Novel Hate Speech Detection Using Word Cloud Visualization and Ensemble Learning Coupled with Count Vectorizer
Source abstract
Abstract
A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hate Speech and Cyberbullying Detection · Spam and Phishing Detection · Internet Traffic Analysis and Secure E-voting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
Funding
Institutional Fund Project
References (46)
Show source-provided references
1. An ensemble method for radicalization and hate speech detection online empowered by sentic computing10.1007/s12559-021-09845-6 ↗
2. MacAvaney, S., Yao, H.R., Yang, E., Russell, K., Goharian, N., and Frieder, O. (2019). Hate speech detection: Challenges and solutions. PLoS ONE, 14.10.1371/journal.pone.0221152 ↗
3. Hajibabaee, P., Malekzadeh, M., Ahmadi, M., Heidari, M., Esmaeilzadeh, A., Abdolazimi, R., and James, H. (2022, January 26–29). Offensive language detection on social media based on text classification. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.10.1109/CCWC54503.2022.9720804 ↗
4. Machine Learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection10.1016/j.ipm.2021.102600 ↗
5. Van Hee, C., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., De Pauw, G., Daelemans, W., and Hoste, V. (2015, January 1–3). Detection and fine-grained classification of cyberbullying events. Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP, Online.
6. Shekhar, S., Garg, H., Agrawal, R., Shivani, S., and Sharma, B. (2021). Hatred and trolling detection transliteration framework using hierarchical LSTM in code-mixed social media text. Complex Intell. Syst., 1–14.10.1007/s40747-021-00487-7 ↗
7. Mihaylova, T., Gencheva, P., Boyanov, M., Yovcheva, I., Mihaylov, T., Hardalov, M., Kiprov, Y., Balchev, D., Koychev, I., and Nikolova, I. (2021). SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering. arXiv.10.18653/v1/S16-1129 ↗
8. Alnazzawi, N. (2022). Using Twitter to Detect Hate Crimes and Their Motivations: The HateMotiv Corpus. Data, 7.10.3390/data7060069 ↗
9. Gambäck, B., and Sikdar, U.K. (2017, January 4). Using convolutional neural networks to classify hate-speech. Proceedings of the First Workshop on Abusive Language Online, Vancouver, BC, Canada.10.18653/v1/W17-3013 ↗
10. Schmidt, A., and Wiegand, M. (2017, January 3). A survey on hate speech detection using natural language processing. Proceedings of the International Workshop on Natural Language Processing for Social Media, SocialNLP, ACL, Valencia, Spain.10.18653/v1/W17-1101 ↗
11. Greevy, E., and Smeaton, A.F. (2004, January 25–29). Classifying racist texts using a support vector machine. Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Sheffield, UK.10.1145/1008992.1009074 ↗
12. Alkomah, F., and Ma, X. (2022). A Literature Review of Textual Hate Speech Detection Methods and Datasets. Information, 13.10.14569/IJACSA.2022.01308100 ↗
13. Automatic Hate speech Detection using Machine Learning: A Comparative Study
14. Diao, S., Xu, R., Su, H., Jiang, Y., Song, Y., and Zhang, T. (2021;, January 1–6). Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Virtual Event.10.18653/v1/2021.acl-long.259 ↗
15. Combating hate speech using an adaptive ensemble learning model with a case study on COVID-1910.1016/j.eswa.2021.115632 ↗
16. Aggression detection through deep neural model on twitter10.1016/j.future.2020.07.050 ↗
17. Data expansion using back translation and paraphrasing for hate speech detection10.1016/j.osnem.2021.100153 ↗
18. Arabic Questions Classification Using Modified TF-IDF10.1109/ACCESS.2021.3094115 ↗
19. Ceasing hate with MoH: Hate Speech Detection in Hindi–English code-switched language10.1016/j.ipm.2021.102760 ↗
20. A Framework for Hate speech Detection Using Deep Convolutional Neural Network10.1109/ACCESS.2020.3037073 ↗
21. Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network10.1016/j.chb.2016.05.051 ↗
22. Mohapatra, S.K., Prasad, S., Bebarta, D.K., Das, T.K., Srinivasan, K., and Hu, Y.C. (2021). Automatic Hate speech Detection in English-Odia Code Mixed Social Media Data Using Machine Learning Techniques. Appl. Sci., 11.10.3390/app11188575 ↗
23. Bagging predictors10.1007/BF00058655 ↗
24. Zisad, S.N., Hossain, M.S., and Andersson, K. (2020, January 19). Speech emotion recognition in neurological disorders using convolutional neural network. Proceedings of the International Conference on Brain Informatics, Padua, Italy.10.1007/978-3-030-59277-6_26 ↗
25. Goel, K., Rajani, N., Vig, J., Tan, S., Wu, J., Zheng, S., Xiong, C., Bansal, M., and Ré, C. (2021). Robustness gym: Unifying the nlp evaluation land-scape. arXiv.10.18653/v1/2021.naacl-demos.6 ↗
26. Thakur, N., Reimers, N., Daxenberger, J., and Gurevych, I. (2020). Augmented sbert: Data augmentation method for improv-ing bi-encoders for pairwise sentence scoring tasks. arXiv.10.18653/v1/2021.naacl-main.28 ↗
27. Ciolino, M., Noever, D., and Kalin, J. (2021). Multilingual Augmenter: The Model Chooses. arXiv.
28. Hu, Z., Jiang, Y., Bach, N., Wang, T., Huang, Z., Huang, F., and Tu, K. (2021;, January 1–6). Multi-View Cross-Lingual Structured Prediction with Minimum Supervision. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Virtual Event.10.18653/v1/2021.acl-long.207 ↗
29. Gao, Y., Zhu, H., Ng, P., Santos CN, D., Wang, Z., Nan, F., Zhang, D., Nallapati, R., Arnold, A.O., and Xiang, B. (2020). Answering ambiguous questions through generative evidence fusion and round-trip prediction. arXiv.10.18653/v1/2021.acl-long.253 ↗
30. William, P., Gade, R., esh Chaudhari, R., Pawar, A.B., and Jawale, M.A. (2022, January 7–9). Machine Learning based Automatic Hate Speech Recognition System. Proceedings of the 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India.10.1109/ICSCDS53736.2022.9760959 ↗
31. Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA10.1016/j.asoc.2020.107057 ↗
32. On the evaluation and combination of state- of-the-art features in twitter sentiment analysis10.1007/s10462-020-09895-6 ↗
33. Singh, C., Imam, T., Wibowo, S., and Grandhi, S. (2022). A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews. Appl. Sci., 12.10.3390/app12083709 ↗
34. Daghriri, T., Proctor, M., and Matthews, S. (2022). Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph19063230 ↗
35. Gorrell, G., Kochkina, E., Liakata, M., Aker, A., Zubiaga, A., Bontcheva, K., and Derczynski, L. (2019, January 6–7). SemEval-2019 task 7: RumourEval, determining rumour veracity and support for rumours. Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, MN, USA.10.18653/v1/S19-2147 ↗
36. A probabilistic clustering model for hate speech classification in twitter10.1016/j.eswa.2021.114762 ↗
37. Vel, S.S. (2021, January 25–27). Pre-Processing techniques of Text Mining using Computational Linguistics and Python Libraries. Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India.
38. Ho, T.K. (1995, January 14–16). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recogni-tion, Montreal, QC, Canada.
39. Gholizadeh, S. (2022). Top Popular Python Libraries in Research, ResearchGate. Authorea Preprints.10.22541/au.164580055.55493761/v1 ↗
40. Pajankar, A., and Joshi, A. (2022). Introduction to Pandas. Hands-on Machine Learning with Python, Apress.10.1007/978-1-4842-7921-2 ↗
41. Jokić, D., Stanković, R., Krstev, C., and Šandrih, B. (2021, January 1–4). A Twitter Corpus and lexicon for abusive speech detection in Serbian. Proceedings of the 3rd Conference on Language, Data and Knowledge (LDK 2021), Zaragoza, Spain.
42. A multilingual evaluation for online hate speech detection10.1145/3377323 ↗
43. Waseem, Z. (2016, January 5). Are you a racist or am I seeing things? Annotator influence on hate speech detection on twitter. Proceedings of the First Workshop on NLP and Computational Social Science, Austin, TX, USA.10.18653/v1/W16-5618 ↗
44. HCovBi-caps: Hate speech detection using convolutional and Bi-directional gated recurrent unit with Capsule network10.1109/ACCESS.2022.3143799 ↗
45. He, B., Ziems, C., Soni, S., Ramakrishnan, N., Yang, D., and Kumar, S. (2021, January 8–11). Racism is a virus: Anti-asian hate and counterspeech in social media during the COVID-19 crisis. Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Virtual Event, The Netherlands.10.1145/3487351.3488324 ↗
46. Schapire, R.E. (2013). Explaining adaboost. Empirical Inference, Springer.10.1007/978-3-642-41136-6_5 ↗