A Review of Research on Social Network Event Detection Methods

Authors

  • Tianqi Chen

DOI:

https://doi.org/10.62051/ijcsit.v3n1.12

Keywords:

Social Networks, Information Dissemination, Event Detection

Abstract

In recent years, with the development of technological infrastructure and the use of tech products, internet usage has become widespread globally. Significant advancements have been made in the use of social networks, which are now more readily accessible through Internet and Web 3.0 technologies such as Facebook, Twitter, and Instagram. Consequently, over the past decade, numerous researchers have been developing methods for event detection based on data collected from social media platforms. The methodologies devised for discovering events are typically modular in design and novel in terms of scale and speed. To review the research in this field, we have compiled existing works on social network event detection and conducted a comprehensive and in-depth survey. Methods for social network event detection are elaborated and categorized, with performance evaluations conducted using relevant metrics. Finally, we offer perspectives on future directions.

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References

Zhou S, Ng S T, Huang G, et al. Extracting interrelated information from road-related social media data [J]. Advanced Engineering Informatics, 2022, 54: 101780.

Zhang W, Zhong T, Li C, et al. CausalRD: A Causal View of Rumor Detection via Eliminating Popularity and Conformity Biases [C]//IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 2018: 1369-1378.

Zervopoulos A, Alvanou A G, Bezas K, et al. Deep learning for fake news detection on Twitter regarding the 2019 Hong Kong protests [J]. Neural Computing and Applications, 2022, 34(2): 969-982.

Allan H, Gao Q, Li H, et al. A structural evolution-based anomaly detection method for generalized evolving social networks [J]. The Computer Journal, 2022, 65(5): 1189-1199.

Wang B, Yang C, Chen Y. Detection Anomaly in Video Based on Deep Support Vector Data Description [J]. Computational Intelligence and Neuroscience, 2022, 2012.

Selvi E, Adimoolam M, Karthi G, et al. Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video [J]. Electronics, 2020, 11(24): 4210.

Xia R, Xuan K, Yu J. A state-independent and time-evolving network for early rumor detection in social media [C]//Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP). 2020: 9042-9051.

Xu K, Zou K, Huang Y, et al. Mining community and inferring friendship in mobile social networks [J]. Neurocomputing, 2016, 174: 605-616.

Sun X, Wu Y, Liu L, et al. Efficient event detection in social media data streams [C]//2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE, 2015: 1711-1717.

Chen G, Kong Q, Mao W. Online event detection and tracking in social media based on neural similarity metric learning [C]//2017 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2017: 182-184.

Cadena J, Chen F, Vullikanti A. Graph anomaly detection based on Steiner connectivity and density [J]. Proceedings of the IEEE, 2018, 106(5): 829-845.

Dionísio N, Alves F, Ferreira P M, et al. Towards end-to-end cyberthreat detection from Twitter using multi-task learning [C]//2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-8.

Gao K, Xu H, Wang J. A rule-based approach to emotion cause detection for Chinese micro-blogs [J]. Expert Systems with Applications, 2015, 42(9): 4517-4528.

Wang H, Gao Q, Li H, et al. A structural evolution-based anomaly detection method for generalized evolving social networks [J]. The Computer Journal, 2022, 65(5): 1189-1199.

Vivek Joe Bharath A, Thirumarimurugan M. Analysis and implementation of certain multivariate statistical process monitoring Tools for fault detection and isolation (FDI) task in a laboratory scale shell-tube heat exchanger [J]. Journal of Intelligent & Fuzzy Systems, 2022, 43(1): 1651-1668.

Bhuvaneswari A, Valliyammai C. Information entropy based event detection during disaster in cyber-social networks [J]. Journal of Intelligent & Fuzzy Systems, 2019, 36(5): 3981-3992.

Aynehband M, Hosseinzadeh M, Zarrabi H, et al. Accuracy and availability modeling of social networks for Internet of Things event detection applications [J]. Wireless Networks, 2019, 25: 4299-4317.

Persia F, Helmer S. A framework for high-level event detection in a social network context via an extension of iseql [C]//2018 IEEE 12th International Conference on Semantic Computing (ICSC). IEEE, 2018: 140-147.

Hu W, Wang H, Qiu Z, et al. An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent [J]. World Wide Web, 2017, 20: 775-795.

Hasni S, Faiz S. Real-time Event localization and detection over social networks using Apache Intelligence [C]//2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2017: 264-271.

Unankard S, Li X, Sharaf M A. Emerging event detection in social networks with location sensitivity [J]. World Wide Web, 2015, 18: 1393-1417.

Zhu T, Li J, Hu X, et al. The dynamic privacy-preserving mechanisms for online dynamic social networks [J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(6): 2962-2974.

Zhou H, Yin H, Zheng H, et al. A survey on multi-modal social event detection [J]. Knowledge-Based Systems, 2020, 195: 105695.

Nguyen T, Grishman R. Graph convolutional networks with argument-aware pooling for event detection [C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).

Ahmad F, Abbasi A, Kitchens B, et al. Deep learning for adverse event detection from web search [J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(6): 2681-2695.

Yuan J. Learning context-aware representation for event detection [C]//2021 17th International Conference on Computational Intelligence and Security (CIS). IEEE, 2021: 600-603.

Miao Y, Chen C, Pan L, et al. Machine learning–based cyber-attacks targeting on controlled information: A survey [J]. ACM Computing Surveys (CSUR), 2021, 54(7): 1-36.

Wu G, Guo Z, Li L, et al. Video Abnormal Event Detection Based on CNN and LSTM [C]//2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). IEEE, 2020: 334-338.

Shin S, Choi M, Choi J, et al. Stexnmf: Spatio-temporally exclusive topic discovery for anomalous event detection [C]//2017 IEEE International conference on data mining (ICDM). IEEE, 2017: 435-444.

Zeng Y, Feng Y, Ma R, et al. Scale up event extraction learning via automatic training data generation [C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2018, 32(1).

Sun P, Zhang R, Jiang Y, et al. Sparse R-CNN: An end-to-end framework for object detection [J]. IEEE transactions on pattern analysis and machine intelligence, 2023.

Kaur R, Singh S. A comparative analysis of structural graph metrics to identify anomalies in online social networks [J]. Computers & Electrical Engineering, 2021, 57: 294-310.

Yan L, Luo C, Shao R. Discrete log anomaly detection: a novel time-aware graph-based link prediction approach [J]. Information Sciences, 2023, 647: 119576.

Ni W, Sun H. Research on Visual Communication Page Design Theory of Multi-Source Information Fusion Using Deep Web Crawlers [C]//2023 International Conference on Applied Physics and Computing (ICAPC). IEEE, 2023: 455-459.

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Published

15-06-2024

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Section

Articles

How to Cite

Chen, T. (2024). A Review of Research on Social Network Event Detection Methods. International Journal of Computer Science and Information Technology, 3(1), 82-92. https://doi.org/10.62051/ijcsit.v3n1.12