Abstract
Social recommender systems, such as “Who to follow” on Twitter, utilize approaches that recommend friends of a friend or interest-wise similar people. Such algorithmic approaches have been criticized for resulting in filter bubbles and echo chambers, calling for diversity-enhancing recommendation strategies. Consequently, this article proposes a social diversification strategy for recommending potentially relevant people based on three structural positions in egocentric networks: dormant ties, mentions of mentions, and community membership. In addition to describing our analytical approach, we report an experiment with 39 Twitter users who evaluated 72 recommendations from each proposed network structural position altogether. The users were able to identify relevant connections from all recommendation groups. Yet, perceived familiarity had a strong effect on perceptions of relevance and willingness to follow-up on the recommendations. The proposed strategy contributes to the design of a people recommender system, which exposes users to diverse recommendations and facilitates new social ties in online social networks. In addition, we advance user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations.
Highlights
Social media and social networking services such as Twitter are widely used in professional cooperation within and across organizations, helping to gain new insights and share knowledge. e functionality of recommending new connections is essential for expanding the social network and introducing new professional ties
Such people recommender systems represent the areas of social computing and social matching [1], which are argued to require careful design of the algorithmic principles [2]. us, people recommenders aim at influencing followership by suggesting seemingly suitable others based on user modeling and predictive analytics
E majority of existing approaches tend to support homophily bias [3]—a tendency of preferring others with similar characteristics as oneself, focusing on similarities in user-created content [4]. Another commonly used principle is the triadic closure [5] in the followership networks [6] that focuses on friend-of-a-friend connections
Summary
Social media and social networking services such as Twitter are widely used in professional cooperation within and across organizations, helping to gain new insights and share knowledge. e functionality of recommending new connections is essential for expanding the social network and introducing new professional ties. E functionality of recommending new connections is essential for expanding the social network and introducing new professional ties Such people recommender systems represent the areas of social computing and social matching [1], which are argued to require careful design of the algorithmic principles [2]. E majority of existing approaches tend to support homophily bias [3]—a tendency of preferring others with similar characteristics as oneself, focusing on similarities in user-created content [4] Another commonly used principle is the triadic closure [5] in the followership networks [6] that focuses on friend-of-a-friend connections. An important goal has been set to increase diversity in the recommendations [9, 10], potentially decreasing human and algorithmic biases [11] Our work highlights this goal toward diversification and heterogeneity, especially in the professional networking context where
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