Abstract
We are currently witnessing an information explosion with aid of many micro-blogging toolkits like the Twitter. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time, it is often hard to understand what these trending topics are about where most of these trending topics are far away from the personal preferences of the twitter user. In this article, we pay attention to the issue of personalizing the search for trending topics via enabling the twitter user to provide RSS feeds that include the personal preferences along with a twitter client that can filter personalized tweets and trending topics according to a sound algorithm for capturing the trending information. The algorithms used are the Latent Dirichlet allocation (LDA) along with the Levenshtein Distance. Our experimentations show that the developed prototype for personalized trending topics (T3C) finds more interesting trending topics that match the Twitter user list of preferences than traditional techniques without RSS personalization.
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