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.

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.

Social tagging arose out of the need to organize found content that is worth revisiting. A significant side effect has been the use of social tagging sites as navigational signposts for interesting content. The collective behavior of users who tagged contents seems to offer a good basis for exploratory search interfaces, even for users who are not using social bookmarking sites. In this paper, we present the design of a tag-based exploratory system and detail an experiment in understanding its effectiveness. The tag-based search system allows users to utilize relevance feedback on tags to indicate their interest in various topics, enabling rapid exploration of the topic space. The experiment shows that the system seems to provide a kind of scaffold for users to learn new topics.