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| DOI | 10.1140/epjds/s13688-024-00456-3 |
| Evaluating Twitter's algorithmic amplification of low-credibility content: an observational study | |
| Corsi, Giulio | |
| 发表日期 | 2024 |
| EISSN | 2193-1127 |
| 起始页码 | 13 |
| 结束页码 | 1 |
| 卷号 | 13期号:1 |
| 英文摘要 | Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of approximate to 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet's engagement level and a user's followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter's recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users. |
| 英文关键词 | Artificial intelligence; Twitter; Disinformation; Content recommendation |
| 语种 | 英语 |
| WOS研究方向 | Mathematics ; Mathematical Methods In Social Sciences |
| WOS类目 | Mathematics, Interdisciplinary Applications ; Social Sciences, Mathematical Methods |
| WOS记录号 | WOS:001176931800002 |
| 来源期刊 | EPJ DATA SCIENCE
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/300426 |
| 作者单位 | University of Cambridge |
| 推荐引用方式 GB/T 7714 | Corsi, Giulio. Evaluating Twitter's algorithmic amplification of low-credibility content: an observational study[J],2024,13(1). |
| APA | Corsi, Giulio.(2024).Evaluating Twitter's algorithmic amplification of low-credibility content: an observational study.EPJ DATA SCIENCE,13(1). |
| MLA | Corsi, Giulio."Evaluating Twitter's algorithmic amplification of low-credibility content: an observational study".EPJ DATA SCIENCE 13.1(2024). |
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