Improved Topic modeling in Twitter through Community Pooling

TL;DR

This paper introduces a community-based tweet pooling method that enhances topic modeling in Twitter data by grouping tweets from user communities, improving accuracy and efficiency over existing methods.

Contribution

The paper proposes a novel community pooling scheme for Twitter topic modeling, outperforming previous pooling methods in quality and speed.

Findings

Community pooling improves topic cluster quality.

Method reduces computational time.

Outperforms state-of-the-art schemes on multiple datasets.

Abstract

Social networks play a fundamental role in propagation of information and news. Characterizing the content of the messages becomes vital for different tasks, like breaking news detection, personalized message recommendation, fake users detection, information flow characterization and others. However, Twitter posts are short and often less coherent than other text documents, which makes it challenging to apply text mining algorithms to these datasets efficiently. Tweet-pooling (aggregating tweets into longer documents) has been shown to improve automatic topic decomposition, but the performance achieved in this task varies depending on the pooling method. In this paper, we propose a new pooling scheme for topic modeling in Twitter, which groups tweets whose authors belong to the same community (group of users who mainly interact with each other but not with other groups) on a user interaction graph. We present a complete evaluation of this methodology, state of the art schemes and previous pooling models in terms of the cluster quality, document retrieval tasks performance and supervised machine learning classification score. Results show that our Community polling method outperformed other methods on the majority of metrics in two heterogeneous datasets, while also reducing the running time. This is useful when dealing with big amounts of noisy and short user-generated social media texts. Overall, our findings contribute to an improved methodology for identifying the latent topics in a Twitter dataset, without the need of modifying the basic machinery of a topic decomposition model.