Speak little and well: recommending conversations in online social streams

Authors:
Jilin Chen;Rowan Nairn;Ed Chi
Affiliations:
University of Minnesota, Minneapolis, USA;Palo Alto Research Center (PARC), Palo Alto, USA;Palo Alto Research Center (PARC), Palo Alto, USA
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2011

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Abstract

Conversation is a key element in online social streams such as Twitter and Facebook. However, finding interesting conversations to read is often a challenge, due to information overload and differing user preferences. In this work we explored five algorithms that recommend conversations to Twitter users, utilizing thread length, topic and tie-strength as factors. We compared the algorithms through an online user study and gathered feedback from real Twitter users. In particular, we investigated how users' purposes of using Twitter affect user preferences for different types of conversations and the performance of different algorithms. Compared to a random baseline, all algorithms recommended more interesting conversations. Further, tie-strength based algorithms performed significantly better for people who use Twitter for social purposes than for people who use Twitter for informational purpose only.