A Human Word Association based model for topic detection in social networks

TL;DR

This paper presents a novel topic detection framework for social networks inspired by human word association, which outperforms existing methods in accuracy and applicability across different datasets.

Contribution

It introduces a human word association-based model and a specialized extraction algorithm for improved topic detection in social networks.

Findings

Significant improvement in topic-recall and keyword F1 measure.

Outperforms other methods on FA-CUP dataset.

Effective on Persian Telegram posts dataset.

Abstract

With the widespread use of social networks, detecting the topics discussed on these platforms has become a significant challenge. Current approaches primarily rely on frequent pattern mining or semantic relations, often neglecting the structure of the language. Language structural methods aim to discover the relationships between words and how humans understand them. Therefore, this paper introduces a topic detection framework for social networks based on the concept of imitating the mental ability of word association. This framework employs the Human Word Association method and includes a specially designed extraction algorithm. The performance of this method is evaluated using the FA-CUP dataset, a benchmark in the field of topic detection. The results indicate that the proposed method significantly improves topic detection compared to other methods, as evidenced by Topic-recall and the keyword F1 measure. Additionally, to assess the applicability and generalizability of the proposed method, a dataset of Telegram posts in the Persian language is used. The results demonstrate that this method outperforms other topic detection methods.