Intelligent Multi-Lingual Cyber-Hate Detection in Online Social Networks: Taxonomy, Approaches, Datasets, and Open Challenges
Open accessAbstract
Sentiment Analysis, also known as opinion mining, is the area of Natural Language Processing that aims to extract human perceptions, thoughts, and beliefs from unstructured textual content. It has become a useful, attractive, and challenging research area concerning the emergence and rise of social media and the mass volume of individuals’ reviews, comments, and feedback. One of the major problems, apparent and evident in social media, is the toxic online textual content. People from diverse cultural backgrounds and beliefs access Internet sites, concealing and disguising their identity under a cloud of anonymity. Due to users’ freedom and anonymity, as well as a lack of regulation governed by social media, cyber toxicity and bullying speech are major issues that need an automated system to be detected and prevented. There is diverse research in different languages and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this manuscript, a comprehensive multi-lingual and systematic review of cyber-hate sentiment analysis is presented. It states the definition, properties, and taxonomy of cyberbullying and how often each type occurs. In addition, it presents the most recent popular cyberbullying benchmark datasets in different languages, showing their number of classes (Binary/Multiple), discussing the applied algorithms, and how they were evaluated. It also provides the challenges, solutions, as well as future directions.
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Twitter provides a vast amount of data due to its large user base, resulting in a significant number of active users worldwide.
“Moreover, Twitter provides a vast amount of data due to its large user base, resulting in a significant number of active users worldwide [9,131,132].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [132]
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Cites
Automated Hate Speech Detection and the Problem of Offensive Language
Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14955 · Cited by 2,610 · see citing works
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
Talat, Z., & Hovy, D. (2016) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/n16-2013 · Cited by 1,919 · see citing works
Deep Learning for Hate Speech Detection in Tweets
Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3041021.3054223 · Cited by 1,253 · see citing works
SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter
Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Pardo, F. M. R., Rosso, P., & Sanguinetti, M. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2007 · Cited by 889 · see citing works
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841 · see citing works
A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020) · Unsupervised and Semi-Supervised Learning · doi:10.1007/978-3-030-22475-2_1 · Cited by 702 · see citing works
Ex Machina: Personal Attacks Seen at Scale
Wulczyn, E., Thain, N., & Dixon, L. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052591 · Cited by 636 · see citing works
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
Founta, A.-M., Djouvas, C., Chatzakou, D., Ilias Leontiadis, A., Blackburn, J., Stringhini, G., Vakali, A. I., Sirivianos, M., & Kourtellis, N. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.14991 · Cited by 605 · see citing works
Watanabe, H., Bouazizi, M., & Ohtsuki, T. O. (2018) · IEEE Access · doi:10.1109/access.2018.2806394 · Cited by 470 · see citing works
Using Machine Learning to Detect Cyberbullying
Reynolds, K., Kontostathis, A., & Edwards, L. (2011) · doi:10.1109/icmla.2011.152 · Cited by 462 · see citing works
Hate Speech Dataset from a White Supremacy Forum
de Gibert, O., Pérez, N., Pablos, A. G., & Cuadros, M. (2018) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w18-5102 · Cited by 428 · see citing works
One-step and Two-step Classification for Abusive Language Detection on Twitter
Park, J. H., & Fung, P. (2017) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w17-3006 · Cited by 383 · see citing works
Al-Garadi, M. A., Varathan, K. D., & Ravana, S. D. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.051 · Cited by 354 · see citing works
Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter
Ibrohim, M. O., & Budi, I. D. (2019) · doi:10.18653/v1/w19-3506 · Cited by 244 · see citing works
Automatic detection of cyberbullying on social networks based on bullying features
Zhao, R., Zhou, A., & Mao, K. (2016) · International Conference on Distributed Computing and Networking (ICDCN) · doi:10.1145/2833312.2849567 · Cited by 243 · see citing works
Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere
Albadi, N., Kurdi, M., & Mishra, S. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508247 · Cited by 239 · see citing works
A Framework for Hate Speech Detection Using Deep Convolutional Neural Network
Roy, P. K., Tripathy, A. K., Das, T. K., & Gao, X. (2020) · IEEE Access · doi:10.1109/access.2020.3037073 · Cited by 205 · see citing works
L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
Mulki, H., Haddad, H., Ali, C. B., & Alshabani, H. (2019) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w19-3512 · Cited by 194 · see citing works
A Multilingual Evaluation for Online Hate Speech Detection
Corazza, M., Menini, S., Cabrio, E., Tonelli, S., & Villata, S. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3377323 · Cited by 172 · see citing works
Lexicon-enhanced sentiment analysis framework using rule-based classification scheme
Asghar, M. Z., Khan, A., Ahmad, S., Qasim, M., & Khan, I. A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171649 · Cited by 168 · see citing works
A survey on sentiment analysis and opinion mining for social multimedia
Li, Z., Fan, Y., Jiang, B., Lei, T., & Liu, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6445-z · Cited by 164 · see citing works
Overview of the EVALITA 2018 Hate Speech Detection Task
Bosco, C., Dell’Orletta⋄, F., Poletto, F., Sanguinetti, M., & Tesconi, M. (2018) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.4503 · Cited by 164 · see citing works
Cyberhate: A review and content analysis of intervention strategies
Blaya, C. (2018) · Aggression and Violent Behavior · doi:10.1016/j.avb.2018.05.006 · Cited by 159 · see citing works
Aggression detection through deep neural model on Twitter
Sadiq, S., Mehmood, A., Ullah, S., Ahmad, M., Choi, G. S., & On, B.-W. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.050 · Cited by 156 · see citing works
Detection and Fine-Grained Classification of Cyberbullying Events
Hee, C. V., Lefever, E., Verhoeven, B., Mennes, J., Desmet, B., Pauw, G. D., Daelemans, W. M. P., & Hoste, V. (2015) · Ghent University Academic Bibliography (Ghent University) · Cited by 154 · see citing works
Alsharif, M. H., Kelechi, A. H., Yahya, K. A., & Chaudhry, S. A. (2020) · Symmetry · doi:10.3390/sym12010088 · Cited by 139 · see citing works
Peled, Y. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e01393 · Cited by 132 · see citing works
Revisiting Semi-Supervised Learning for Online Deceptive Review Detection
Rout, J. K., Dalmia, A., Choo, K. R., Bakshi, S., & Jena, S. K. (2017) · IEEE Access · doi:10.1109/access.2017.2655032 · Cited by 130 · see citing works
Hate Speech Detection in the Bengali Language: A Dataset and Its Baseline Evaluation
Romim, N., Ahmed, M., Talukder, H., & Islam, M. S. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-16-0586-4_37 · Cited by 122 · see citing works
Cyberbullying Detection with a Pronunciation Based Convolutional Neural Network
Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J. P., Kowalski, R., Hu, H., Luo, F., Macbeth, J., & Dillon, E. (2016) · 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2016.0132 · Cited by 118 · see citing works
DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language
Karim, M. R., Dey, S. K., Islam, T., Sarker, S., Menon, M. H., Hossain, K., Hossain, M. A., & Decker, S. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564230 · Cited by 114 · see citing works
Lingiardi, V., Carone, N., Semeraro, G., Musto, C., D’Amico, M., & Brena, S. (2020) · Behaviour & Information Technology · doi:10.1080/0144929x.2019.1607903 · Cited by 113 · see citing works
Prediction of cyberbullying incidents in a media-based social network
Hosseinmardi, H., Rafiq, R. I., Han, R. Y., Lv, Q., & Mishra, S. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752233 · Cited by 109 · see citing works
Constructing domain-dependent sentiment dictionary for sentiment analysis
Ahmed, M., Chen, Q., & Li, Z. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04824-8 · Cited by 103 · see citing works
Detection of Cyberbullying Using Deep Neural Network
Banerjee, V., Telavane, J., Gaikwad, P., & Vartak, P. (2019) · 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) · doi:10.1109/icaccs.2019.8728378 · Cited by 98 · see citing works
A Bi-GRU with attention and CapsNet hybrid model for cyberbullying detection on social media
Kumar, A., & Sachdeva, N. (2022) · World Wide Web · doi:10.1007/s11280-021-00920-4 · Cited by 87 · see citing works
Cyberbullying Detection and Classification Using Information Retrieval Algorithm
Nandhini, B. S., & Sheeba, J. I. (2015) · International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET) · doi:10.1145/2743065.2743085 · Cited by 86 · see citing works
BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
Moon, J., Cho, W. I., & Lee, J. (2020) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2020.socialnlp-1.4 · Cited by 80 · see citing works
Collaborative detection of cyberbullying behavior in Twitter data
Mangaonkar, A., Hayrapetian, A., & Raje, R. R. (2015) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit.2015.7293405 · Cited by 76 · see citing works
Lu, N., Wu, G., Zhang, Z., Zheng, Y., Ren, Y., & Choo, K. R. (2020) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5627 · Cited by 76 · see citing works
Multilingual cyberbullying detection system: Detecting cyberbullying in Arabic content
Haidar, B. R., Chamoun, M., & Serhrouchni, A. (2017) · Cyber Security in Networking Conference (CSNet) · doi:10.1109/csnet.2017.8242005 · Cited by 69 · see citing works
A comprehensive study for Arabic Sentiment Analysis (Challenges and Applications)
Alsayat, A., & Elmitwally, N. S. (2019) · Egyptian Informatics Journal · doi:10.1016/j.eij.2019.06.001 · Cited by 68 · see citing works
Detection of Hate Speech and Offensive Language in Twitter Data Using LSTM Model
Bisht, A., Singh, A., Bhadauria, H. S., Virmani, J., & Kriti, N. A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2740-1_17 · Cited by 66 · see citing works
Evaluating deep learning models for sentiment classification
Ay, B., Talo, M., Hallaç, İ. R., & Galip Aydin, B. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4783 · Cited by 64 · see citing works
Creating a WhatsApp Dataset to Study Pre-teen Cyberbullying
Sprugnoli, R., Menini, S., Tonelli, S., Oncini, F., & Piras, E. M. (2018) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w18-5107 · Cited by 54 · see citing works
Cyberbullying detection using probabilistic socio-textual information fusion
Singh, V. K., Huang, Q., & Atrey, P. K. (2016) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2016.7752342 · Cited by 52 · see citing works
A Large-Scale Dataset for Hate Speech Detection on Vietnamese Social Media Texts
Luu, S. T., Nguyen, K. V., & Nguyen, N. L.-T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-79457-6_35 · Cited by 50 · see citing works
Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility
Nurrahmi, H., & Nurjanah, D. (2018) · International Conference on Information and Communications Technology (ICOIACT) · doi:10.1109/icoiact.2018.8350758 · Cited by 47 · see citing works
Weakly Supervised Cyberbullying Detection Using Co-Trained Ensembles of Embedding Models
Raisi, E., & Huang, B. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508240 · Cited by 46 · see citing works
BERT base model for toxic comment analysis on Indonesian social media
Nabiilah, G. Z., Prasetyo, S. Y., Izdihar, Z. N., & Girsang, A. S. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.188 · Cited by 46 · see citing works
Defining Objectives for Preventing Cyberstalking
Dhillon, G. S., & Smith, K. (2017) · Journal of Business Ethics · doi:10.1007/s10551-017-3697-x · Cited by 42 · see citing works
Llinares, F. M., & Rodriguez-Sala, J. J. (2016) · International Journal of Design & Nature and Ecodynamics · doi:10.2495/dne-v11-n3-406-415 · Cited by 39 · see citing works
MTTM: Metamorphic Testing for Textual Content Moderation Software
Wang, W., Huang, J.-T., Wu, W., Zhang, J., Huang, Y., Li, S., He, P., & Lyu, M. R.-T. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00200 · Cited by 34 · see citing works
Khan, F. H., Qamar, U., & Bashir, S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9496-4 · Cited by 31 · see citing works
Instagram-Based Benchmark Dataset for Cyberbullying Detection in Arabic Text
ALBayari, R., & Abdallah, S. (2022) · Data · doi:10.3390/data7070083 · Cited by 30 · see citing works
Cyberbullying Lexicon for Social Media
Hang, O. C., & Dahlan, H. M. (2019) · International Conference on Research and Innovation in Information Systems · doi:10.1109/icriis48246.2019.9073679 · Cited by 26 · see citing works
Modeling Trolling in Social Media ConversationsPreprint
Mojica, L. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.05310 · Cited by 25 · see citing works
L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT modelsPreprint
Velankar, A., Patil, H. G., Gore, A., Salunke, S., & Joshi, R. B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.13778 · Cited by 24 · see citing works
Ptaszynski, M. E., Pieciukiewicz, A., & Dybała, P. (2019) · Jagiellonian University Repository (Jagiellonian University) · Cited by 24 · see citing works
Denigration Bullying Resolution using Wolf Search Optimized Online Reputation Rumour Detection
Sangwan, S. R., & Bhatia, M. P. S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.06.036 · Cited by 20 · see citing works
Sampling Techniques to Overcome Class Imbalance in a Cyberbullying Context
COLTON, D. L., & Hofmann, M. (2019) · Journal of Computer-Assisted Linguistic Research · doi:10.4995/jclr.2019.11112 · Cited by 19 · see citing works
A Turkish hate speech dataset and detection system
Beyhan, F., Carik, B., Arın, İ., Terzioğlu, A., Yanikoglu, B. A., & Yeniterzi, R. (2022) · Sabanci University · Cited by 13 · see citing works
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks
Corazza, M., Menini, S., Arslan, P., Sprugnoli, R., Cabrio, E., Tonelli, S., & Villata, S. (2018) · Oesterreichisches Musiklexikon online (Institut für kunst- und musikhistorische Forschungen der Österreichischen Akademie der Wissenschaften) · doi:10.1553/0x003a105d · Cited by 10 · see citing works
Social Media Influencer and Cyberbullying: A Lesson Learned from Preliminary Findings
Hassan, S., Yacob, M. I., Nguyen, T. T. N., & Zambri, S. (2018) · Universiti Utara Malaysia Institutional Repository (Universiti Utara Malaysia) · Cited by 10 · see citing works
CYBERBULLYING, HAPPINESS, AND STYLE OF HUMOR AMONG PERPETRATORS: IS THERE A RELATIONSHIP?
Qodir, A., Diponegoro, A. M., & Safaria, T. (2019) · Humanities & Social Sciences Reviews · doi:10.18510/hssr.2019.7331 · Cited by 10 · see citing works
Ollagnier, A., Cabrio, E., Villata, S., & Blaya, C. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5pno5frqmrp9 · Cited by 7 · see citing works
Advances in Information and Communication
Arai, K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-28073-3 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 68 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Namdeo, P.; Pateriya, R.K.; Shrivastava, S. A Review of Cyber Bullying Detection in Social Networking. In Proceedings of the Inventive Communication and Computational Technologies, Coimbatore, India, 10-11 March 2017; pp. 162-170.
- Willard, N.E. Cyberbullying and Cyberthreats: Responding to the Challenge of Online Social Aggression, Threats, and Distress; Research Press: Champaign, IL, USA, 2007.
- Bartalesi Lenzi, V.; Moretti, G.; Sprugnoli, R. Cat: The Celct Annotation Tool. In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), Istanbul, Turkey, 21-27 May 2012; pp. 333-338.
- Arai, K., Ed.; Springer Nature: Cham, Switzerland, 2023; pp. 25-38.
- Used a Crowd-Sourced Hate Speech Lexicon to Collect Tweets Containing Hate Speech Keywords. We Use Crowd-Sourcing to Label a Sample of These Tweets into Three Categories: Those Containing Hate Speech, Only Offensive Language, and Those with Neither. Available online: https://arxiv.org/abs/1703.04009 (accessed on 16 February 2023). · arXiv
Cited by
Sorted by citations
Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms
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Padma, P., & Rao, G. S. N. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.623 · Cited by 11 · see citing works
Sustainable Broadband Internet: Current Status and Future Directions
Moghaddam, S. S., & Moghaddam, K. S. (2025) · IEEE Access · doi:10.1109/access.2025.3638684 · Cited by 8 · see citing works
“A pressing challenge is the increasing prevalence of toxic language and cyberbullying, which underscores the need for automated systems capable of detecting, analyzing, and mitigating harmful behavior in real time to promote safer and more responsible online environments [295], [296].”
Shanmugam, S., Gunasekaran, S., & Anusha, N. (2025) · doi:10.1002/9781394268917.ch14 · Cited by 1 · see citing works
Automated Detection of Hate Speech in Hindi using Supervised Learning Models
Rajak, N., Phulre, A. K., Patel, S. C., Patel, N. D., & Khekare, G. S. (2024) · International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) · doi:10.1109/icdscnc62492.2024.10939991
“All written material needs to destroy an individual's peer group or reputation on online social media platforms or in-person communities [16].”
Rodrigues, S., Ruzario, C., Pereira, C., & Tripathy, A. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2299-3_32
Aras, M., Mani, L., Zahra, A., Utama, D. N., & Sahabu, F. C. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026496
“Gamal et al. (2023) provided a broader perspective on multilingual hate speech detection by offering a taxonomy of datasets, methods, and challenges, emphasizing that linguistic and sociocultural diversity must be embedded within detection systems to improve performance.”
Detecting racism in the digital age: a survey of datasets and algorithms
Miqdadi, I. E., Hourri, S., Hayati, A., Namir, Y., Idrysy, F. Z. E., & Kharroubi, J. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09913-z
“…share overlapping linguistic features and social implications (Alam al., 2021; Adaikkan & Thenmozhi, 2024; Al-Nahrain, 2024; Harish et al., 2023; Gamal et al., 2023; Herodotou et al., 2020; Hasan et al., 2023; Mahendru & Dubey, 2022 ; Mladenović et al., 2021; Nagulapati et al., 2022 ; Philipo…”
Hybrid Deep Learning Model and Natural Language Processing for Cyberbullying Detection/Online Safety
Mandot, M., & Abayomi‐Alli, O. (2026) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-032-29627-6_5
- Authors
- Gamal, D., Alfonse, M., Jiménez-Zafra, S. M., & Aref, M.
- Affiliations
- Ain Shams University (EG)
- Universidad de Jaén (ES)
- Corresponding author
- Gamal, Donia
- Venue
- Big Data and Cognitive Computing
- Publisher
- MDPI AG
- Published
- 2023-03-24
- Volume
- 7
- Issue
- 2
- Pages
- 58
- ISSN
- 2504-2289 (ISSN-L)
- OpenAlex
- W4360844193
- Cited by
- 9
- References
- 68
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 2
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Big Data and Cognitive Computing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- cyberbullying, sentiment analysis, social networking sites, toxic content, benchmark dataset, open problems
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