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Time-Series Topic Analysis of Large-Scale Social Media Data using Two-stage Clustering

Authors:
Takako Hashimoto

Keywords: social media analysis; knowledge discovery; graph mining; two-stage clustering; time series.

Abstract:
Social media is a highly influential platform for sharing messages, photos, and videos. Understanding public perception through its vast data stream is essential. This study introduces a two-stage clustering method to extract coarse- grained topics from social media text data. First, graph clustering extracts micro-clusters from graphs generated based on the similarity of user posts, with each micro-cluster representing a fine-grained topic. The time series of these micro- clusters are then analyzed in the second stage through time series clustering to reveal coarse-grained topics. In this study, we consider applying this method to Yahoo! Japan News Comments related to the election of two specific candidates in Japan. This is expected to extract people's reactions to the candidates before and after the election.

Pages: 13 to 18

Copyright: Copyright (c) IARIA, 2025

Publication date: March 9, 2025

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-68558-244-9

Location: Lisbon, Portugal

Dates: from March 9, 2025 to March 13, 2025