Utilising Twitter Metadata for Hate Classification
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Several studies used DistilBERT for cyberbullying detection in social networks and obtained promising performance.
“Several studies [54-56] used DistilBERT for cyberbullying detection in social networks. Their experimental results show that they obtained a promising performance while using DistilBERT as the word-embedding technique as well as the fine-tuned classifier.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [56]
Cites
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Dietterich, T. G. (1998) · Neural Computation · doi:10.1162/089976698300017197 · Cited by 3,840 · see citing works
Investigating the impact of data normalization on classification performance
Singh, D., & Singh, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105524 · Cited by 1,820 · see citing works
Online communication, social media and adolescent wellbeing: A systematic narrative review
Best, P., Manktelow, R., & Taylor, B. (2014) · Children and Youth Services Review · doi:10.1016/j.childyouth.2014.03.001 · Cited by 1,067 · 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
Does personal social media usage affect efficiency and well-being?
Brooks, S. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.12.053 · Cited by 434 · see citing works
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media
Mozafari, M., Farahbakhsh, R., & Crespi, N. (2020) · Studies in Computational Intelligence · doi:10.1007/978-3-030-36687-2_77 · Cited by 396 · 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
HateBERT: Retraining BERT for Abusive Language Detection in English
Caselli, T., Basile, V., Mitrović, J., & Granitzer, M. (2021) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2021.woah-1.3 · Cited by 373 · see citing works
Spread of Hate Speech in Online Social Media
Mathew, B., Dutt, R., Goyal, P., & Mukherjee, A. (2019) · ACM Web Science Conference (WebSci) · doi:10.1145/3292522.3326034 · Cited by 370 · see citing works
Directions in abusive language training data, a systematic review: Garbage in, garbage out
Vidgen, B., & Derczynski, L. (2020) · PLoS ONE · doi:10.1371/journal.pone.0243300 · Cited by 335 · see citing works
Prevalence and Psychological Effects of Hateful Speech in Online College Communities
Saha, K., Chandrasekharan, E., & Choudhury, M. D. (2019) · ACM Web Science Conference (WebSci) · doi:10.1145/3292522.3326032 · Cited by 175 · see citing works
Ayo, F. E., Folorunso, O. O., Ibharalu, F. T., & Osinuga, I. A. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100311 · Cited by 122 · see citing works
Walther, J. B. (2022) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2021.12.010 · Cited by 114 · see citing works
Using BERT to Extract Topic-Independent Sentiment Features for Social Media Bot Detection
Heidari, M., & Jones, J. H. (2020) · 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon51285.2020.9298158 · Cited by 102 · see citing works
Hate Speech Detection in Twitter using Transformer Methods
Mutanga, R. T., Naicker, N., & Oludayo, O. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110972 · Cited by 68 · see citing works
Barlett, C. P., DeWitt, C. C., Maronna, B., & Johnson, K. (2018) · Violence and Gender · doi:10.1089/vio.2017.0057 · Cited by 62 · see citing works
The impact and response to electronic bullying and traditional bullying among adolescents
Horner, S. B., Asher, Y. M., & Fireman, G. D. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.03.007 · Cited by 52 · see citing works
Enhanced Twitter Sentiment Classification Using Contextual Information
Vosoughi, S., Zhou, H., & Roy, D. K. (2015) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w15-2904 · Cited by 49 · see citing works
Llinares, F. M., Moneva, A., & Esteve, M. (2018) · Crime Science · doi:10.1186/s40163-018-0089-1 · Cited by 49 · see citing works
Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
Dai, X., Karimi, S., Hachey, B., & Paris, C. L. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.151 · Cited by 44 · see citing works
The Effects of Normalisation Methods on Speech Emotion Recognition
Sefara, T. J. (2019) · International Multidisciplinary Information Technology and Engineering Conference (IMITEC) · doi:10.1109/imitec45504.2019.9015895 · Cited by 39 · see citing works
Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
Masud, S., Dutta, S., Makkar, S., et al. (2021) · Proceedings - International Conference on Data Engineering · doi:10.1109/icde51399.2021.00050 · Cited by 39 · see citing works
ALONE: A Dataset for Toxic Behavior Among Adolescents on Twitter
Wijesiriwardene, T., Inan, H., Kursuncu, U., Gaur, M., Shalin, V. L., Thirunarayan, K., Sheth, A., & Arpinar, I. B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60975-7_31 · Cited by 29 · see citing works
Using Transfer-based Language Models to Detect Hateful and Offensive Language Online
Isaksen, V., & Gambäck, B. (2020) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2020.alw-1.3 · Cited by 28 · see citing works
Easing Legal News Monitoring with Learning to Rank and BERT
Sanchez, L., He, J., Manotumruksa, J., Albakour, D., Martinez, M., & Lipani, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45442-5_42 · Cited by 11 · see citing works
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A Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103 · see citing works
“Several studies [54–56] used DistilBERT for cyberbullying detection in social networks.”
Background
- Authors
- Warke, O., Jose, J. M., & Breitsohl, J.
- Affiliations
- University of Glasgow (GB)
- Corresponding author
- Warke, Oliver
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 676-684
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4327644089
- Cited by
- 4
- References
- 25
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 0.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- 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
- hate speech detection, BERT classifier, social media abuse, text classification
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