An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media

Title

An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media

Creator

Nath N.; George J.P.; Kesan A.; Rodrigues A.

Description

Social media has become an integral part of life as users are spending a significant amount of time networking online. Two primary reasons for its increasing popularity are ease of access and freedom of speech. People can express themselves without worrying about consequences. Due to lack of restriction, however, cases of cyberbullying and hate speeches are increasing on social media. Twitter and Facebook receive over a million posts daily, and manual filtration of this enormous number is a tedious task. This paper proposes a deep learning-based hybrid architecture (CNN + LSTM) to identify hate speeches by using Stanfords GloVe, which is a pre-trained word embedding. The model has been tested under different parameters and compared with several state-of-the-art models. The proposed framework has outperformed existing models and has also achieved the best accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-462, pp. 347-355.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

CNN; Detection; Hate speech; Hybrid model; LSTM

Coverage

Nath N., CHRIST (Deemed to be University), Bengaluru, India; George J.P., CHRIST (Deemed to be University), Bengaluru, India; Kesan A., CHRIST (Deemed to be University), Bengaluru, India; Rodrigues A., CHRIST (Deemed to be University), Bengaluru, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981192210-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-2211-4_30

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135007967&doi=10.1007%2f978-981-19-2211-4_30&partnerID=40&md5=13972f303d79c8e87bb7e04c29d9b447