No abstract

We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.

No abstract

In this paper, we present overview of our approach for clustering tweets. Due to short text of tweets, traditional text clustering mechanisms alone may not produce optimal results. We believe that there is an underlying theme/topic present in majority of tweets which is evident in growing usage of hashtag feature in the Twitter network. Clustering tweets based on these themes seems a more natural way for grouping. We propose to use Wikipedia topic taxonomy to discover the themes from the tweets and use the themes along with traditional word based similarity metric for clustering. We show some of our initial results to demonstrate the effectiveness of our approach.