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.

翻译：建议算法被指出为数字领域传播错误信息的主要罪魁祸首之一,然而,仍然不清楚这些算法如何真正传播错误信息,例如,没有说明哪些特定建议方法更倾向于建议错误信息项目,或者算法的内部参数对错误信息传播能力的影响会更大。基于这一事实,我们在本文件中分析了一些最受欢迎的建议算法对Twitter传播错误信息的影响。根据这种分析和对研究文献的全面审查,提供了一套关于如何调整这些算法的准则。还制作了一个数据集,并发给科学界,以激发关于未来设计和制定建议算法以抵制错误信息的讨论。数据集包括编辑上标记的新闻项目和关于其错误信息性质的主张。