Detection of toxic comments over the internet using deep learning methods

Title

Detection of toxic comments over the internet using deep learning methods

Creator

Naskar A.; Harchandani R.; Thomas K.T.

Description

People now share their ideas on a wide range of topics on social media, which has become an integral part of contemporary culture. The majority of people are increasingly turning to social media as a necessity, and there are numerous incidents of social media addiction that have been reported. Socialmedia channels. Socialmedia platforms have established their worth over time by bringing individuals from different backgrounds together, but they have also shown harmful side effects that could have serious consequences. One such unfavourable result is how extremely poisonous many discussions on social media are. Online abuse, hate speech, and occasionally outrage culture are now all considered to be toxic. In this study, we leverage the Transformers Bidirectional Encoder Representations to build an efficient model to detect and classify toxicity in user-generated content on social media. The Kaggle dataset with labelled toxic comments, was used to refine the BERT pre-trained model. Other Deep learning models, including Bidirectional LSTM, Bidirectional-LSTM with attention, and a few other models, were also tested to see which performed best in this classification task. We further evaluate the proposed models utilising dataset obtained from Twitter in order to find harmful content (tweets) using relevant hashtags. The findings showed how well the suggested methodology classified and analysed toxic comments. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors.

Source

Artificial Intelligence, Blockchain, Computing and Security: Volume 1, Vol-1, pp. 447-454.

Date

2023-01-01

Publisher

CRC Press

Subject

BER; BERT; BidirectionalLSTM; Finetuning; Hate speech; Language model; Neural networks; Pretraining; Sentiment analysis; Social media; Toxic; Toxic comment; Twitter

Coverage

Naskar A., Department of Data Science, Department of Data Science CHRIST (Deemed to be University) Pune, CHRIST (Deemed to be University), Maharashtra, Lavasa, Pune, Lavasa, India; Harchandani R., Department of Data Science, Department of Data Science CHRIST (Deemed to be University) Pune, CHRIST (Deemed to be University), Maharashtra, Lavasa, Pune, Lavasa, India; Thomas K.T., Department of Data Science, Department of Data Science CHRIST (Deemed to be University) Pune, CHRIST (Deemed to be University), Maharashtra, Lavasa, Pune, Lavasa, India

Rights

Restricted Access

Relation

ISBN: 978-100384581-2; 978-103249393-0

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003393580-68

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180023049&doi=10.1201%2f9781003393580-68&partnerID=40&md5=e6e8bf2b156b8eec2d4e59b40ecdf4c3