Detecting Synchronized Hate Speech in Online Social Networks via Social Synchrony and Ant Colony Optimization
Subject Areas : IT Strategy
Shabana Nargis Rasool
1
,
Sarika Jain
2
*
,
Ajay Vikram Singh
3
1 - Department of Computer Science, Islamic University of Science and Technology, Kashmir, India
2 - Amity Institute of Information Technology, Amity University Noida, Uttar Pradesh, India
3 - Department of Computer Science, Islamic University of Science and Technology, Kashmir, India
Keywords: Hate Speech, Social Synchrony, Ant Colony Optimization, Feature Selection, Online Social Networks,
Abstract :
Online platforms have become fertile grounds for hate speech, often spreading through bursts of coordinated user activity. Detecting such patterns requires more than analyzing individual posts, as it calls for understanding the collective rhythm of online interactions. In the present study, we present SIACO (Social Synchrony Identification using Ant Colony Optimization), a nature-inspired framework that detects hate-speech events by tracing synchrony in user behaviour. SIACO models how hateful expressions emerge and fade collectively, using Ant Colony Optimization to refine linguistic features and improve classification accuracy. Upon evaluation on a Twitter dataset, the framework consistently outperforms both traditional machine learning models and transformer-based baselines, achieving up to a 10% improvement across major evaluation metrics. The framework also offers interpretable insights into the linguistic and temporal cues driving coordinated hate. The performance scores obtained highlight the value of looking at hate speech not just as text, but as a social phenomenon unfolding in synchrony.
[1] S. Bausch and M. McGiboney, “Nielsen online report social networks & blogs now 4th most popular online activity,” https://www.nielsen.com, 2009, accessed Jan. 2023.
[2] J. Weng and B.-S. Lee, “Event detection in twitter,” in Proceedings of the International AAAI Conference on Web and social media, vol. 5, pp. 401–408, 2011.
[3] V. Müller and U. Lindenberger,“Cardiac and respirato- ry patterns synchronize between persons during choir singing,” PLOS ONE, vol. 6, no. 9 , p.e24893, 2011.
[4] Z. Néda, E. Ravasz, Y. Brechet, T. Vicsek, and A.-L. Barabási, “The sound of many hands clapping,” Nat- ure, vol. 403, no. 6772, pp. 849–850, 2000.
[5] M. Abdul Jawad and F. Khursheed, “Deep and dense convolutional neural network for multi category classification of magnification specific and magnification independent breast cancer histopathological images,” Biomedical Signal Processing and Control, vol. 78, p. 103935, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S1746809422004372.
[6] Q. Xuan and V. Filkov, “Synchrony in social groups and its benefits,” in Handbook of Human Computati- on, 2013, pp. 791–802.
[7] A. Mesaros, T. Heittola, T. Virtanen, and M. D. Plumbley, “Sound event detection: A tutorial,” IEEE Signal Processing Magazine, vol. 38, no. 5, pp. 67–83, 2021.
[8] A. Srinivasulu, S. Mohan, H. T, S. P, and R. Y, “Apnea event detection using machine learning technique for the linical diagnosis of sleep apnea syndrome,” in Proceedings of the 3rd International Conference on Signal Processing and Communication (ICPSC), 2021, pp. 490–493.
[9] M. Abdul Jawad and F. Khursheed, “A novel approach for color-balanced reference image selection for breast histology image normalization,” Biomedical Signal Processing and Control, vol. 94, p. 106299, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1746809424003574.
[10] M. D. Choudhury, H. Sundaram, A. John, and D. D. Seligmann, “Social synchrony: Predicting mimicry of user actions in online social media,” in 2009 International Conference on Computational Science and Engineering, vol. 4. IEEE, 2009, pp. 151–158.
[11] F. Benevenuto, T. Rodrigues, M. Cha, and V. Almeida, “Characterizing user behavior in online social networks,” in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, 2009, pp. 49–62.
[12] R. A. Rossi, B. Gallagher, J. Neville, and K.Henderson“Modeling dynamic behavior in large evolving graphs,” in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, 2013, pp. 667–676.
[13] N. D. Rodríguez, M. P. Cuéllar, J. Lilius, and M. D. Calvo-Flores, “A fuzzy ontology for semantic modelling and recognition of human behaviour,” Knowledge-Based Systems, vol. 66, pp. 46–60, 2014.
[14] Natalia Díaz Rodríguez, M. P. Cuéllar, Johan Lilius, and Miguel Delgado Calvo-Flores, “A survey on ontologies for human behavior recognition,” ACM Computing Surveys (CSUR), vol. 46, no. 4, pp. 1–33, 2014.
[15] M. Weskida and R. Michalski, “Finding influentials in social networks using evolutionary algorithm,” Journal of Computational Science, vol. 31, pp. 77–85, 2019.
[16].J. Zhao, J. Wu, X. Feng, H. Xiong, and K. Xu, “Information propagation in online social networks: A tie-strength perspective,” Knowledge and Information Systems, vol. 32, pp. 589–608, 2012.
[17] P. Cordero, M. Enciso, A. Mora, M. Ojeda-Aciego, and C. Rossi, “Knowledge discovery in social networks by using a logic-based treatment of implications,” Knowledge-Based Systems, vol. 87, pp. 16–25, 2015.
[18] B. A. Huberman, D. M. Romero, and F. Wu, “Social networks that matter: Twitter under the microscope,” arXiv preprint rXiv:0812.1045, 2008.
[19].F. Alderisio, M. Lombardi, G. Fiore, and M. di Bernardo, “Study of movement coordination in human ensembles via a novel computer-based set-up,” arXiv preprint arXiv:1608.04652, 2016.
[20].C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, “Limits of predictability in human mobility,” Science, vol. 327, no. 5968, pp. 1018–1021, 2010.
[21].C. Li, A. Sun, and A. Datta, “Twevent: Segment-based event detection from tweets,” in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012, pp. 155–164.
[22] F. Alvanaki, M. Sebastian, K. Ramamritham, and G. Weikum, “Enblogue: Emergent topic detection in web 2.0 streams,” in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, 2011, pp. 1271–1274.
[23] J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst, “Cascading behavior in large blog graphs: Patterns and a model,” in Society of Applied and Industrial Mathematics: Data Mining, 2007, pp. 551–556.
[24] G. Petkos, S. Papadopoulos, L. Aiello, R. Skraba, and Y. Kompatsiaris, “A soft frequent pattern mining approach for textual topic detection,” in Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14), 2014, pp. 1–10.
[25].S. Gaglio, G. L. Re, and M. Morana, “Real-time detection of twitter social events from the user’s perspective,” in 2015 IEEE International Conference on Communications (ICC). IEEE, 2015, pp. 1207–1212.
[26] O. Ozdikis, P. Senkul, and H. Oguztuzun, “Semantic expansion of tweet contents for enhanced event detection in twitter,” in 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2012, pp. 20–24.
[27].Kaggle, “Twitter dataset,” https://www.kaggle.com/datasets, 2023, accessed Jan. 2023.
[28] H. S. Alatawi, A. Alhothali, and K. Moria, “Detection of hate speech using BERT and hate speech word embedding with deep model,” CoRR, vol. abs/2111.01515, 2021. [Online]. Available: https://arxiv.org/abs/2111.01515.
[29] Rasool, S.N., Jain, S. & Moon, A.H. Detection of seed users vis-à-vis social synchrony in online social networks using graph analysis. Int. J. Inf. Tech., 15, 3715–3726 (2023). https://doi.org/10.1007/s41870-023-01435-z.