Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users' experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms' existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation algorithm's existence at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends' decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm's presence, however, algorithmic awareness led to more active engagement with Facebook and bolstered overall feelings of control on the site.

No abstract

Abstract. Managing friendship relationships in social media is challenging due to the growing number of people in online social networks (OSNs). To deal with this challenge, OSNs' users may rely on manually grouping friends with personally meaningful labels. However, manual grouping can become burdensome when users have to create multiple groups for various purposes such as privacy control, selective sharing, and filtering of content. More recently, recommendation-based grouping tools such as Facebook smart lists have been proposed to address this concern. In these tools, users must verify every single friend suggestion. This can hinder users' adoption when creating large content sharing groups. In this paper, we proposed an automated friend grouping tool that applies three clustering algorithms on a Facebook friendship network to create groups of friends. Our goal was to uncover which algorithms were better suited for social network groupings and how these algorithms could be integrated into a grouping interface. In a series of semi-structured interviews, we asked people to evaluate and modify the groupings created by each algorithm in our interface. We observed an overwhelming consensus among the participants in preferring this automated grouping approach to existing recommendation-based techniques such as Facebook smart lists. We also discovered that the automation created a significant bias in the final modified groups. Finally, we found that existing group scoring metrics do not translate well to OSN groupings-new metrics are needed. Based on these findings, we conclude with several design recommendations to improve automated friend grouping approaches in OSNs.

Managing friendship relationships is challenging due to the growing number of people in online social networks (OSNs). While grouping friends sometimes mitigates this challenge, the burden of manual grouping still prevents OSNs users to create groups widely for privacy control, selective sharing and filtering. In this paper, we present an automated friend grouping tool which utilizes three different clustering algorithms to create groups from Facebook friendship networks. By conducting 18 semi-structured interviews, we investigated the advantages and disadvantages of automated friend grouping in OSNs.

Social media feeds, personalized search results and recommendations are examples of algorithmically curated content in our daily digital Life. While the algorithms that curated this content have great power to shape users' experiences, they are mostly hidden behind the interface, leaving users unaware of their presence. Whether it is helpful to give users knowledge of the algorithms' existence and if this knowledge affects interaction behavior are open questions. To assist us in addressing these questions, we developed a system, FeedVis, that exposes Facebook users to comparisons between algorithmically curated and unadulterated News Feeds. We used the tools visualizations as concrete artifacts to study users' perceptions of the algorithms governing their social media feeds.