In this paper, we preliminarily learn the problem of reconstructing users' life history based on the their Twitter stream and proposed an unsupervised framework that create a chronological list for personal important events (PIE) of individuals. By analyzing individual tweet collections, we find that what are suitable for inclusion in the personal timeline should be tweets talking about personal (as opposed to public) and time-specific (as opposed to time-general) topics. To further extract these types of topics, we introduce a non-parametric multi-level Dirichlet Process model to recognize four types of tweets: personal time-specific (PersonTS), personal time-general (PersonTG), public time-specific (PublicTS) and public time-general (PublicTG) topics, which, in turn, are used for further personal event extraction and timeline generation. To the best of our knowledge, this is the first work focused on the generation of timeline for individuals from Twitter data. For evaluation, we have built gold standard timelines that contain PIE related events from 20 ordinary twitter users and 20 celebrities. Experimental results demonstrate that it is feasible to automatically extract chronological timelines for Twitter users from their tweet collection 1 .
There exists explorations REF for individual timeline from Twitter.
Timeline Generation: Tracking individuals on Twitter
ArXiv
ArXiv
Computer Science