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Multiple recent efforts have used large-scale data and computational models to automatically detect misinformation in online news articles. Given the potential impact of misinformation on democracy, many of these efforts have also used the political ideology of these articles to better model misinformation and study political bias in such algorithms. However, almost all such efforts have used source level labels for credibility and political alignment, thereby assigning the same credibility and political alignment label to all articles from the same source (e.g., the New York Times or Breitbart). Here, we report on the impact of journalistic best practices to label individual news articles for their credibility and political alignment. We found that while source level labels are decent proxies for political alignment labeling, they are very poor proxies-almost the same as flipping a coin-for credibility ratings. Next, we study the implications of such source level labeling on downstream processes such as the development of automated misinformation detection algorithms and political fairness audits therein. We find that the automated misinformation detection and fairness algorithms can be suitably revised to support their intended goals but might require different assumptions and methods than those which are appropriate using source level labeling. The results suggest caution in generalizing recent results on misinformation detection and political bias therein. On a positive note, this work shares a new dataset of journalistic quality individually labeled articles and an approach for misinformation detection and fairness audits.
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Social media algorithms can curb misinformation, but do they?
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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- Award ID(s):
- 2432052
- PAR ID:
- 10587827
- Publisher / Repository:
- AAAS
- Date Published:
- Journal Name:
- Science
- ISSN:
- 1095-9203
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
