Counteracting filter bubbles with homophily-aware link recommendations
Counteracting filter bubbles with homophily-aware link recommendations
复制标题
通过同质感知链接推荐来消除过滤气泡
DOI：
10.1007/978-3-031-17114-7_15
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xu, Kevin S.
中科院分区：
文献类型：
--
作者：
Warton, Robert;Volny, Chris;Xu, Kevin S.
随着社交媒体上互动的流行，从这些网络中收集的数据非常适合分析社会趋势。本文旨在解决的一个趋势是政治同质化。对政治同质性的证据进行了充分的研究，表明人们有强烈的倾向与具有相似政治意识形态的人互动。此外，由于链接在社交网络中自然形成，无论是通过推荐还是间接互动，新的链接都很可能加强社区。这使得社交媒体更加孤立，最终更加两极分化。我们的目标是通过提供链接建议来解决这个问题，这将减少网络同质性。我们提出了几种基于共同邻居的链接预测算法的变体，其目的是向相似但也会减少同质性的用户推荐链接。我们证明，接受这些建议确实可以减少同质性的网络，而接受链接的建议，从一个标准的共同邻居算法不。
With the prevalence of interaction on social media, data compiled from these networks are perfect for analyzing social trends. One such trend that this paper aims to address is political homophily. Evidence of political homophily is well researched and indicates that people have a strong tendency to interact with others with similar political ideologies. Additionally, as links naturally form in a social network, either through recommendations or indirect interaction, new links are very likely to reinforce communities. This serves to make social media more insulated and ultimately more polarizing. We aim to address this problem by providing link recommendations that will reduce network homophily. We propose several variants of common neighbor-based link prediction algorithms that aim to recommend links to users who are similar but also would decrease homophily. We demonstrate that acceptance of these recommendations can indeed reduce the homophily of the network, whereas acceptance of link recommendations from a standard common neighbors algorithm does not.