Personalizing your social computing world: A case study using Twitter

Twitter portal has become one of the major sources of social networking. Everyday millions of people send tweets to share their interest and view what other people are showing interests on. People who subscribe to Twitter portal are often overwhelmed not only by the large number of tweets they receive, but also by the fact that a large number of those tweets are irrelevant to their interests. One way to address this is to look to personalize this interaction. In this paper we describe a Smart Tweet Portal (STP) that we have built using an Adaptive Information Retrieval technique to personalize itself to user's interest. This portal filters unwanted tweets and delivers only tweets of user's interests. It employs similar features like that of Twitter (followers, following and number of tweets) as well as some user actions (Retweet, Post, Download and Like). In this way users can create their own highly personal cut on their online social world around them. The portal was evaluated by 12 users. The evaluation showed 91.7% overall user satisfaction for the relevancy of tweets and for displaying tweets of interests, indicating that the described system proved to be useful and user friendly.