Interaction Graph, Topical Communities, and Efficient Local Event Detection from Social Streams

12 Pages Posted: 22 Jan 2023

See all articles by Shubham Gupta

Shubham Gupta

affiliation not provided to SSRN

Suman Kundu

affiliation not provided to SSRN

Abstract

Social networks have become an essential part of daily life, and hence every real-world activity finds its place in this virtual world. The present paper proposes a methodology to find localized micro-events from the social network stream. The method is named CommunityINDICATOR. A concept of `separation of concerns' from the software design principle is incorporated in the methodology to reduce the execution time drastically from existing state-of-the-art methods of event detection. In order to reduce the execution time, the algorithm first generates an interaction graph from the social stream and applies community detection followed by a clustering algorithm onto it to detect micro-level events. Experiments have been conducted on Twitter data stream of 5 different cities on three different continents with the size of 2 million tweets. The proposed algorithm outperforms the latest state-of-the-art methods with 2-5% margin and others with more than 30% margin in terms of precision and recall. In addition, CommunityINDICATOR takes less time for the majority of the experiments, and in some cases, it takes more than 40% less time than the nearest comparing method.

Keywords: event detection, event extraction, data stream mining, unsupervised learning, machine learning

Suggested Citation

Gupta, Shubham and Kundu, Suman, Interaction Graph, Topical Communities, and Efficient Local Event Detection from Social Streams. Available at SSRN: https://ssrn.com/abstract=4334017 or http://dx.doi.org/10.2139/ssrn.4334017

Shubham Gupta (Contact Author)

affiliation not provided to SSRN ( email )

No Address Available

Suman Kundu

affiliation not provided to SSRN ( email )

No Address Available

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