Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments on a large Twitter dataset shows our methods are very effective.
REF studied a global bursty pattern derived from multiple types of tweets (posts, retweets, URL-embedded tweets) and modeled the smoothness of the state context.
Identifying Event-related Bursts via Social Media Activities
EMNLP
Computer Science