On the locality of keywords in Twitter streams

Abstract

The continuously increasing popularity of social media sites such as Twitter and Facebook has recently led to a number of approaches to detect and extract event information from social media streams. Such events play an important role, e.g., in supporting location-based services and improving situational awareness. Moreover, the introduction of GPS-equipped communication devises has led to an increase in the percentage of geo-tagged messages. These help to detect localized events, i.e., events occurring at a certain location, such as sport events or accidents. The main entities that indicate a localized event are local keywords that exhibit a surge in usage at the event location. In this paper, we propose an approach to extract local keywords from a Twitter stream by (1) identifying local keywords, and (2) estimating the central location of each keyword. This extraction process is performed in an online fashion using a sliding window on the Twitter stream. In addition, we address the problem of spatial outliers that adversely affect a proper identification of local keywords. Outliers occur when people far away from an event location use related keywords in their Tweets. We handle this problem by adjusting the spatial distribution of keywords based on their co-occurrence with place names that may refer to the location of an event. We evaluate the performance of our framework to reliably and efficiently extracting local keywords and estimating their central locations using a Twitter dataset.