SMERC: Social media event response clustering using textual and temporal information
This work addresses real-time event detection from social media for applications like news monitoring, but it is incremental as it builds on existing tweet clustering methods by adding temporal analysis.
The authors tackled tweet clustering for event detection by incorporating temporal information, showing that related tweets have exponentially decaying time gaps while unrelated ones are uniform, and demonstrated state-of-the-art performance in clustering precision and recall on sporting event data.
Tweet clustering for event detection is a powerful modern method to automate the real-time detection of events. In this work we present a new tweet clustering approach, using a probabilistic approach to incorporate temporal information. By analysing the distribution of time gaps between tweets we show that the gaps between pairs of related tweets exhibit exponential decay, whereas the gaps between unrelated tweets are approximately uniform. Guided by this insight, we use probabilistic arguments to estimate the likelihood that a pair of tweets are related, and build an improved clustering method. Our method Social Media Event Response Clustering (SMERC) creates clusters of tweets based on their tendency to be related to a single event. We evaluate our method at three levels: through traditional event prediction from tweet clustering, by measuring the improvement in quality of clusters created, and also comparing the clustering precision and recall with other methods. By applying SMERC to tweets collected during a number of sporting events, we demonstrate that incorporating temporal information leads to state of the art clustering performance.