Voices in the digital storm: Unraveling online polarization with ChatGPT

Yunfei Xing, Justin Zuopeng Zhang, Guangqing Teng and Xiaotang Zhou

Technology in Society, 2024, vol. 77, issue C

Abstract: ChatGPT, an esteemed natural language processing model, has demonstrated remarkable capabilities in intelligent text generation, interactive conversation, and myriad additional tasks. The utilization of ChatGPT has generated a wide debate among users with different attitudes on social media platforms, culminating in the phenomenon of polarization. Based on confirmation bias theory, this paper presented a theoretical framework that elucidates the process of online polarization. Subsequently, we develop the sentiment classification (BERTSentiment) and topic identification (BERTopic) model leveraging the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model. To empirically investigate the public sentiment regarding ChatGPT, an in-depth study was conducted on the X platform. The results indicate that although a small portion of users (approximately 10%) express negative sentiments regarding ChatGPT's ethical considerations, functionality, and accuracy, the majority of users exhibit either positive or neutral views. Among the public concerns, AI and bot functions, response quality, instant messaging, enterprise applications, and technological aspects emerge as the most prominent topics. This study sheds light on public perceptions regarding the progress and integration of emerging technologies. Moreover, it introduces a fresh data mining perspective that enhances our understanding of polarization in the context of social media research.

Keywords: ChatGPT; Opinion polarization; social media; BERTopic; BERTSentiment (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:77:y:2024:i:c:s0160791x24000824

DOI: 10.1016/j.techsoc.2024.102534

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