Hope speech detection in YouTube comments

• Published in: Social network analysis and mining Vol. 12; no. 1; p. 75
• Main Author: Chakravarthi, Bharathi Raja
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2022; Springer Nature B.V
• Subjects: Applications of Graph Theory and Complex Networks; Artificial intelligence; Bullying; Computer Science; COVID-19; Data Mining and Knowledge Discovery; Datasets; Decision making; Decision trees; Digital media; Disease transmission; Economics; Game Theory; Hate speech; Humanities; Inclusion; Internet; Language; Law; Machine learning; Mental health; Methodology of the Social Sciences; Minority & ethnic groups; Multiculturalism & pluralism; Original; Original Article; People with disabilities; Regression analysis; Social and Behav. Sciences; Social exclusion; Social integration; Social media; Social networks; Statistics for Social Sciences; STEM education; Technology; Unpleasant; Virtual communities; Dravidian languages; Inclusion; Equality; Multilingual; Hope speech; Diversity
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-022-00901-z

Recent work on language technology has tried to recognize abusive language such as those containing hate speech and cyberbullying and enhance offensive language identification to moderate social media platforms. Most of these systems depend on machine learning models using a tagged dataset. Such models have been successful in detecting and eradicating negativity. However, an additional study has lately been conducted on the enhancement of free expression through social media. Instead of eliminating ostensibly unpleasant words, we created a multilingual dataset to recognize and encourage positivity in the comments, and we propose a novel custom deep network architecture, which uses a concatenation of embedding from T5-Sentence. We have experimented with multiple machine learning models, including SVM, logistic regression, K-nearest neighbour, decision tree, logistic neighbours, and we propose new CNN based model. Our proposed model outperformed all others with a macro F1-score of 0.75 for English, 0.62 for Tamil, and 0.67 for Malayalam.