A recent article in Science by Guess et al. estimated the effect of Facebook's news feed algorithm on exposure to misinformation and political information among Facebook users. However, its reporting and conclusions did not account for a series of temporary emergency changes to Facebook's news feed algorithm in the wake of the 2020 U.S. presidential election that were designed to diminish the spread of voter-fraud misinformation. Here, we demonstrate that these emergency measures systematically reduced the amount of misinformation in the control group of the study, which was using the news feed algorithm. This issue may have led readers to misinterpret the results of the study and to conclude that the Facebook news feed algorithm used outside of the study period mitigates political misinformation as compared to reverse chronological feed.
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Generating Location-Based News Leads for National Politics Reporting
Computational news discovery refers to the use of algorithms to orient editorial attention to potentially newsworthy events or information prior to publication. In this paper we describe the design, development, and initial evaluation of a computational news discovery tool, called Lead Locator, which is geared towards supplementing national politics reporting by suggesting potentially interesting locations to report on. Based on massive amounts of data from a national voter file, Lead Locator ranks counties based on statistical properties such as their extremity in the distribution of a variable of interest (e.g. voter turnout) as well as their political relevance in terms of shifts in voting patterns. It then presents an automatically generated tip sheet of potentially interesting locations that reporters can interactively browse and search to help inform their reporting ideas.
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- Award ID(s):
- 1845460
- PAR ID:
- 10206645
- Date Published:
- Journal Name:
- Proc Computational + Journalism Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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