推荐算法对错误信息放大的影响分析
Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation
摘要：
推荐算法已经被指出是数字领域错误信息传播的罪魁祸首之一。然而，目前还不清楚这些算法如何真正传播错误信息，例如，还没有示出哪些特定推荐方法更倾向于建议错误信息项，或者算法的哪些内部参数可能更多地影响它们的错误信息传播能力。出于这一事实，在本文中，我们提出了一些最流行的推荐算法在Twitter上传播的错误信息的效果进行分析。一组关于如何适应这些算法的指导方针提供了这样的分析和研究文献的全面审查的基础上。还生成了一个数据集并发布给科学界，以激发关于未来设计和开发推荐算法以对抗错误信息的讨论。该数据集包括编辑标记的新闻条目和关于其错误信息性质的声明。
英文摘要：
Recommendation algorithms have been pointed out as one of the major culprits of misinformation spreading in the digital sphere. However, it is still unclear how these algorithms really propagate misinformation, e.g., it has not been shown which particular recommendation approaches are more prone to suggest misinforming items, or which internal parameters of the algorithms could be influencing more on their misinformation propagation capacity. Motivated by this fact, in this paper we present an analysis of the effect of some of the most popular recommendation algorithms on the spread of misinformation in Twitter. A set of guidelines on how to adapt these algorithms is provided based on such analysis and a comprehensive review of the research literature. A dataset is also generated and released to the scientific community to stimulate discussions on the future design and development of recommendation algorithms to counter misinformation. The dataset includes editorially labelled news items and claims regarding their misinformation nature.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
