Event Detection and Event-Relevant Tweet Extraction with Human Mobility

Event detection has been proved important in various applications, such as route selection to avoid the congestion an event causes or deciding whether to join an event that one is interested in. While geotagged tweets are popular sources of information for event detection, they are usually insufficient for accurate detection when scarce. On the other hand, non-geotagged tweets are more abundant, but include much noise that also deters accurate event detection. In this work, we aimed to enhance detection performance by combining aggregated smartphone GPS data and non-geotagged tweets. We propose a novel method to detect events based on deviations from inferred normal human mobility, selecting event-related topics that correlated with human mobility, and extracting event-relevant tweets by scoring each tweet according to its relevance to an event. The relevance of each tweet is gauged from the tweet’s meaning and posting time. We conducted empirical evaluations using data that include multiple events, such as baseball game and airport congestion. Our proposed method detected 9 out of 10 events regardless of the type and scale of the events, which attests improvement over the geotag-based method. We also confirmed that our model was able to extract the essential event-relevant tweets with an average accuracy of over 90%.