Accelerating automatic hate speech detection using parallelized ensemble learning models

Expert Systems with Applications - Tập 230 - Trang 120564 - 2023
Shivang Agarwal1, Ankur Sonawane1, C. Ravindranath Chowdary1
1Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India

Tài liệu tham khảo

Agarwal, 2020, A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection, Expert Systems with Applications, 146, 10.1016/j.eswa.2019.113160 Agarwal, 2021, Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19, Expert Systems with Applications, 185, 10.1016/j.eswa.2021.115632 Agarwal, 2022, A-iLearn: An adaptive incremental learning model for spoof fingerprint detection, Machine Learning with Applications, 7, 10.1016/j.mlwa.2021.100210 Agrawal, 2018, Deep learning for detecting cyberbullying across multiple social media platforms, 141 Arango, 2019, Hate speech detection is not as easy as you may think: A closer look at model validation, 45 Badjatiya, 2017, Deep learning for hate speech detection in tweets, 759 Badjatiya, 2019, Stereotypical bias removal for hate speech detection task using knowledge-based generalizations, 49 Basile, 2019, SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter, 54 Chopra, 2020, Hindi-english hate speech detection: Author profiling, debiasing, and practical perspectives, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 386, 10.1609/aaai.v34i01.5374 Corazza, 2020, A multilingual evaluation for online hate speech detection, ACM Transactions on Internet Technology, 20, 10.1145/3377323 Davidson, 2017, Automated hate speech detection and the problem of offensive language, 512 Dragoni, 2018, OntoSenticNet: A commonsense ontology for sentiment analysis, IEEE Intelligent Systems, 33, 77, 10.1109/MIS.2018.033001419 Eliacik, 2018, Influential user weighted sentiment analysis on topic based microblogging community, Expert Systems with Applications, 92, 403, 10.1016/j.eswa.2017.10.006 Fares, 2019, Unsupervised word-level affect analysis and propagation in a lexical knowledge graph, Knowledge-Based Systems, 165, 432, 10.1016/j.knosys.2018.12.017 Fortuna, 2018, A survey on automatic detection of hate speech in text, ACM Computing Surveys, 51, 85:1 Gröndahl, 2018, All you need is ”Love”: Evading hate speech detection, 2 Hakak, 2021, An ensemble machine learning approach through effective feature extraction to classify fake news, Future Generation Computer Systems, 117, 47, 10.1016/j.future.2020.11.022 Hewitt, 2016, The problem of identifying misogynist language on Twitter (and other online social spaces), 333 Kwok, 2013, Locate the hate: Detecting tweets against blacks MacAvaney, 2019, Hate speech detection: Challenges and solutions, PLOS ONE, 14, 1, 10.1371/journal.pone.0221152 Markov, 2021, Exploring stylometric and emotion-based features for multilingual cross-domain hate speech detection, 149 Matloob, 2021, Software defect prediction using ensemble learning: A systematic literature review, IEEE Access, 9, 98754, 10.1109/ACCESS.2021.3095559 Pamungkas, 2021, A joint learning approach with knowledge injection for zero-shot cross-lingual hate speech detection, Information Processing & Management, 58, 10.1016/j.ipm.2021.102544 Park, 2017, One-step and two-step classification for abusive language detection on Twitter, 41 Ribeiro, 2020 Rosa, 2019, Automatic cyberbullying detection: A systematic review, Computers in Human Behavior, 93, 333, 10.1016/j.chb.2018.12.021 Salawu, 2020, Approaches to automated detection of cyberbullying: A survey, IEEE Transactions on Affective Computing, 11, 3, 10.1109/TAFFC.2017.2761757 Sap, 2019, The risk of racial bias in hate speech detection, 1668 Sigurbergsson, 2020, Offensive language and hate speech detection for danish, 3498 Wang, 2014, Sentiment classification: The contribution of ensemble learning, Decision Support Systems, 57, 77, 10.1016/j.dss.2013.08.002 Waseem, 2016, Hateful symbols or hateful people? Predictive features for hate speech detection on Twitter, 88 Ye, 2021, Multi-view ensemble learning method for microblog sentiment classification, Expert Systems with Applications, 166, 10.1016/j.eswa.2020.113987 Zhang, 2018, Detecting hate speech on Twitter using a convolution-GRU based deep neural network, 745 Ziems, 2020 Zimmerman, 2018, Improving hate speech detection with deep learning ensembles