Abstract:
Information propagation, decision making, and emergency prevention depend on social media event popularity prediction. Existing methods only predict single attributes like messages, hashtags, and images, which are not enough to represent complex social event propagation.
We predict event popularity using messages with multiple hashtags in this paper. We use hashtag influence to predict event popularity. We first propose a hashtag-influence-based cascade model to select influential hashtags over an event hashtag graph built by pairwise hashtag similarity and event-related hashtag topic distribution.
An event’s hashtag influence on content and social impact is measured using a novel method. Greedy hashtag correlation-based seed selection is suggested.
We then embed feature importance over events into the XGBOOST model to predict event popularity. An event-structure-based method incrementally updates the prediction model over social streams. We tested the proposed method extensively.
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