Detecting Antisocial Behavior on Social Media During COVID-19 Lockdown

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Detecting Antisocial Behavior on Social Media During COVID-19 Lockdown

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Detecting Antisocial Behavior on Social Media During COVID-19 Lockdown

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Novel and Intelligent Digital Systems (NiDS 2024)

ISBN

978-3-031-73343-7

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

12

Strana od-do

189-200

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Athény

Datum konání akce

25. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—