A Local-Global LDA Model for Discovering Geographical Topics from Social Media

TL;DR

This paper introduces a Local-Global LDA model that effectively discovers geographical topics from social media data by distinguishing local and global word relevance, outperforming baseline methods in multiple evaluation metrics.

Contribution

The paper presents a novel LDA-based model that incorporates local and global context weighting to improve geographical topic discovery from social media data.

Findings

Outperforms baseline methods in perplexity and entropy metrics

Effectively filters irrelevant words in geographical topic modeling

Demonstrates robustness on Weibo social media data

Abstract

Micro-blogging services can track users' geo-locations when users check-in their places or use geo-tagging which implicitly reveals locations. This "geo tracking" can help to find topics triggered by some events in certain regions. However, discovering such topics is very challenging because of the large amount of noisy messages (e.g. daily conversations). This paper proposes a method to model geographical topics, which can filter out irrelevant words by different weights in the local and global contexts. Our method is based on the Latent Dirichlet Allocation (LDA) model but each word is generated from either a local or a global topic distribution by its generation probabilities. We evaluated our model with data collected from Weibo, which is currently the most popular micro-blogging service for Chinese. The evaluation results demonstrate that our method outperforms other baseline methods in several metrics such as model perplexity, two kinds of entropies and KL-divergence of discovered topics.