Discovering Organizational Correlations from Twitter

TL;DR

This paper presents an unsupervised framework called multi-CG that discovers organizational correlations from Twitter data by integrating multiple representations, effectively capturing complex relationships that are otherwise difficult to measure directly.

Contribution

The paper introduces a novel multi-CG framework that combines multiple Twitter-based representations to robustly identify organizational correlations without supervision.

Findings

The consensus correlation graph effectively captures organizational relationships.

Multi-CG outperforms single-representation methods in accuracy and robustness.

Empirical results demonstrate the framework's ability to uncover complex organizational links.

Abstract

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.