A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models

TL;DR

This study presents a sentiment analysis framework utilising large language models for longitudinal sentiment analysis of the Sinophobic sentiments expressed in X (Twitter) during the COVID-19 pandemic, revealing that the evolution of the pandemic influenced public sentiment and the prevalence of Sinophobic discourse.

Abstract

The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. Large language models (LLMs) are pre-trained deep learning models used for natural language processing (NLP) tasks. The ability of LLMs to understand and generate human-like text makes them particularly useful for analysing social media data to detect and evaluate sentiments. We present a sentiment analysis framework utilising LLMs for longitudinal sentiment analysis of the Sinophobic sentiments expressed in X (Twitter) during the COVID-19 pandemic. The results show a significant correlation between the spikes in Sinophobic tweets, Sinophobic sentiments and surges in COVID-19 cases, revealing that the evolution of the pandemic influenced public sentiment and the prevalence of Sinophobic discourse. Furthermore, the sentiment analysis revealed a predominant presence of negative sentiments, such as annoyance and denial, which underscores the impact of political narratives and misinformation shaping public opinion. The lack of empathetic sentiment which was present in previous studies related to COVID-19 highlights the way the political narratives in media viewed the pandemic and how it blamed the Chinese community. Our study highlights the importance of transparent communication in mitigating xenophobic sentiments during global crises.

Figures (16)

• Figure 1: Framework for longitudinal sentiment analysis of Sinophobic tweets during COVID-19, involving five major stages: dataset preprocessing and filtering (Stage 1), n-gram analysis and infection rates (Stage 2), fine-tuning BERT model (Stage 3), multi-label sentiment classification (Stage 4), and longitudinal analysis and visualisation (Stage 5).
• Figure 2: Number of total Sinophobic tweets over time
• Figure 3: Number of Sinophobic tweets for each country over time.
• Figure 4: Number of COVID cases for each country over time.
• Figure 5: Bigrams and trigrams of Sinophobic tweets between 2020-04 and 2022-01 worldwide.