Abstract:
Recently, social media research has grown. This article predicts active users in online social events, which is rare compared to previous work on emerging topics. This prediction task is a binary classification problem based on Ferguson and New York Chockhold tweet streams. Then, a comprehensive user feature system is created to characterize the events’ online participants, including statistical, image-pixel, emotional, and personality features. Next, the Weighted Random Forest classifier is used to classify. The user feature system and classifier can store and predict previous events. The Weighted-RF trained by Ferguson event samples can predict NYC event active users with an AUC of 0.8392. The image-content-based personality model also helps quantify online social events by depicting user portraits.
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