@inproceedings{verma-etal-2022-benchmarking,
title = "Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts",
author = "Verma, Kanishk and
Milosevic, Tijana and
Cortis, Keith and
Davis, Brian",
editor = "Adebayo, Kolawole and
Nanda, Rohan and
Verma, Kanishk and
Davis, Brian",
booktitle = "Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lateraisse-1.4",
pages = "26--31",
abstract = "Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms. Unfortunately, most existing datasets focusing on cyberbullying detection or classification are i) limited in number ii) usually targeted to one specific online social networking (OSN) platform, or iii) often contain low-quality annotations. In this study, we fine-tune and benchmark state of the art neural transformers for the binary classification of cyberbullying in social media texts, which is of high value to Natural Language Processing (NLP) researchers and computational social scientists. Furthermore, this work represents the first step toward building neural language models for cross OSN platform cyberbullying classification to make them as OSN platform agnostic as possible.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="verma-etal-2022-benchmarking">
<titleInfo>
<title>Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kanishk</namePart>
<namePart type="family">Verma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tijana</namePart>
<namePart type="family">Milosevic</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Keith</namePart>
<namePart type="family">Cortis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brian</namePart>
<namePart type="family">Davis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kolawole</namePart>
<namePart type="family">Adebayo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rohan</namePart>
<namePart type="family">Nanda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kanishk</namePart>
<namePart type="family">Verma</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Brian</namePart>
<namePart type="family">Davis</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Language Resources Association</publisher>
<place>
<placeTerm type="text">Marseille, France</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms. Unfortunately, most existing datasets focusing on cyberbullying detection or classification are i) limited in number ii) usually targeted to one specific online social networking (OSN) platform, or iii) often contain low-quality annotations. In this study, we fine-tune and benchmark state of the art neural transformers for the binary classification of cyberbullying in social media texts, which is of high value to Natural Language Processing (NLP) researchers and computational social scientists. Furthermore, this work represents the first step toward building neural language models for cross OSN platform cyberbullying classification to make them as OSN platform agnostic as possible.</abstract>
<identifier type="citekey">verma-etal-2022-benchmarking</identifier>
<location>
<url>https://aclanthology.org/2022.lateraisse-1.4</url>
</location>
<part>
<date>2022-06</date>
<extent unit="page">
<start>26</start>
<end>31</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts
%A Verma, Kanishk
%A Milosevic, Tijana
%A Cortis, Keith
%A Davis, Brian
%Y Adebayo, Kolawole
%Y Nanda, Rohan
%Y Verma, Kanishk
%Y Davis, Brian
%S Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F verma-etal-2022-benchmarking
%X Cyberbullying is bullying perpetrated via the medium of modern communication technologies like social media networks and gaming platforms. Unfortunately, most existing datasets focusing on cyberbullying detection or classification are i) limited in number ii) usually targeted to one specific online social networking (OSN) platform, or iii) often contain low-quality annotations. In this study, we fine-tune and benchmark state of the art neural transformers for the binary classification of cyberbullying in social media texts, which is of high value to Natural Language Processing (NLP) researchers and computational social scientists. Furthermore, this work represents the first step toward building neural language models for cross OSN platform cyberbullying classification to make them as OSN platform agnostic as possible.
%U https://aclanthology.org/2022.lateraisse-1.4
%P 26-31
Markdown (Informal)
[Benchmarking Language Models for Cyberbullying Identification and Classification from Social-media Texts](https://aclanthology.org/2022.lateraisse-1.4) (Verma et al., LATERAISSE 2022)
ACL