Date of Award
Master of Science
Social media today is an important source of information about various events happening around the world. Among various social networking platforms, microtext based ones such as Twitter are of special interest as they are also a rich source of real-time events. In this thesis, our goal is to study the effectiveness of using Twitter as a social sensor for obtaining real-time information on road traffic conditions. Specifically, we focus on: i) identifying tweets that contain traffic event related information, ii) classify such tweets into six main groups of accident, fire, road construction, police activities, weather and others, iii) extract fine-grained location information about the traffic incident by analyzing tweet text. Our experimental results show that using Twitter as a social sensor for obtaining rich information about traffic events is indeed a promising approach. We show that we can correctly detect traffic related tweets with an accuracy of 81%. Moreover, the accuracy of correctly classifying traffic related tweets into one of the six categories is 97%. Lastly, our experimental results show that using only geo-tags of tweets is not sufficient for fine-grained localization of traffic incidents due to two reasons: i) a vast majority of traffic related tweets do not contain geo-tags, and ii) the location mentioned in the tweet text and the geo-tag of a tweet do not always agree. Such observations prove that fine-grained localization of traffic incidents from tweet must also include analysis of the tweet text using Natural Language Processing techniques.
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