Counteracting Filter Bubbles with Homophily-Aware Link Recommendations

Robert Warton, Chris Volny, Kevin S. Xu

Abstract: 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.