950 · Cited by 194 · see citing works
Epstein, Z., Pennycook, G., & Rand, D. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376232 · Cited by 67 · see citing works
- Authors
- Narwal, V., Salih, M. H., Lopez, J. A., Ortega, A., O'Donovan, J., Höllerer, T., & Savage, S.
- Affiliations
- West Virginia University (US)
- Universidad Nacional Autónoma de México (MX)
- University of California, Santa Barbara (US)
- Venue
- Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2017-05-06
- Pages
- 2796-2801
- OpenAlex
- W2592913799
- Cited by
- 16
- References
- 10
- Citations by year
- 2024: 1 · 2022: 2 · 2021: 2 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 1
- FWCI
- 9.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- J. Wayne and Kathy Richards Faculty Fellowship
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- media bias detection, Twitter, social bots, activism, evidence aggregation, user engagement, crowdsourcing
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