Journalists, fact-checkers, academics, and community media are overwhelmed in their attempts to support communities suffering from gender-, race- and ethnicity-targeted information ecosystem threats, including but not limited to misinformation, hate speech, weaponized controversy and online-to-offline harassment. Yet, for a plethora of reasons, minoritized groups are underserved by current approaches to combat such threats. In this panel, we will present and discuss the challenges and open problems facing such communities and the researchers hoping to serve them. We will also discuss the current state-of-the-art as well as the most promising future directions, both within IR specifically, across Computer Science more broadly, as well as that requiring transdisciplinary and cross-sectoral collaborations. The panel will attract both IR practitioners and researchers and include at least one panelist outside of IR, with unique expertise in this space.
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Bridging Contextual and Methodological Gaps on the “Misinformation Beat”: Insights from Journalist-Researcher Collaborations at Speed
As misinformation, disinformation, and conspiracy theories increase online, so does journalism coverage of these topics. This reporting is challenging, and journalists fill gaps in their expertise by utilizing external resources, including academic researchers. This paper discusses how journalists work with researchers to report on online misinformation. Through an ethnographic study of thirty collaborations, including participant-observation and interviews with journalists and researchers, we identify five types of collaborations and describe what motivates journalists to reach out to researchers — from a lack of access to data to support for understanding misinformation context. We highlight challenges within these collaborations, including misalignment in professional work practices, ethical guidelines, and reward structures. We end with a call to action for CHI researchers to attend to this intersection, develop ethical guidelines around supporting journalists with data at speed, and offer practical approaches for researchers filling a “data mediator” role between social media and journalists.
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- Award ID(s):
- 1749815
- NSF-PAR ID:
- 10330498
- Date Published:
- Journal Name:
- CHI '22: CHI Conference on Human Factors in Computing Systems
- Volume:
- 2022
- Page Range / eLocation ID:
- Article 244: 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3491102.3517503
