Fang Jin, Feng Chen, Rupinder Paul Khandpur, Naren Ramakrishnan
Event detection in online social media has primarily focused on identifying abnormal spikes, or bursts, in activity. However, disruptive events such as socio-economic disasters, civil unrest, and even power outages, often involve abnormal troughs or lack of activity, leading to absenteeism. We present the first study, to our knowledge, that models absenteeism and uses detected absenteeism instances as a basis for event detection in location-based social networks such as Twitter. The proposed framework addresses the challenges of (i) early detection of absenteeism, (ii) identifying the locus of the absenteeism, and (iii) identifying groups or communities underlying the absenteeism. Our approach uses the formalism of graph wavelets to represent the spatiotemporal structure of user activity in a location-based social network. This formalism facilitates multiscale analysis, enabling us to detect anomalous behavior at different graph resolutions, which in turn allows the identification of event locations and underlying groups. The effectiveness of our approach is evaluated using Twitter activity related to civil unrest events in Latin America.
Fang Jin, Feng Chen, Rupinder Paul Khandpur, Chang-Tien Lu, Naren Ramakrishnan: Absenteeism Detection in Social Media. SDM 2017: 606-614
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- SIAM International Conference on Data Mining
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