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Probabilistic temporal semantic graph: a holistic framework for event detection in twitter

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Abstract

Event detection on social media platforms, especially Twitter, poses significant challenges due to the dynamic nature and high volume of data. The rapid flow of tweets and the varied ways users express thoughts complicate the identification of relevant events. Accurately identifying and interpreting events from this noisy and fast-paced environment is crucial for various applications, including crisis management and market analysis. This paper presents a novel unsupervised framework for event detection on social media, designed to enhance the accuracy and efficiency of identifying significant events from Twitter data. The framework incorporates several innovative techniques, including dynamic bandwidth adjustment based on local data density, Mahalanobis distance integration, adaptive kernel density estimation, and an improved Louvain-MOMR method for community detection. Additionally, a new scoring system is implemented to accurately extract trending words that evoke strong emotions, improving the identification of event-related keywords. The proposed framework demonstrates robust performance across three diverse datasets: FACup, Super Tuesday, and US Election, showcasing its effectiveness in capturing temporal and semantic patterns within tweets.

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No datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Hadis Bashiri & Hassan Naderi

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  1. Hadis Bashiri
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All authors contributed to the design and implementation of the research, analysis of the results and to the writing of the manuscript. All authors reviewed the manuscript.

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Correspondence to Hassan Naderi.

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Declaration of generative AI and AI-assisted technologies in the writing process: during the preparation of this work the authors used ChatGPT, Quillbot, and Grammarly in order to rephrase and edit the text grammatically and in terms of typo. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

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Bashiri, H., Naderi, H. Probabilistic temporal semantic graph: a holistic framework for event detection in twitter. Knowl Inf Syst 66, 7581–7607 (2024). https://doi.org/10.1007/s10115-024-02208-1

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