A Semantic Modular Framework for Events Topic Modeling in Social Media

TL;DR

This paper introduces a Semantic Modular Model for event detection in social media, combining multiple modules to improve clustering and keyword extraction, outperforming existing methods on Twitter datasets.

Contribution

The paper presents a novel five-module Semantic Modular Model that enhances event detection and keyword relevance in social media content.

Findings

Higher performance in event identification on Twitter datasets

Outperforms state-of-the-art in keyword-precision by 7.9%

Effective in extracting important keywords for events

Abstract

The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural language processing has received researchers' attention, and various algorithms have been developed for this goal. In this paper, we propose a Semantic Modular Model (SMM) consisting of 5 different modules, namely Distributional Denoising Autoencoder, Incremental Clustering, Semantic Denoising, Defragmentation, and Ranking and Processing. The proposed model aims to (1) cluster various documents and ignore the documents that might not contribute to the identification of events, (2) identify more important and descriptive keywords. Compared to the state-of-the-art methods, the results show that the proposed model has a higher performance in identifying events with lower ranks and extracting keywords for more important events in three English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed method outperformed the best reported results in the mean keyword-precision metric by 7.9\%.