Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment
Abstract Society often relies on social algorithms that adapt to human behavior. Yet scientists struggle to generalize the combined behavior of mutually-adapting humans and algorithms. This scientific challenge is a governance problem when algorithms amplify human responses to falsehoods. Could atte...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-38277-5 |
| _version_ | 1827895296485490688 |
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| author | J. Nathan Matias |
| author_facet | J. Nathan Matias |
| author_sort | J. Nathan Matias |
| collection | DOAJ |
| description | Abstract Society often relies on social algorithms that adapt to human behavior. Yet scientists struggle to generalize the combined behavior of mutually-adapting humans and algorithms. This scientific challenge is a governance problem when algorithms amplify human responses to falsehoods. Could attempts to influence humans have second-order effects on algorithms? Using a large-scale field experiment, I test if influencing readers to fact-check unreliable sources causes news aggregation algorithms to promote or lessen the visibility of those sources. Interventions encouraged readers to fact-check articles or fact-check and provide votes to the algorithm. Across 1104 discussions, these encouragements increased human fact-checking and reduced vote scores on average. The fact-checking condition also caused the algorithm to reduce the promotion of articles over time by as much as −25 rank positions on average, enough to remove an article from the front page. Overall, this study offers a path for the science of human-algorithm behavior by experimentally demonstrating how influencing collective human behavior can also influence algorithm behavior. |
| first_indexed | 2024-03-12T22:19:00Z |
| format | Article |
| id | doaj.art-9f5fa3409cc14f51b02bad62121ae283 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-12T22:19:00Z |
| publishDate | 2023-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9f5fa3409cc14f51b02bad62121ae2832023-07-23T11:10:37ZengNature PortfolioScientific Reports2045-23222023-07-011311910.1038/s41598-023-38277-5Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experimentJ. Nathan Matias0Department of Communication, Cornell UniversityAbstract Society often relies on social algorithms that adapt to human behavior. Yet scientists struggle to generalize the combined behavior of mutually-adapting humans and algorithms. This scientific challenge is a governance problem when algorithms amplify human responses to falsehoods. Could attempts to influence humans have second-order effects on algorithms? Using a large-scale field experiment, I test if influencing readers to fact-check unreliable sources causes news aggregation algorithms to promote or lessen the visibility of those sources. Interventions encouraged readers to fact-check articles or fact-check and provide votes to the algorithm. Across 1104 discussions, these encouragements increased human fact-checking and reduced vote scores on average. The fact-checking condition also caused the algorithm to reduce the promotion of articles over time by as much as −25 rank positions on average, enough to remove an article from the front page. Overall, this study offers a path for the science of human-algorithm behavior by experimentally demonstrating how influencing collective human behavior can also influence algorithm behavior.https://doi.org/10.1038/s41598-023-38277-5 |
| spellingShingle | J. Nathan Matias Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment Scientific Reports |
| title | Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment |
| title_full | Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment |
| title_fullStr | Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment |
| title_full_unstemmed | Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment |
| title_short | Influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact-check articles, in a field experiment |
| title_sort | influencing recommendation algorithms to reduce the spread of unreliable news by encouraging humans to fact check articles in a field experiment |
| url | https://doi.org/10.1038/s41598-023-38277-5 |
| work_keys_str_mv | AT jnathanmatias influencingrecommendationalgorithmstoreducethespreadofunreliablenewsbyencouraginghumanstofactcheckarticlesinafieldexperiment |