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master's thesis
Prepoznavanje nasilja na društvenim mrežama
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:674063
Vukasović, Ana
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Vukasović, A. (2022). Prepoznavanje nasilja na društvenim mrežama (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:674063

Vukasović, Ana. "Prepoznavanje nasilja na društvenim mrežama." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:674063

Vukasović, Ana. "Prepoznavanje nasilja na društvenim mrežama." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:674063

Vukasović, A. (2022). 'Prepoznavanje nasilja na društvenim mrežama', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 05 October 2024, https://urn.nsk.hr/urn:nbn:hr:168:674063

Vukasović A. Prepoznavanje nasilja na društvenim mrežama [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 October 05] Available at: https://urn.nsk.hr/urn:nbn:hr:168:674063

A. Vukasović, "Prepoznavanje nasilja na društvenim mrežama", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:674063

Metadata
TitlePrepoznavanje nasilja na društvenim mrežama
Title (english)Detection of Cyberbullying on Social Networks
AuthorAna Vukasović
MentorIvica Botički (mentor)
Committee memberIvica Botički (član povjerenstva)
Committee memberTomislav Jagušt (član povjerenstva)
Committee memberDanijel Mlinarić (predsjednik povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Kontinuiranim rastom društvenih mreža, ljudi imaju sve veću virtualnu povezanost, ali i nažalost sve veću platformu za online nasilje. Kako bi se takvo ponašanje spriječilo, počinju se koristiti sustavi za automatsko detektiranje nasilnog govora na društvenim mrežama. Ovaj rad opisuje nekoliko takvih sustava temeljenih na strojnom učenju za objave na engleskom jeziku na Twitter društvenoj mreži. Modeli određuju radi li se o nasilju te o kojem obliku nasilja. Osim evaluacije isprobanih modela, detaljno su opisani i korišteni skupovi podataka. Model s najboljim performansama temelji se na ugađanju predtreniranog BERT modela, pomoću kojeg se iz teksta dobije kontekstualizirana gusta vektorska reprezentacija, te dodatnih značajki napravljenih predobradom teksta koje opisuju prisutnosti određenih znakova u tekstu (interpunkcijski znakovi, hashtagovi, itd.) koji zajedno ulaze u višeslojni perceptron. Opisani model postigao je 89% točnosti na testnom skupu.
Abstract (english)
With the continuous growth of social networks, people are more and more virtually connected, but this also, unfortunately, increases the platform for online violence - cyberbullying. To prevent such behavior, systems for automatic detection of cyberbullying on social networks are starting to be used. This graduate thesis describes several such new machine learning-based systems for posts in English on the Twitter social network. The models determine whether it is cyberbullying and what form of cyberbullying. In addition to the evaluation of the tested models, used datasets are described in detail. The best-performing model is based on tuning a pre-trained BERT model, which yields a contextualized dense vector representation from the text, and additional features made by text preprocessing that describe the presence of certain characters in the text (punctuation, hashtags, etc.) are put at the input of a multilayered perceptron. The described model achieved 89% accuracy on the test set.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:674063
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-14 10:16:21