1 research outputs found
Temporal Identification of Latent Communities on Twitter
User communities in social networks are usually identified by considering
explicit structural social connections between users. While such communities
can reveal important information about their members such as family or
friendship ties and geographical proximity, they do not necessarily succeed at
pulling like-minded users that share the same interests together. In this
paper, we are interested in identifying communities of users that share similar
topical interests over time, regardless of whether they are explicitly
connected to each other on the social network. More specifically, we tackle the
problem of identifying temporal topic-based communities from Twitter, i.e.,
communities of users who have similar temporal inclination towards the current
emerging topics on Twitter. We model each topic as a collection of highly
correlated semantic concepts observed in tweets and identify them by clustering
the time-series based representation of each concept built based on each
concept's observation frequency over time. Based on the identified emerging
topics in a given time period, we utilize multivariate time series analysis to
model the contributions of each user towards the identified topics, which
allows us to detect latent user communities. Through our experiments on Twitter
data, we demonstrate i) the effectiveness of our topic detection method to
detect real world topics and ii) the effectiveness of our approach compared to
well-established approaches for community detection.Comment: Submitted to WSDM 201