Type of Culminating Activity

Graduate Student Project

Graduation Date

5-2016

Degree Title

Master of Science in Computer Science

Department

Computer Science

Major Advisor

Maria Soledad Pera, Ph.D.

Abstract

Twitter, with 288 million active users, has become the most popular platform for continuous real-time discussions. This leads to huge amounts of information related to the real-world, which has attracted researchers from both academia and industry. Event detection on Twitter has gained attention as one of the most popular domains of interest within the research community. Unfortunately, existing event detection methodologies have yet to fully explore Twitter metadata and instead rely solely on identifying events based on prior information or focus on events that belong to specific categories. Given the heavy volume of tweets that discuss events, summarization techniques can be used to create overviews that capture key facts related to events. Unfortunately, these techniques are constrained to analyze only well-structured events.

In this project, we conducted an in-depth study on the usage of Twitter around Points-of-Interest to verify that indeed Twitter data can be used to detect events of a city. Thereafter, we designed and implemented CEST (City Event Summarization using Twitter), a tool that analyzes tweets to identify different types of events occur- ring in a city and generate the corresponding summaries for the detected events. Unlike existing methodologies, we developed CEST to process unstructured documents and take advantage of short hand notations, hashtags, keywords, geographical data, temporal information, and sentiment terms within tweets in-tandem to both detect and generate a brief overview of events without prior knowledge. Furthermore, we introduced a novel strategy that analyzes sentiment and tweeting behavior of users over time to create a qualitative score that captures events' appeal to attendees.

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