Toxic text classification

Title

Toxic text classification

Creator

Ghosh S.; Kumar S.; Lepcha S.; Jain S.S.

Description

The users of the Internet increase every moment with increasing population and accessibility of the Internet. With the increase in the number of users of the Internet, the number of controversies, arguments and abuses of all kinds increases. It becomes necessary for social media and other sites to identify toxic content amongst a large number of content being posted by the users of the sites every second. The traditional algorithms that depend on users reporting toxic content for it to be deleted and necessary actions to be taken against the users posting the content would take a long time, within which it would have gained media attention and would have lead to huge fights over the content. Thus, it becomes important for the content to be evaluated for toxicity at the time it is posted in order to stop it from being posted. Therefore, we have designed and trained a deep learning model that can be read through the textual content given through it and determine if it is toxic or not. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

Source

Lecture Notes in Networks and Systems, Vol-132, pp. 251-260.

Date

2021-01-01

Publisher

Springer

Subject

Classification; Content moderation; Deep learning; Internet content; Social media

Coverage

Ghosh S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Kumar S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Lepcha S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Jain S.S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India

Rights

Restricted Access

Relation

ISSN: 23673370

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1007/978-981-15-5309-7_27

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090372547&doi=10.1007%2f978-981-15-5309-7_27&partnerID=40&md5=8c2062be624c172d4b54463181a84d0f