<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="atapattu-etal-2020-automated">
    <titleInfo>
        <title>Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thushari</namePart>
        <namePart type="family">Atapattu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mahen</namePart>
        <namePart type="family">Herath</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Georgia</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Katrina</namePart>
        <namePart type="family">Falkner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association</title>
        </titleInfo>
        <originInfo>
            <publisher>Australasian Language Technology Association</publisher>
            <place>
                <placeTerm type="text">Virtual Workshop</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5 (HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistiBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved ~0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.</abstract>
    <identifier type="citekey">atapattu-etal-2020-automated</identifier>
    <location>
        <url>https://aclanthology.org/2020.alta-1.2</url>
    </location>
    <part>
        <date>2020-12</date>
        <extent unit="page">
            <start>11</start>
            <end>20</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability
%A Atapattu, Thushari
%A Herath, Mahen
%A Zhang, Georgia
%A Falkner, Katrina
%S Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association
%D 2020
%8 December
%I Australasian Language Technology Association
%C Virtual Workshop
%F atapattu-etal-2020-automated
%X Cyberbullying is a prevalent and growing social problem due to the surge of social media technology usage. Minorities, women, and adolescents are among the common victims of cyberbullying. Despite the advancement of NLP technologies, the automated cyberbullying detection remains challenging. This paper focuses on advancing the technology using state-of-the-art NLP techniques. We use a Twitter dataset from SemEval 2019 - Task 5 (HatEval) on hate speech against women and immigrants. Our best performing ensemble model based on DistiBERT has achieved 0.73 and 0.74 of F1 score in the task of classifying hate speech (Task A) and aggressiveness and target (Task B) respectively. We adapt the ensemble model developed for Task A to classify offensive language in external datasets and achieved ~0.7 of F1 score using three benchmark datasets, enabling promising results for cross-domain adaptability. We conduct a qualitative analysis of misclassified tweets to provide insightful recommendations for future cyberbullying research.
%U https://aclanthology.org/2020.alta-1.2
%P 11-20

Markdown (Informal)

[Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability](https://aclanthology.org/2020.alta-1.2) (Atapattu et al., ALTA 2020)

• Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability (Atapattu et al., ALTA 2020)

ACL

• Thushari Atapattu, Mahen Herath, Georgia Zhang, and Katrina Falkner. 2020. Automated Detection of Cyberbullying Against Women and Immigrants and Cross-domain Adaptability. In Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association, pages 11–20, Virtual Workshop. Australasian Language Technology Association.