Abstract
Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.
Highlights
Volunteered geographic information (VGI) [1] is a recent development that has considerably influenced the way humans interact and information is retrieved
How should the social event be depicted from a geographical perspective? Where is the event location? Is there any irregular geographical pattern caused by the event, and how can such patterns be represented with the geo-tagged social media posts?
Previous works have mainly detected events by investigating the semantic feature of the streaming social media data, while we considered that social events may lead to irregular geographical patterns in human mobility, and such geographical irregularity, in turn, can be used to detect social events
Summary
Volunteered geographic information (VGI) [1] is a recent development that has considerably influenced the way humans interact and information is retrieved. Most research on event detection with social media uses semantic-based methods and text mining [3,4]. Platforms, such as Twitter, provide accessible streaming data, which contains real-time text information, making it possible to detect emerging social events by examining the change in semantic-based features [5,6,7]. A new event detection methodology is proposed by investigating the geographical patterns of VGI data. To the best of our knowledge, previous works mainly detected social events by word frequency and semantic analysis, and there is still a need for a comprehensive methodology to effectively detect events by investigating irregular geographic patterns
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