Abstract
As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA) algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.
Highlights
Twitter is one of the most popular social networking and microblogging services in the world
All the Twitter data for research were downloaded through the Twitter streaming application programming interface (API), for which there are three main streaming endpoints [48]: (1) Public Streams: streams of public data flowing through Twitter can be pushed
Our study explored the use of Twitter data to detect real-world events
Summary
Twitter is one of the most popular social networking and microblogging services in the world. As of 2017, Twitter had reached 330 million monthly active users [3] and an estimated 500 million tweets are sent per day Because these massive Twitter data can be accessed programmatically via APIs, Twitter has been a treasure trove for geo-social researchers based on big data [4], which offers an unprecedented opportunity to study social networks and human communication with active data [5,6], making tweets one of the favorite data sources of geo-social researchers [7,8]
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