Utilizing Structural Network Positions to Diversify People Recommendations on Twitter

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.

Automated summary

This article proposes a social diversification strategy for recommending potentially relevant people based on structural positions in egocentric networks. The strategy is experimentally validated and found to be effective in improving diversity and social connections. The article also advances user-centered evaluation methods by proposing measures for subjective perceptions of people recommendations.