Using semantic clustering to support situation awareness on Twitter: The case of World Views

TL;DR

This paper introduces SVOSSTC, a semantic clustering system leveraging Subject-Verb-Object structures to improve situation awareness on Twitter, especially during crises, by forming meaningful and consistent world views from social media data.

Contribution

The paper presents a novel SVO-based semantic clustering method, SVOSSTC, tailored for Twitter data to enhance situation awareness in crisis management.

Findings

SVOSSTC produces more meaningful clusters than existing methods.

The system improves the granularity and consistency of social media data analysis.

Results demonstrate the approach's effectiveness in real-world crisis scenarios.

Abstract

In recent years, situation awareness has been recognised as a critical part of effective decision making, in particular for crisis management. One way to extract value and allow for better situation awareness is to develop a system capable of analysing a dataset of multiple posts, and clustering consistent posts into different views or stories (or, world views). However, this can be challenging as it requires an understanding of the data, including determining what is consistent data, and what data corroborates other data. Attempting to address these problems, this article proposes Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on utilising the Subject-Verb-Object (SVO) typology in order to construct semantically consistent world views, in which individuals---particularly those involved in crisis response---might achieve an enhanced picture of a situation from social media data. To evaluate our system and its ability to provide enhanced situation awareness, we tested it against existing approaches, including human data analysis, using a variety of real-world scenarios. The results indicated a noteworthy degree of evidence (e.g., in cluster granularity and meaningfulness) to affirm the suitability and rigour of our approach. Moreover, these results highlight this article's proposals as innovative and practical system contributions to the research field.