A discussion group is a repeated, synchronized conversation organized around a specific topic. Groups are extremely valuable to the attendees, creating a sense of community among like-minded users. While groups may involve many users, there are many outside the group that would benefit from participation. However, finding the right group is not easy given their quantity and given topic overlap. We study the following problem: given a search query, find a good ranking of discussion groups. We describe a random walk model for how users select groups: starting with a group relevant to the query, a hypothetical user repeatedly selects an authoritative user in the group and then moves to a group according to what the authoritative user prefers. The stationary distribution of this walk yields a group ranking. We analyze this random walk model, demonstrating that it enjoys many natural properties of a desirable ranking algorithm. We study groups on Twitter where conversations can be organized via pre-designated hashtags. These groups are an emerging phenomenon and there are at least tens of thousands in existence today according to our calculations. Via an extensive collection of experiments on one year of tweets, we show that our model effectively ranks groups, outperforming several baseline solutions.Source: Microsoft Research
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