AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Big Data Mining and Analytics Article
PDF (2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

A Survey on Event Tracking in Social Media Data Streams

School of Computer Science and Communication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Jiangsu University, Zhenjiang 212013, China
School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, UK
Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China
University Executive Office, Edinburgh Napier University, Edinburgh, EH11 4BN, UK
Show Author Information

Abstract

Social networks are inevitable parts of our daily life, where an unprecedented amount of complex data corresponding to a diverse range of applications are generated. As such, it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks. Event tracking in social networks finds various applications, such as network security and societal governance, which involves analyzing data generated by user groups on social networks in real time. Moreover, as deep learning techniques continue to advance and make important breakthroughs in various fields, researchers are using this technology to progressively optimize the effectiveness of Event Detection (ED) and tracking algorithms. In this regard, this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks. We introduce mainstream event tracking methods, which involve three primary technical steps: ED, event propagation, and event evolution. Finally, we introduce benchmark datasets and evaluation metrics for ED and tracking, which allow comparative analysis on the performance of mainstream methods. Finally, we present a comprehensive analysis of the main research findings and existing limitations in this field, as well as future research prospects and challenges.

Keywords

Event Detection (ED)event propagationevent evolutionsocial networks

References

[1]

Y. Peng, Y. Zhao, and J. Hu, On the role of community structure in evolution of opinion formation: A new bounded confidence opinion dynamics, Inf. Sci., vol. 621, pp. 672–690, 2023.
Crossref Google Scholar
[2]

H. Hassani, R. Razavi-Far, M. Saif, and E. Herrera-Viedma, Blockchain-enabled trust building for managing consensus in linguistic opinion dynamics, IEEE Trans. Fuzzy Syst., vol. 31, no. 8, pp. 2722–2733, 2023.
Crossref Google Scholar
[3]

I. V. Kozitsin, Opinion dynamics of online social network users: A micro-level analysis, J. Math. Sociol., vol. 47, no. 1, pp. 1–41, 2023.
Crossref Google Scholar
[4]

P. Liu, Y. Li, and P. Wang, Opinion dynamics and minimum adjustment-driven consensus model for multi-criteria large-scale group decision making under a novel social trust propagation mechanism, IEEE Trans. Fuzzy Syst., vol. 31, no. 1, pp. 307–321, 2023.
Crossref Google Scholar
[5]

L. Weng, Q. Zhang, Z. Lin, L. Wu, and J. H. Zhang, Integrating interactions between target users and opinion leaders for better recommendations: An opinion dynamics approach, Comput. Commun., vol. 198, pp. 98–107, 2023.
Crossref Google Scholar
[6]

Y. Liu, J. Liu, and K. Wu, Cost-effective competition on social networks: A multi-objective optimization perspective, Inf. Sci., vol. 620, pp. 31–46, 2023.
Crossref Google Scholar
[7]

Z. Wu, Q. Zhou, Y. Dong, J. Xu, A. H. Altalhi, and F. Herrera, Mixed opinion dynamics based on DeGroot model and Hegselmann-Krause model in social networks, IEEE Trans. Syst. Man, Cybern.: Syst., vol. 53, no. 1, pp. 296–308, 2022.
Crossref Google Scholar
[8]
A. Saravanou, N. Panagiotou, and D. Gunopulos, News monitor: A framework for querying news in real time, in Proc. 43 rd European Conf. on Information Retrieval, Virtual Event, 2021, pp. 543–548.
Crossref
[9]

Z. K. Abbas and A. A. Al-Ani, An adaptive algorithm based on principal component analysis-deep learning for anomalous events detection, Indones. J. Electr. Eng. Comput. Sci., vol. 29, no. 1, pp. 421–430, 2023.
Crossref Google Scholar
[10]

X. Chen, H. Wang, L. Ke, Z. Lu, H. Su, and X. Chen, Identifying Cantonese rumors with discriminative feature integration in online social networks, Expert Syst. Appl., vol. 215, p. 119347, 2023.
Crossref Google Scholar
[11]

L. Jiang, L. Shi, L. Liu, J. Yao, B. Yuan, and Y. Zheng, An efficient evolutionary user interest community discovery model in dynamic social networks for internet of people, IEEE Internet Things J., vol. 6, no. 6, pp. 9226–9236, 2019.
Crossref Google Scholar
[12]

L. L. Shi, L. Liu, Y. Wu, L. Jiang, J. Panneerselvam, and R. Crole, A social sensing model for event detection and user influence discovering in social media data streams, IEEE Trans. Comput. Soc. Syst., vol. 7, no. 1, pp. 141–150, 2020.
Crossref Google Scholar
[13]

Y. Xiao, C. Song, and Y. Liu, Social hotspot propagation dynamics model based on multidimensional attributes and evolutionary games, Commun. Nonlinear Sci. Numer. Simul., vol. 67, pp. 13–25, 2019.
Crossref Google Scholar
[14]

O. Loyola-González, A. López-Cuevas, M. A. Medina-Pérez, B. Camiña, J. E. Ramírez-Márquez, and R. Monroy, Fusing pattern discovery and visual analytics approaches in tweet propagation, Inform. Fusion, vol. 46, pp. 91–101, 2019.
Crossref Google Scholar
[15]
D. Diallo and T. Hecking, Gradual network sparsification and georeferencing for location-aware event detection in microblogging services, in Proc. 11 th Int. Conf. on Complex Networks and Their Applications XI, Province of Palermo, Italy, 2023, pp. 108–120.
Crossref
[16]

M. Liu, X. Wang, S. Liang, X. Sheng, and S. Lou, Single and composite disturbance event recognition based on the DBN-GRU network in φ-OTDR, Appl. Opt., vol. 62, no. 1, pp. 133–141, 2023.
Crossref Google Scholar
[17]

P. H. Khotimah, A. Arisal, A. F. Rozie, E. Nugraheni, D. Riswantini, W. Suwarningsih, D. Munandar, and A. Purwarianti, Monitoring Indonesian online news for COVID-19 event detection using deep learning, Int. J. Electr. Comput. Eng., vol. 13, no. 1, pp. 957–971, 2023.
Crossref Google Scholar
[18]

Y. Jiang, R. Liang, and J. Zhang, Network public opinion detection during the coronavirus pandemic: a short-text relational topic model, ACM T. Knowl. Discov. D., vol. 16, no. 3, pp. 1–27, 2021.
Crossref Google Scholar
[19]

L. Zhang, T. Wang, and Z. Jin, The research on social networks public opinion propagation influence models and its controllability, China Commun., vol. 15, no. 7, pp. 98–110, 2018.
Crossref Google Scholar
[20]

M. Zhuang, Y. Li, and X. Tan, Analysis of public opinion evolution of COVID-19 based on LDA-ARMA hybrid model, Complex Intell. Syst., vol. 7, pp. 3165–3178, 2021.
Crossref Google Scholar
[21]

C. Qian, N. Mathur, N. H. Zakaria, R. Arora, V. Gupta, and M. Ali, Understanding public opinions on social media for financial sentiment analysis using AI-based techniques, Inf. Process. Manage., vol. 59, no. 6, p. 103098, 2022.
Crossref Google Scholar
[22]

P. Wu, J. Liu, and F. Shen, A deep one-class neural network for anomalous event detection in complex scenes, IEEE Trans. Neural Netw. Learn., vol. 31, no. 7, pp. 2609–2622, 2019.
Crossref Google Scholar
[23]

J. Kim and M. Hastak, Social network analysis: Characteristics of online social networks after a disaster, Int. J. Inf. Manag.,, vol. 38, no. 1, pp. 86–96, 2018.
Crossref Google Scholar
[24]

I. Afyouni, Z. Al Aghbari, and R. A. Razack, Multi-feature, multi-modal, and multi-source social event detection: A comprehensive survey, Inf. Fusion, vol. 79, pp. 279–308, 2022.
Crossref Google Scholar
[25]

A. Bondielli and F. Marcelloni, A survey on fake news and rumour detection techniques, Inf. Sci., vol. 497, pp. 38–55, 2019.
Crossref Google Scholar
[26]

L. L. Shi, L. Liu, Y. Wu, L. Jiang, and J. Hardy, Event detection and user interest discovering in social media data streams, IEEE Access, vol. 5, pp. 20953–20964, 2017.
Crossref Google Scholar
[27]

W. Hu, H. Wang, Z. Qiu, C. Nie, L. Yan, and B. Du, An event detection method for social networks based on hybrid link prediction and quantum swarm intelligent, World Wide Web, vol. 20, no. 4, pp. 775–795, 2017.
Crossref Google Scholar
[28]

D. Mumin, L. L. Shi, L. Liu, and J. Panneerselvam, Data-driven diffusion recommendation in online social networks for the internet of people, IEEE Trans. Syst. Man, Cybern.: Syst., vol. 52, no. 1, pp. 166–178, 2022.
Crossref Google Scholar
[29]

J. Tong, L. Shi, L. Liu, J. Panneerselvam, and Z. Han, A novel influence maximization algorithm for a competitive environment based on social media data analytics, Big Data Mining and Analytics, vol. 5, no. 2, pp. 130–139, 2022.
Crossref Google Scholar
[30]

L. L. Shi, L. Liu, Y. Wu, L. Jiang, M. Kazim, H. Ali, and J. Panneerselvam, Human-centric cyber social computing model for hot-event detection and propagation, IEEE Trans. Comput. Soc. Syst., vol. 6, no. 5, pp. 1042–1050, 2019.
Crossref Google Scholar
[31]

D. M. Kingsbury, M. P. Bhatta, B. Castellani, A. Khanal, E. Jefferis, and J. S. Hallam, The personal social networks of resettled Bhutanese refugees during pregnancy in the United States: A social network analysis, J. Commun. Health, vol. 43, no. 6, pp. 1028–1036, 2018.
Crossref Google Scholar
[32]

L. Jiang, L. Liu, J. Yao, and L. Shi, A user interest community evolution model based on subgraph matching for social networking in mobile edge computing environments, J. Cloud Comput., vol. 9, no. 1, p. 69, 2020.
Crossref Google Scholar
[33]
J. Teevan, D. Ramage, and M. R. Morris, # TwitterSearch: A comparison of microblog search and web search, in Proc. 4 th ACM Int. Conf. on Web Search and Data Mining, Hong Kong, China, 2011, pp. 35–44.
Crossref
[34]

H. Sun, Y. Geng, L. Hu, L. Shi, and T. Xu, Measuring China’s new energy vehicle patents: A social network analysis approach, Energy, vol. 153, pp. 685–693, 2018.
Crossref Google Scholar
[35]

B. Liu, Q. Zhou, R. X. Ding, I. Palomares, and F. Herrera, Large-scale group decision making model based on social network analysis: Trust relationship-based conflict detection and elimination, Eur. J. Oper. Res., vol. 275, pp. 737–754, 2019.
Crossref Google Scholar
[36]
R. Bridgstock, Employability and career development learning through social media: Exploring the potential of LinkedIn, in Challenging Future Practice Possibilities, J. Higgs, S. Cork, and D. Horsfall, eds. Rotterdam, The Netherlands: Sense-Brill, 2019, pp. 143–152.
Crossref
[37]
B. Palani, S. Elango, and K. V. Viswanathan, CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and BERT, Multimed. Tools Appl., vol. 81, no. 4, pp. 5587–5620, 2022.
Crossref
[38]

L. L. Shi, L. Liu, L. Jiang, R. Zhu, and J. Panneerselvam, QoS prediction for smart service management and recommendation based on the location of mobile users, Neurocomputing, vol. 471, pp. 12–20, 2022.
Crossref Google Scholar
[39]
G. Doddington, A. Mitchell, M. Przybocki, L. Ramshaw, S. Strassel, and R. Weischedel, The automatic content extraction (ACE) program-tasks, data, and evaluation, in Proc. 4 th Int. Conf. on Language Resources and Evaluation, Lisbon, Portugal, 2004, pp. 837–840.
[40]

R. F. He and S. Y. Duan, Joint Chinese event extraction based multi-task learning, (in Chinese), J. Softw., vol. 30, no. 4, pp. 1015–1030, 2019.
Google Scholar
[41]

W. Z. Al-Dyani, F. K. Ahmad, and S. S. Kamaruddin, A survey on event detection models for text data streams, J. Comput. Sci., vol. 16, no. 7, pp. 916–935, 2020.
Crossref Google Scholar
[42]

B. Jang, M. Kim, G. Harerimana, S. U. Kang, and J. W. Kim, Bi-LSTM model to increase accuracy in text classification: Combining Word2vec CNN and attention mechanism, Appl. Sci., vol. 10, no. 17, p. 5841, 2020.
Crossref Google Scholar
[43]

L. Zhan, X. Jiang, and Q. Liu, Research on Chinese event extraction method based on HMM and multi-stage method, J. Phys.: Conf. Ser., vol. 1732, no. 1, p. 012024, 2021.
Crossref Google Scholar
[44]

H. Fei, Y. Ren, and D. Ji, A tree-based neural network model for biomedical event trigger detection, Inf. Sci., vol. 512, pp. 175–185, 2020.
Crossref Google Scholar
[45]

X. Feng, B. Qin, and T. Liu, A language-independent neural network for event detection, Sci. China Inf. Sci., vol. 61, no. 9, p. 092106, 2018.
Crossref Google Scholar
[46]

J. Jin, H. Guo, J. Xu, X. Wang, and F. Y. Wang, An end-to-end recommendation system for urban traffic controls and management under a parallel learning framework, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1616–1626, 2021.
Crossref Google Scholar
[47]
J. Liu, Y. Chen, K. Liu, and J. Zhao, Event detection via gated multilingual attention mechanism, in Proc. 32 nd AAAI Conf. on Artificial Intelligence, the 30 th Innovative Applications of Artificial Intelligence, and the 8 th AAAI Symp. on Educational Advances in Artificial Intelligence, New Orleans, LA, USA, 2018, pp. 4865–4872.
Crossref
[48]
A. Brown, A. Tuor, B. Hutchinson, and N. Nichols, Recurrent neural network attention mechanisms for interpretable system log anomaly detection, in Proc. 1 st Workshop on Machine Learning for Computing Systems, Tempe, AZ, USA, 2018, p. 1.
Crossref
[49]

W. Yan, L. Zhou, Z. Qian, L. Xiao, and H. Zhu, Sentiment analysis of student texts using the CNN-BiGRU-AT model, Sci. Program, vol. 2021, p. 8405623, 2021.
Crossref Google Scholar
[50]
N. Chen, J. Li, and Y. Man, Research on aviation unsafe information text analysis based on improved CNN-BiGRU-att model, in Proc. IEEE 3 rd Int. Conf. on Civil Aviation Safety and Information Technology, Changsha, China, 2021, pp. 322–327.
Crossref
[51]
P. Cao, Y. Chen, J. Zhao, and T. Wang, Incremental event detection via knowledge consolidation networks, in Proc. 2020 Conf. on Empirical Methods in Natural Language Processing. doi: 10.18653/v1/2020.emnlp-main.52.
Crossref
[52]

L. Li, Y. Lin, and B. Du, Real-time traffic incident detection based on a hybrid deep learning model, Transp. A: Transp. Sci, vol. 18, no. 1, pp. 78–98, 2022.
Crossref Google Scholar
[53]

P. Luo, L. Ding, X. Yang, and Y. Xiang, Chinese event detection based on data augmentation and weakly supervised adversarial training, (in Chinese), J. Comput. Appl., vol. 42, no. 10, pp. 102–114, 2022.
Google Scholar
[54]
S. Liu, Y. Chen, K. Liu, and J. Zhao, Exploiting argument information to improve event detection via supervised attention mechanisms, in Proc. 55 th Annu. Meeting of the Association for Computational Linguistics, Vancouver, Canada, 2017, pp. 1789–1798.
Crossref
[55]
H. Yan, X. Jin, X. Meng, J. Guo, and X. Cheng, Event detection with multi-order graph convolution and aggregated attention, in Proc. 2019 Conf. on Empirical Methods in Natural Language Processing and the 9 th Int. Joint Conf. on Natural Language Processing, Hong Kong, China, 2019, pp. 5766–5770.
Crossref
[56]

Z. Wang, Y. Guo, and J. Wang, Empower Chinese event detection with improved atrous convolution neural networks, Neural Comput. Appl., vol. 33, no. 11, pp. 5805–5820, 2021.
Crossref Google Scholar
[57]
D. Shah, M. Hurley, J. Liu, and M. P. Daggett, Unsupervised content-based characterization and anomaly detection of online community dynamics, in Proc. 52 nd Hawaii Int. Conf. on System Sciences, Grand Wailea, HA, USA, 2019, pp. 2264–2273.
Crossref
[58]
A. Bhuvaneswari and C. Valliyammai, Social IoT-enabled emergency event detection framework using geo-tagged microblogs and crowdsourced photographs, in Emerging Technologies in Data Mining and Information Security, A. Abraham, P. Dutta, J. K. Mandal, A. Bhattacharya, and S. Dutta, eds. Singapore: Springer, 2019, pp. 151–162.
Crossref
[59]

M. Hasan, M. A. Orgun, and R. Schwitter, Real-time event detection from the Twitter data stream using the TwitterNews+ framework, Inf. Process. Manage., vol. 56, no. 3, pp. 1146–1165, 2019.
Crossref Google Scholar
[60]
O. Kolchyna, T. T. P. Souza, P. C. Treleaven, and T. Aste, A framework for Twitter events detection, differentiation and its application for retail brands, in Proc. 2016 Future Technologies Conf., San Francisco, CA, USA, 2016, pp. 323–331.
Crossref
[61]
T. Sakaki, M. Okazaki, and Y. Matsuo, Earthquake shakes Twitter users: Real-time event detection by social sensors, in Proc. 19 th Int. Conf. on World Wide Web, Raleigh, NC, USA, 2010, pp. 851–860.
Crossref
[62]
R. Long, H. Wang, Y. Chen, O. Jin, and Y. Yu, Towards effective event detection, tracking and summarization on microblog data, in Proc. 12 th Int. Conf. on Web-Age Information Management, Wuhan, China, 2011, pp. 652–663.
Crossref
[63]
J. Weng and B. S. Lee, Event detection in Twitter, in Proc. 5 th Int. AAAI Conf. on Weblogs and Social Media, Barcelona, Spain, 2011, pp. 401–408.
Crossref
[64]
T. Matuszka, Z. Vincellér, and S. Laki, On a keyword-lifecycle model for real-time event detection in social network data, in Proc. IEEE 4 th Int. Conf. on Cognitive Infocommunications, Budapest, Hungary, 2013, pp. 453–458.
Crossref
[65]
J. Allan, Introduction to topic detection and tracking, in Topic Detection and Tracking : Event-based Information Organization, J. Allan, ed. Boston, MA, USA: Springer, 2002, pp. 1–16.
Crossref
[66]

A. Troudi, C. A. Zayani, S. Jamoussi, and I. A. B. Amor, A new mashup based method for event detection from social media, Inf. Syst. Front., vol. 20, no. 5, pp. 981–992, 2018.
Crossref Google Scholar
[67]

F. Atefeh and W. Khreich, A survey of techniques for event detection in Twitter, Comput. Intell., vol. 31, no. 1, pp. 132–164, 2015.
Crossref Google Scholar
[68]
T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An efficient data clustering method for very large databases, ACM SIGMOD Rec., vol. 25, no. 2, pp. 103–114, 1996.
Crossref
[69]

Z. Lv, T. Liu, C. Shi, J. A. Benediktsson, and H. Du, Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images, IEEE Access, vol. 7, pp. 34425–34437, 2019.
Crossref Google Scholar
[70]
M. Mathioudakis and N. Koudas, TwitterMonitor: Trend detection over the Twitter stream, in Proc. 2010 ACM SIGMOD Int. Conf. on Management of Data, Indianapolis, IN, USA, 2010, pp. 1155–1158.
Crossref
[71]

A. Angel, N. Koudas, N. Sarkas, D. Srivastava, M. Svendsen, and S. Tirthapura, Dense subgraph maintenance under streaming edge weight updates for real-time story identification, VLDB J., vol. 23, no. 2, pp. 175–199, 2014.
Crossref Google Scholar
[72]
P. Soucy and G. W. Mineau, Beyond TFIDF weighting for text categorization in the vector space model, in Proc. 19 th Int. Joint Conf. on Artificial Intelligence, Edinburgh, UK, 2005, pp. 1130–1135.
[73]

S. B. Kaleel and A. Abhari, Cluster-discovery of Twitter messages for event detection and trending, J. Comput. Sci., vol. 6, pp. 47–57, 2015.
Crossref Google Scholar
[74]
R. Giovanetti and L. Lancieri, Model of computer architecture for online social networks flexible data analysis: The case of Twitter data, in Proc. 2016 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, San Francisco, CA, USA, 2016, pp. 677–684.
Crossref
[75]

J. Wang, L. Xia, X. Hu, and Y. Xiao, Abnormal event detection with semi-supervised sparse topic model, Neural Comput. Appl., vol. 31, no. 5, pp. 1607–1617, 2019.
Crossref Google Scholar
[76]
S. Liu and P. Jansson, City event detection from social media with neural embeddings and topic model visualization, in Proc. 2017 IEEE Int. Conf. on Big Data, Boston, MA, USA, 2017, pp. 4111–4116.
Crossref
[77]
X. Sun, Y. Wu, L. Liu, and J. Panneerselvam, Efficient event detection in social media data streams, in Proc. 2015 IEEE Int. Conf. on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Liverpool, UK, 2015, pp. 1711–1717.
Crossref
[78]

T. Hofmann, Unsupervised learning by probabilistic latent semantic analysis, Mach. Learn., vol. 42, nos. 1&2, pp. 177–196, 2001.
Crossref Google Scholar
[79]

D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.
Google Scholar
[80]
X. Yan, J. Guo, Y. Lan, and X. Cheng, A biterm topic model for short texts, in Proc. 22 nd Int. Conf. on World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 1445–1456.
Crossref
[81]
Y. Pan, J. Yin, S. Liu, and J. Li, A biterm-based dirichlet process topic model for short texts, in Proc. 3 rd Int. Conf. on Computer Science and Service System. doi: 10.2991/csss-14.2014.71.
Crossref
[82]
J. Li, Z. Tai, R. Zhang, W. Yu, and L. Liu, Online bursty event detection from microblog, in Proc. IEEE/ACM 7 th Int. Conf. on Utility and Cloud Computing, London, UK, 2014, pp. 865–870.
Crossref
[83]
J. Chen, Q. Shang, and H. Xiong, Hot events detection for Chinese microblogs based on the TH-LDA model, in Proc. 2018 Int. Conf. on Transportation & Logistics, Information & Communication, Smart City, Chengdu, China, 2018, pp. 157–166.
Crossref
[84]
J. Yu and L. Qiu, ULW-DMM: An effective topic modeling method for microblog short text, IEEE Access, vol. 7, pp. 884–893, 2019.
Crossref
[85]

S. Jiang, X. Qian, J. Shen, Y. Fu, and T. Mei, Author topic model-based collaborative filtering for personalized POI recommendations, IEEE Trans. Multimed., vol. 17, no. 6, pp. 907–918, 2015.
Crossref Google Scholar
[86]
D. Gunawan, R. F. Rahmat, A. Putra, and M. F. Pasha, Filtering spam text messages by using Twitter-LDA algorithm, in Proc. 2018 IEEE Int. Conf. on Communication, Networks and Satellite, Medan, Indonesia, 2018, pp. 1–6.
Crossref
[87]

M. Gupta and P. Gupta, Research and implementation of event extraction from Twitter using LDA and scoring function, Int. J. Inf. Technol., vol. 11, no. 2, pp. 365–371, 2019.
Crossref Google Scholar
[88]

D. Nolasco and J. Oliveira, Subevents detection through topic modeling in social media posts, Future Gener. Comput. Syst., vol. 93, pp. 290–303, 2019.
Crossref Google Scholar
[89]
X. Tan, G. Deng, and X. Hu, Multi-granularity context semantic fusion model for Chinese event detection, in Proc. 10 th Int. Conf. on Internet Computing for Science and Engineering, Guilin, China, 2021, pp. 1–7.
Crossref
[90]

H. Yin, J. Cao, Y. Du, G. Wang, L. Cao, X. Wang, and H. Zhang, Chinese abrupt event recognition based on CBiGRU-ATT model, IOP Conf. Ser.: Mater. Sci. Eng., vol. 782, no. 5, p. 052043, 2020.
Crossref Google Scholar
[91]
J. Araki and T. Mitamura, Open-domain event detection using distant supervision, in Proc. 27 th Int. Conf. on Computational Linguistics, Santa Fe, NM, USA, 2018, pp. 878–891.
[92]
L. Huang and H. Ji, Semi-supervised new event type induction and event detection, in Proc. 2020 Conf. on Empirical Methods in Natural Language Processing. doi: 10.18653/v1/2020.emnlp-main.53.
Crossref
[93]
Y. Hong, W. Zhou, J. Zhang, G. Zhou, and Q. Zhu, Self-regulation: Employing a generative adversarial network to improve event detection, in Proc. 56 th Annu. Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018, pp. 515–526.
Crossref
[94]
X. Wang, X. Han, Z. Liu, M. Sun, and P. Li, Adversarial training for weakly supervised event detection, in Proc. 2019 Conf. of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, Minneapolis, MN, USA, 2019, pp. 998–1008.
Crossref
[95]

C. Rossi, F. S. Acerbo, K. Ylinen, I. Juga, P. Nurmi, A. Bosca, F. Tarasconi, M. Cristoforetti, and A. Alikadic, Early detection and information extraction for weather-induced floods using social media streams, Int. J. Disast. Risk Reduct., vol. 30, pp. 145–157, 2018.
Crossref Google Scholar
[96]

K. Xie, G. Di Tosto, L. Lu, and Y. S. Cho, Detecting leadership in peer-moderated online collaborative learning through text mining and social network analysis, Internet High. Educ., vol. 38, pp. 9–17, 2018.
Crossref Google Scholar
[97]

S. Dabiri and K. Heaslip, Developing a Twitter-based traffic event detection model using deep learning architectures, Expert Syst. Appl., vol. 118, pp. 425–439, 2019.
Crossref Google Scholar
[98]

W. Cui, P. Wang, Y. Du, X. Chen, D. Guo, J. Li, and Z. Zhou, An algorithm for event detection based on social media data, Neurocomputing, vol. 254, pp. 53–58, 2017.
Crossref Google Scholar
[99]

H. Abdelhaq, M. Gertz, and A. Armiti, Efficient online extraction of keywords for localized events in Twitter, GeoInformatica, vol. 21, no. 2, pp. 365–388, 2017.
Crossref Google Scholar
[100]

N. Alsaedi, P. Burnap, and O. Rana, Can we predict a riot? Disruptive event detection using Twitter, ACM Trans. Internet Technol., vol. 17, no. 2, p. 18, 2017.
Crossref Google Scholar
[101]
W. Hu, H. Wang, C. Peng, H. Liang, and B. Du, RETRACTED: An event detection method for social networks based on link prediction, Inf. Syst., vol. 71, pp. 16–26, 2017.
Crossref
[102]

L. Shi, Y. Wu, L. Liu, X. Sun, and L. Jiang, Event detection and identification of influential spreaders in social media data streams, Big Data Mining and Analytics., vol. 1, no. 1, pp. 34–46, 2018.
Crossref Google Scholar
[103]
D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the spread of influence through a social network, in Proc. 9 th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Washington, DC, USA, 2003, pp. 137–146.
Crossref
[104]

H. Liang and K. W. Fu, Network redundancy and information diffusion: The impacts of information redundancy, similarity, and tie strength, Commun. Res., vol. 46, no. 2, pp. 250–272, 2019.
Crossref Google Scholar
[105]

J. Wu, J. Chang, Q. Cao, and C. Liang, A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust, Comput. Ind. Eng., vol. 127, pp. 853–864, 2019.
Crossref Google Scholar
[106]
M. Richardson and P. Domingos, Mining knowledge-sharing sites for viral marketing, in Proc. 8 th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Edmonton, Canada, 2002, pp. 61–70.
Crossref
[107]
J. Yang and J. Leskovec, Modeling information diffusion in implicit networks, in Proc. 2010 IEEE Int. Conf. on Data Mining, Sydney, Australia, 2010, pp. 599–608.
Crossref
[108]
M. Wu, J. Guo, C. Zhang, and J. Xie, Social media communication model research bases on Sina-weibo, in Proc. of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, 2011, pp. 445–454.
Crossref
[109]

F. Xiong, Y. Liu, Z. J. Zhang, J. Zhu, and Y. Zhang, An information diffusion model based on retweeting mechanism for online social media, Phys. Lett. A, vol. 376, nos. 30&31, pp. 2103–2108, 2012.
Crossref Google Scholar
[110]
P. Yang, G. Yang, J. Liu, J. Qi, Y. Yang, X. Wang, and T. Wang, DUAPM: An effective dynamic micro-blogging user activity prediction model towards cyber-physical-social systems, IEEE Trans. Ind. Inf., vol. 16, no. 8, pp. 5317–5326, 2020.
Crossref
[111]
S. Karthika and R. Geetha, Communalyzer— understanding life cycle of community in social networks, in Innovations in Computer Science and Engineering, H. S. Saini, R. Sayal, A. Govardhan, and R. Buyya, eds. Singapore: Springer, 2019, pp. 197–204.
Crossref
[112]

M. J. Kim, C. K. Lee, and N. S. Contractor, Seniors’ usage of mobile social network sites: Applying theories of innovation diffusion and uses and gratifications, Comput. Hum. Behav., vol. 90, pp. 60–73, 2019.
Crossref Google Scholar
[113]
D. Bhattacharya and S. Ram, Sharing news articles using 140 characters: A diffusion analysis on Twitter, in Proc. 2012 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining, Istanbul, Turkey, 2012, pp. 966–971.
Crossref
[114]
E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic, The role of social networks in information diffusion, in Proc. 21 st Int. Conf. on World Wide Web, Lyon, France, 2012, pp. 519–528.
Crossref
[115]

J. S. More and C. Lingam, A gradient-based methodology for optimizing time for influence diffusion in social networks, Soc. Netw. Anal. Min., vol. 9, no. 1, p. 5, 2019.
Crossref Google Scholar
[116]

L. Yang, J. Wang, C. Gao, and T. Li, A crisis information propagation model based on a competitive relation, J. Ambient Intell. Hum. Comput., vol. 10, pp. 2999–3009, 2019.
Crossref Google Scholar
[117]
H. Kwak, C. Lee, H. Park, and S. Moon, What is Twitter, a social network or a news media? in Proc. 19 th Int. Conf. on World Wide Web, Raleigh, NC, USA, 2010, pp. 591–600.
Crossref
[118]

R. Ghebleh, A comparative classification of information dissemination approaches in vehicular ad hoc networks from distinctive viewpoints: A survey, Comput. Netw., vol. 131, pp. 15–37, 2018.
Crossref Google Scholar
[119]
Z. Tan, D. Wu, T. Gao, I. You, and V. Sharma, AIM: Activation increment minimization strategy for preventing bad information diffusion in OSNs, Future Gener. Comput. Syst., vol. 94, pp. 293–301, 2019.
Crossref
[120]

A. Bovet and H. A. Makse, Influence of fake news in Twitter during the 2016 US presidential election, Nat. Commun., vol. 10, no. 1, p. 7, 2019.
Crossref Google Scholar
[121]
S. Vieweg, A. L. Hughes, K. Starbird, and L. Palen, Microblogging during two natural hazards events: What Twitter may contribute to situational awareness, in Proc. SIGCHI Conf. on Human Factors in Computing Systems, Atlanta, GA, USA, 2010, pp. 1079–1088.
Crossref
[122]
Y. Qu, C. Huang, P. Zhang, and J. Zhang, Microblogging after a major disaster in China: A case study of the 2010 Yushu earthquake, in Proc. ACM 2011 Conf. on Computer Supported Cooperative Work, Hangzhou, China, 2011, pp. 25–34.
Crossref
[123]
Y. Zhang, J. Zhou, and J. Cheng, Preference-based top-K influential nodes mining in social networks, in Proc. IEEE 10 th Int. Conf. on Trust, Security and Privacy in Computing and Communications, Changsha, China, 2011, pp. 1512–1518.
Crossref
[124]
D. Varshney, S. Kumar, and V. Gupta, Modeling information diffusion in social networks using latent topic information, in Proc. 10 th Int. Conf. on Intelligent Computing, Taiyuan, China, 2014, pp. 137–148.
Crossref
[125]

S. Pei, L. Muchnik, J. S. Andrade Jr, Z. Zheng, and H. A. Makse, Searching for superspreaders of information in real-world social media, Sci. Rep., vol. 4, no. 1, p. 5547, 2014.
Crossref Google Scholar
[126]

Q. Xuan, X. Shu, Z. Ruan, J. Wang, C. Fu, and G. Chen, A self-learning information diffusion model for smart social networks, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 3, pp. 1466–1480, 2020.
Crossref Google Scholar
[127]

N. Dakiche, F. B. S. Tayeb, Y. Slimani, and K. Benatchba, Tracking community evolution in social networks: A survey, Inf. Process. Manag., vol. 56, no. 3, pp. 1084–1102, 2019.
Crossref Google Scholar
[128]
Z. Wang and Y. Guo, Rumor events detection enhanced by encoding sentimental information into time series division and word representations, Neurocomputing, vol. 397, pp. 224–243, 2020.
Crossref
[129]
F. Figueiredo, F. Benevenuto, and J. M. Almeida, The tube over time: Characterizing popularity growth of youtube videos, in Proc. 4 th ACM Int. Conf. on Web Search and Data Mining, Hong Kong, China 2011, pp. 745–754.
Crossref
[130]

C. C. Yang, X. Shi, and C. P. Wei, Discovering event evolution graphs from news corpora, IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Hum., vol. 39, no. 4, pp. 850–863, 2009.
Crossref Google Scholar
[131]
B. Suh, L. Hong, P. Pirolli, and E. H. Chi, Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network, in Proc. IEEE 2 nd Int. Conf. on Social Computing, Minneapolis, MN, USA, 2010, pp. 177–184.
Crossref
[132]
Y. Artzi, P. Pantel, and M. Gamon, Predicting responses to microblog posts, in Proc. 2012 Conf. of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, Montréal, Canada, 2012, pp. 602–606.
[133]

M. Hasan, M. A. Orgun, and R. Schwitter, A survey on real-time event detection from the Twitter data stream, J. Inf. Sci., vol. 44, no. 4, pp. 443–463, 2018.
Crossref Google Scholar
[134]
S. Dutta, A. K. Das, G. Dutta, and M. Gupta, A comparative study on cluster analysis of microblogging data, in Emerging Technologies in Data Mining and Information Security, A. Abraham, P. Dutta, J. K. Mandal, A. Bhattacharya, and S. Dutta, eds. Singapore: Springer, 2019, pp. 873–881.
Crossref
[135]

H. J. Choi and C. H. Park, Emerging topic detection in twitter stream based on high utility pattern mining, Expert Syst. Appl., vol. 115, pp. 27–36, 2019.
Crossref Google Scholar
[136]
R. Nallapati, A. Feng, F. Peng, and J. Allan, Event threading within news topics, in Proc. 13 th ACM Int. Conf. on Information and Knowledge Management, Washington, DC, USA, 2004, pp. 446–453.
Crossref
[137]
C. Lin, C. Lin, J. Li, D. Wang, Y. Chen, and T. Li, Generating event storylines from microblogs, in Proc. 21 st ACM Int. Conf. on Information and Knowledge Management, Maui, HA, USA, 2012, pp. 175–184.
Crossref
[138]
S. Mishra, Bridging models for popularity prediction on social media, in Proc. 12 th ACM Int. Conf. on Web Search and Data Mining, Melbourne, Australia, 2019, pp. 810–811.
Crossref
[139]

D. R. Liu, H. Omar, C. H. Liou, H. C. Chi, and C. H. Hsu, Recommending blog articles based on popular event trend analysis, Inf. Sci., vol. 305, pp. 302–319, 2015.
Crossref Google Scholar
[140]

D. Lin, B. Ma, M. Jiang, N. Xiong, K. Lin, and D. Cao, Social network rumor diffusion predication based on equal responsibility game model, IEEE Access, vol. 7, pp. 4478–4486, 2018.
Crossref Google Scholar
Big Data Mining and Analytics
Volume 7 Issue 1,
March 2024
Pages 217-243
DOI: 10.26599/BDMA.2023.9020021
Cite this article:
Han Z, Shi L, Liu L, et al. A Survey on Event Tracking in Social Media Data Streams. Big Data Mining and Analytics, 2024, 7(1): 217-243. https://doi.org/10.26599/BDMA.2023.9020021

729

Views

107

Downloads

2

Crossref

3

Web of Science

2

Scopus

0

CSCD

Altmetrics
Received: 03 November 2022
Revised: 24 April 2023
Accepted: 11 August 2023
Published: 25 December 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return