Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers

• URL: http://arxiv.org/abs/2501.05423v1
• Date: Thu, 09 Jan 2025 18:30:14 GMT
• Title: Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers
• Authors: Jerry Chongyi Hu, Mohammed Shahid Modi, Boleslaw K. Szymanski,
• Abstract summary: We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories.
• Score: 0.0
• License:
• Abstract: Studying public sentiment during crises is crucial for understanding how opinions and sentiments shift, resulting in polarized societies. We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19 stage, the outbreak stage, and the early stage of epidemic prevention. We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories. Analyzing sentiment shifts on Weibo provides insights into how social events and government actions influence public opinion. This study contributes to understanding the dynamics of social sentiments during health crises, fulfilling a gap in sentiment analysis for Chinese platforms. By examining these dynamics, we aim to offer valuable perspectives on digital communication's role in shaping society's responses during unprecedented global challenges.

Related papers

• Community Shaping in the Digital Age: A Temporal Fusion Framework for Analyzing Discourse Fragmentation in Online Social Networks [45.58331196717468]
  This research presents a framework for analyzing the dynamics of online communities in social media platforms. By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution.
  arXiv  Detail & Related papers  (2024-09-18T03:03:02Z)
• A longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models [3.3741245091336083]
  The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. 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.
  arXiv  Detail & Related papers  (2024-08-29T23:39:11Z)
• Large language models for sentiment analysis of newspaper articles during COVID-19: The Guardian [0.16777183511743468]
  This study provides a sentiment analysis of The Guardian newspaper during various stages of COVID-19. During the early pandemic stages, public sentiment prioritised urgent crisis response, later shifting focus to addressing the impact on health and the economy. Results indicate that during the early pandemic stages, public sentiment prioritised urgent crisis response, later shifting focus to addressing the impact on health and the economy.
  arXiv  Detail & Related papers  (2024-05-20T07:10:52Z)
• Know it to Defeat it: Exploring Health Rumor Characteristics and Debunking Efforts on Chinese Social Media during COVID-19 Crisis [65.74516068984232]
  We conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site. Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type. We show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies across rumor types and debunkers.
  arXiv  Detail & Related papers  (2021-09-25T14:02:29Z)
• When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
  We present CovidEmo, 1K tweets labeled with emotions. We examine how well large pre-trained language models generalize across domains and crises.
  arXiv  Detail & Related papers  (2021-07-23T04:07:14Z)
• Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
  Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions. In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
  arXiv  Detail & Related papers  (2020-09-12T15:54:51Z)
• On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic [5.900114841365645]
  Despite the gravity of the issue, very few studies have studied online antisocial behaviors amid the COVID-19 pandemic. In this paper, we fill the research gap by collecting and annotating a large dataset of over 40 million COVID-19 related tweets. We also conduct an empirical analysis of our annotated dataset and found that new abusive lexicons are introduced amid the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2020-07-21T11:11:35Z)
• Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions [44.92240076313168]
  We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020. By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures. Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
  arXiv  Detail & Related papers  (2020-05-29T09:24:38Z)
• The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic [66.80677233314002]
  Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
  arXiv  Detail & Related papers  (2020-04-21T13:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.