Finding Influential Local Users with Similar Interest from Geo-Tagged Social Media Data

Geo-tagged social media data provides abundant resources for people in need of local information. In this paper, we study how to find the top-k influential local users from geo-tagged social media data who have interests similar to a query. Such local users can be of particular importance for a variety of activities from events organizing to online advertising. We formulate the problem as Top-k Influential Similar Local Query (TkISL) and provide a complete set of techniques for solving it. To effectively manage the social media users, we design three hybrid user profiling techniques, an indexing tree, and an upper bound query-user similarity that enables efficient pruning in query processing. To process TkISL queries, we propose a baseline method and a more efficient improved method. The former directly uses the indexing tree and the upper bound for pruning, whereas the latter speeds up the query processing by enhancing the tree and pruning. Finally, we conduct extensive experimental studies to evaluate our proposals on real geo-tagged tweet corpora. The experimental results demonstrate the efficiency and effectiveness of our proposals.