Topic Oriented Hate Speech Detection

Raihan Jamil; Mohammad Abdullah Al Nayeem Khan; Md Musfique Anwar

doi:10.1007/978-3-030-96305-7_34

Topic Oriented Hate Speech Detection

Raihan Jamil
, Mohammad Abdullah Al Nayeem Khan^*
, Md Musfique Anwar

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Online social media is a very popular platform nowadays where people can easily make virtual connections with each other and can freely express their opinions and interests on various topics over time. The ability to freely express oneself often results in the spread of hate speech in the virtual world. Thus, it is very important to detect automatically hate speech to reduce its spread on social media. Most of the existing research works in this area paid less attention to the topical hate speech detection. In this paper, we addressed topic-oriented hate speech detection using machine learning classifiers. Experimental results on a real dataset demonstrate the efficacy of the proposed model.

Original language	English
Title of host publication	Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021
Editors	Ajith Abraham, Patrick Siarry, Vincenzo Piuri, Niketa Gandhi, Gabriella Casalino, Oscar Castillo, Patrick Hung
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	365-375
Number of pages	11
ISBN (Print)	9783030963040
DOIs	https://doi.org/10.1007/978-3-030-96305-7_34
State	Published - 2022
Externally published	Yes
Event	21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021 - Virtual, Online Duration: 14 Dec 2021 → 16 Dec 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	420 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021
City	Virtual, Online
Period	14/12/21 → 16/12/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Hate speech
Machine learning
Online social media

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-030-96305-7_34

Cite this

Jamil, R., Khan, M. A. A. N., & Anwar, M. M. (2022). Topic Oriented Hate Speech Detection. In A. Abraham, P. Siarry, V. Piuri, N. Gandhi, G. Casalino, O. Castillo, & P. Hung (Eds.), Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021 (pp. 365-375). (Lecture Notes in Networks and Systems; Vol. 420 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-96305-7_34

Jamil, Raihan ; Khan, Mohammad Abdullah Al Nayeem ; Anwar, Md Musfique. / Topic Oriented Hate Speech Detection. Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. editor / Ajith Abraham ; Patrick Siarry ; Vincenzo Piuri ; Niketa Gandhi ; Gabriella Casalino ; Oscar Castillo ; Patrick Hung. Springer Science and Business Media Deutschland GmbH, 2022. pp. 365-375 (Lecture Notes in Networks and Systems).

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title = "Topic Oriented Hate Speech Detection",

abstract = "Online social media is a very popular platform nowadays where people can easily make virtual connections with each other and can freely express their opinions and interests on various topics over time. The ability to freely express oneself often results in the spread of hate speech in the virtual world. Thus, it is very important to detect automatically hate speech to reduce its spread on social media. Most of the existing research works in this area paid less attention to the topical hate speech detection. In this paper, we addressed topic-oriented hate speech detection using machine learning classifiers. Experimental results on a real dataset demonstrate the efficacy of the proposed model.",

keywords = "Hate speech, Machine learning, Online social media",

author = "Raihan Jamil and Khan, \{Mohammad Abdullah Al Nayeem\} and Anwar, \{Md Musfique\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021 ; Conference date: 14-12-2021 Through 16-12-2021",

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doi = "10.1007/978-3-030-96305-7\_34",

language = "English",

isbn = "9783030963040",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "365--375",

editor = "Ajith Abraham and Patrick Siarry and Vincenzo Piuri and Niketa Gandhi and Gabriella Casalino and Oscar Castillo and Patrick Hung",

booktitle = "Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021",

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}

Jamil, R, Khan, MAAN & Anwar, MM 2022, Topic Oriented Hate Speech Detection. in A Abraham, P Siarry, V Piuri, N Gandhi, G Casalino, O Castillo & P Hung (eds), Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. Lecture Notes in Networks and Systems, vol. 420 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 365-375, 21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021, Virtual, Online, 14/12/21. https://doi.org/10.1007/978-3-030-96305-7_34

Topic Oriented Hate Speech Detection. / Jamil, Raihan; Khan, Mohammad Abdullah Al Nayeem; Anwar, Md Musfique.
Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. ed. / Ajith Abraham; Patrick Siarry; Vincenzo Piuri; Niketa Gandhi; Gabriella Casalino; Oscar Castillo; Patrick Hung. Springer Science and Business Media Deutschland GmbH, 2022. p. 365-375 (Lecture Notes in Networks and Systems; Vol. 420 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Topic Oriented Hate Speech Detection

AU - Jamil, Raihan

AU - Khan, Mohammad Abdullah Al Nayeem

AU - Anwar, Md Musfique

PY - 2022

Y1 - 2022

N2 - Online social media is a very popular platform nowadays where people can easily make virtual connections with each other and can freely express their opinions and interests on various topics over time. The ability to freely express oneself often results in the spread of hate speech in the virtual world. Thus, it is very important to detect automatically hate speech to reduce its spread on social media. Most of the existing research works in this area paid less attention to the topical hate speech detection. In this paper, we addressed topic-oriented hate speech detection using machine learning classifiers. Experimental results on a real dataset demonstrate the efficacy of the proposed model.

AB - Online social media is a very popular platform nowadays where people can easily make virtual connections with each other and can freely express their opinions and interests on various topics over time. The ability to freely express oneself often results in the spread of hate speech in the virtual world. Thus, it is very important to detect automatically hate speech to reduce its spread on social media. Most of the existing research works in this area paid less attention to the topical hate speech detection. In this paper, we addressed topic-oriented hate speech detection using machine learning classifiers. Experimental results on a real dataset demonstrate the efficacy of the proposed model.

KW - Hate speech

KW - Machine learning

KW - Online social media

UR - https://www.scopus.com/pages/publications/85126372206

U2 - 10.1007/978-3-030-96305-7_34

DO - 10.1007/978-3-030-96305-7_34

M3 - Conference contribution

AN - SCOPUS:85126372206

SN - 9783030963040

T3 - Lecture Notes in Networks and Systems

SP - 365

EP - 375

BT - Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021

A2 - Abraham, Ajith

A2 - Siarry, Patrick

A2 - Piuri, Vincenzo

A2 - Gandhi, Niketa

A2 - Casalino, Gabriella

A2 - Castillo, Oscar

A2 - Hung, Patrick

PB - Springer Science and Business Media Deutschland GmbH

T2 - 21st International Conference on Hybrid Intelligent Systems, HIS 2021 and 17th International Conference on Information Assurance and Security, IAS 2021

Y2 - 14 December 2021 through 16 December 2021

ER -

Jamil R, Khan MAAN, Anwar MM. Topic Oriented Hate Speech Detection. In Abraham A, Siarry P, Piuri V, Gandhi N, Casalino G, Castillo O, Hung P, editors, Hybrid Intelligent Systems - 21st International Conference on Hybrid Intelligent Systems, HIS 2021. Springer Science and Business Media Deutschland GmbH. 2022. p. 365-375. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-96305-7_34

Topic Oriented Hate Speech Detection

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this