Active Projects

Active Projects

Social Media Analysis - Dr. Sharon G. Small

Social Media has been exploding in number of platforms and use over the last decade.  Usage has been analyzed following violent behavior by a person who has social media accounts - could their actions have been predicted? Social Media is currently being looked at to see how it has affected our most recent presidential election.  It is currently being used in politics in ways no one could have predicted.

We are currently exploring what can be automatically determined by an analysis of social media posts. For example can we automatically determine a person's: education level, age, race, etc.  Can we determine over time if posts have change in tone or sentiment?  Can we determine if posts imply a violent or pacifist nature? Can posts clustered in geographic areas be used to predict social events?


TREC Tasks Track - Dr. Sharon G. Small

The primary goals of this track are to evaluate system's understanding of tasks users aim to achieve and evaluate relevance of retrieved documents with respect to underlying tasks in query. Research in Information Retrieval has traditionally focused on serving the best results for a single query, ignoring the reasons (or the task) that might have motivated the user to submit that query. Often times search engines are used to complete complex tasks (information needs); achieving these tasks with current search engines requires users to issue multiple queries. For example, booking travel to a location such as London could require the user to submit various queries such as flights to London, hotels in London, points of interest around London, etc. Similarly, a person who is trying to organize a wedding would need to issue separate queries in order to locate stores to buy a wedding gown, arrange catering, book honeymoon, etc. In some cases users may not even be aware of all the subtasks they need to achieve to satisfy their information need, which makes search an even more difficult experience. Ideally, a search engine should be able to understand the reason that caused the user to submit a query (i.e., the actual task that caused the query to be issued), and rather than just showing results relevant to the query submitted, the search engine should be able to guide the user to achieve their task by incorporating the information about the actual information need. The goal of this track is to devise evaluation methodologies for evaluating the quality of task based information retrieval systems. We have completed a system and submitted our results to TREC 2017.