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A New Mashup Based Method for Event Detection from Social Media

Author

Listed:
  • Abir Troudi

    (University of Sfax)

  • Corinne Amel Zayani

    (University of Sfax)

  • Salma Jamoussi

    (University of Sfax)

  • Ikram Amous Ben Amor

    (University of Sfax)

Abstract

Some events, such as terrorism attacks, earthquakes, and other events that represent tipping points, remain engraved in our memories. Today, through social media, researchers attempt to propose approaches for event detection. However, they are confronted to certain challenges owing to the noise of data propagated throughout social media. In this paper, a new mashup based method for event detection from social media is proposed using hadoop framework. The suggested approach aims at detecting real-world events by exploiting data collected from different social media sites. Indeed, the detected events are characterized by such descriptive dimensions as topic, time and location. Moreover, our approach assures a bilingual event detection. In fact, the proposed approach is able to detect events in English and French languages. In addition, our approach provides a mashup based multidimensional visualization by combining different multimedia components so as to add more details to the detected events. Furthermore, in order to overcome the problems occurring from the processing of big data, we integrated our approach into the hadoop distributed system.

Suggested Citation

  • Abir Troudi & Corinne Amel Zayani & Salma Jamoussi & Ikram Amous Ben Amor, 2018. "A New Mashup Based Method for Event Detection from Social Media," Information Systems Frontiers, Springer, vol. 20(5), pages 981-992, October.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:5:d:10.1007_s10796-018-9828-9
    DOI: 10.1007/s10796-018-9828-9
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    Citations

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    Cited by:

    1. Jennifer Fromm & Kaan Eyilmez & Melina Baßfeld & Tim A. Majchrzak & Stefan Stieglitz, 2023. "Social Media Data in an Augmented Reality System for Situation Awareness Support in Emergency Control Rooms," Information Systems Frontiers, Springer, vol. 25(1), pages 303-326, February.
    2. Bin Xia & Yuxuan Bai & Junjie Yin & Yun Li & Jian Xu, 2021. "LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling," Information Systems Frontiers, Springer, vol. 23(2), pages 285-298, April.
    3. Saptarshi Ghosh & Kripabandhu Ghosh & Debasis Ganguly & Tanmoy Chakraborty & Gareth J. F. Jones & Marie-Francine Moens & Muhammad Imran, 2018. "Exploitation of Social Media for Emergency Relief and Preparedness: Recent Research and Trends," Information Systems Frontiers, Springer, vol. 20(5), pages 901-907, October.
    4. Bin Xia & Yuxuan Bai & Junjie Yin & Yun Li & Jian Xu, 0. "LogGAN: a Log-level Generative Adversarial Network for Anomaly Detection using Permutation Event Modeling," Information Systems Frontiers, Springer, vol. 0, pages 1-14.

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