Cross-cultural Sentiment Analysis of Social Media Responses to a Sudden Crisis Event (2025)

Abstract: Although the responses to events such as COVID-19 have been extensively studied, research on sudden crisis response in a multicultural context is still limited. In this paper, our contributions are 1)We examine cultural differences in social media posts related to such events in two different countries, specifically the United Kingdom lockdown of 2020-03-23 and the China Urumqi fire1 of 2022-11-24. 2) We extract the emotional polarity of tweets and weibos gathered temporally adjacent to those two events, by fine-tuning transformer-based language models for each language. We evaluate each model’s performance on 2 benchmarks, and show that, despite being trained on a relatively small amount of data, they exceed baseline accuracies. We find that in both events, the increase in negative responses is both dramatic and persistent, and does not return to baseline even after two weeks. Nevertheless, the Chinese dataset reflects, at the same time, positive responses to subsequent government action. Our study is one of the first to show how sudden crisis events can be used to explore affective reactions across cultures

## introduction the covid-19 pandemic has now been ongoing for three years, impacting significant events such as the wuhan outbreak, vaccine roll-outs, and state of emergency declarations. throughout these events, individuals have been expressing their viewpoints on various social media platforms, which have become integral to their lives. while polarity detection is well-studied (e.g., @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ; @xcite ), in sudden event contexts @xcite ; @xcite ; @xcite ), research on crisis response in a multicultural context is still limited ( imran et al. 1 https://wikipedia.org/wiki/2022_ãoerãmqi_fire (2020)). this study aims to contribute to the understanding of how to guide and focus people's emotional responses during emergencies through the analysis of sentiment expressed on social media during sudden crisis events. our research purpose is to investigate how individuals from different cultural and linguistic backgrounds respond to the covid-19 pandemic in social medias, with a specific focus on crisis events in the united kingdom and china. cultural values and norms significantly influence people's behaviors @xcite during a crisis. understanding these behaviors can help in tailoring public health messages that are culturally sensitive and more likely to be effective @xcite . and the degree to which different cultures comply with and trust public health measures (such as social distancing, mask-wearing, and vaccinations) can provide insights into how these measures should be communicated and enforced. addationaly, different cultures have unique ways of dealing with crisis and adversity. studying these can offer valuable lessons in building resilience and mental health support systems @xcite . importantly, this work reflects a growing shift in nlp toward socially beneficial applications @xcite , using language models not just for technical benchmarks but to understand real-world emotional responses in times of crisis. each dataset covers a one-month period, spanning the two weeks before and after a sudden crisis event, and each collects manual crowd-sourced annotations of the polarity expressed in the posts. we have developed two language-specific transformer-based models to analyze the sentiment of these posts, classifying their polarity as negative, neutral, or positive. compared to prior studies @xcite @xcite ) that consider only sentence-level or aspect-level texts, our work is more challenging, as it is cross-cultural studies involve understanding and navigating diverse cultural norms, values, and communication styles. analyzing sentiment in this context requires sensitivity to cultural nuances that influence how emotions are expressed and interpreted. moreover, focusing on the before-and-after aspects adds a temporal dimension, demanding an examination of evolving emotional dynamics and how cultural factors shape these changes over time. this complexity makes the study more challenging but also more comprehensive in capturing the full spectrum of emotional responses to sudden crisis events with nation-wide impact. ## experiments we evaluated the performance of satwbert for english and sachbert for chinese by comparing their accuracy rates and macrof1 against baseline models. the dataset was divided into 80% training and 20% test data for five-fold cross-validation, for both languages. this ensured a comprehensive assessment of the models' effectiveness. ## conclusion we presented two transformer-based models for sentiment analysis that were tailored to sudden crisis events. our models demonstrated stable and superior performance compared to baseline models. they enabled cross-cultural comparisons of people's responses, showing a notable persistence of negative responses to sudden crisis events. we aim to further enhance our models by examining further sudden crisis events, and by expanding our multi-cultured analysis to events that are synchronous across countries. we are also exploring ways of reliably extending our tripartite division of sentiment to one of a five-way scale, in order to better accommodate extreme sentiments (e.g., "very positive"). social media plays a crucial role in public health emergencies, enabling the public to access important information and express their emotions. however, there are significant differences in social media usage patterns and public sentiment responses across different countries and regions. our research results can aid agencies in developing effective response strategies for public health emergencies and promoting better public mental health.