Detecting Antisocial Behavior on Social Media During COVID-19 Lockdown
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922252" target="_blank" >RIV/00216275:25410/24:39922252 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-73344-4_15" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-73344-4_15</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-73344-4_15" target="_blank" >10.1007/978-3-031-73344-4_15</a>
Alternative languages
Result language
angličtina
Original language name
Detecting Antisocial Behavior on Social Media During COVID-19 Lockdown
Original language description
The widespread availability of the internet has rendered the engagement with social media an integral component of contemporary society. Platforms such as Facebook, Twitter/X, YouTube, among others, are designed to facilitate extensive, efficient, and sustained user participation, offering both anonymity and opportunities for positive engagement. However, these platforms have also become arenas for antisocial behaviors, including disregard for others’ rights, lack of empathy, trolling, and aggression, leading to significant negative psychological impacts on affected individuals. These impacts range from anxiety and emotional trauma to depression, psychological disorders, self-isolation, diminished self-esteem, and even suicidal thoughts. This study focuses on antisocial behavior (ASB) manifested in tweets from Ghana during the 21-day COVID-19 lockdown. We develop a gold-standard annotated ASB corpus from collected and pre-processed data. We then assess the performance of different baseline classifiers against three transformer models-BERT, RoBERTa, and ELECTRA-in a binary classification task designed to detect ASB. Each model demonstrated varying degrees of success; however, the RoBERTa model, upon fine-tuning, exhibited superior performance, achieving an accuracy rate of 95.59% and an F1 score of 94.99%, thereby outperforming the other models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Novel and Intelligent Digital Systems (NiDS 2024)
ISBN
978-3-031-73343-7
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
12
Pages from-to
189-200
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Athény
Event date
Sep 25, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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