Linguistic Pattern Analysis in the Climate Change-Related Tweets from UK and N igeria (2023)

Abstract: To understand the global trends of human opinion on climate change in specific geographical areas, this research proposes a framework to analyse linguistic features and cultural differences in climate-related tweets. Our study combines transformer networks with linguistic feature analysis to address small dataset limitations and gain insights into cultural differences in tweets from the UK and Nigeria. Our study found that Nigerians use more leadership language and informal words in discussing climate change on Twitter compared to the UK, as these topics are treated as an issue of salience and urgency. In contrast, the UK’s discourse about climate change on Twitter is characterised by using more formal, logical, and longer words per sentence compared to Nigeria. Also, we confirm the geographical identifiability of tweets through a classification task using DistilBERT, which achieves 83% of accuracy.

## introduction the ipcc reported in 2022 that climate change is currently impacting all inhabited regions worldwide, with human activities contributing to many observed changes in physical and biological systems @xcite . the electronic data produced by internet users during climate-related events can offer valuable insights into how different geographic areas perceive the risks associated with climate change @xcite . however, intercultural dialogue and discourse are increasingly being studied in linguistics, as culture is seen as a fundamental aspect of human activity @xcite . one such area of study is conversation culturomics, which uses language analysis to understand human culture and can help conservationists respond to cultural trends while staying socially relevant. previous research by @xcite identified ways in which this analysis can be useful, such as assessing the cultural impact of conservation interventions and promoting public understanding. our research aims at contributing to the comprehension of the interactions that exist across the uk and nigeria by analysing and identifying linguistic features that each group uses to communicate the climate change narrative and therefore gain insights into the factors that shape these opinions and identify areas where more education and conservation interventions are needed. this is relevant as previous research by @xcite highlighted a focus on anglophone culture in studying climate change and conceptions on social media. therefore, this study's inclusion of african perspectives contributes to the overall understanding of cultural differences in climate-related discourse. in this paper, we use linguistic feature analysis supported by transformer networks to enhance classification performance and generate insights on cultural differences in climate-related discourse. seen as only roughly 2% of twitter data is geotagged @xcite , our study is based on a small dataset, and we look to overcome the limitations of data sparsity when analysing specific cultures through the inclusion of linguistic and socio-cultural features. ## related works researchers have contributed to the field of nlp by providing a wide range of approaches and techniques for analysing and predicting sentiments. recent research by @xcite proposed a framework to examine the conversations on climate change between two communities on twitter, namely activists and sceptics. the framework compares users' hashtags, bot percentage, and messaging to understand the differences between the communities. the study found that sceptics' messages focused more on attacking personalities, while activists' messages aimed to call for action against climate change. in addition to polarising tweet users, social media have been used to analyse emotions in tweets. @xcite studied climate policy opinions on twitter in the uk and spain. results show uk sentiment is more positive, with anticipation prevailing, while fear is dominant in spain. gender analysis also indicates higher male tweeting in both countries, yet spain demonstrates a more balanced gender distribution. also, @xcite research investigated sentiment patterns in tweets, particularly weather and time's impact on aggregate sentiment, and evaluated how clearly the well-known individual patterns translate into population-wide patterns. machine learning techniques with weather-correlated tweets shows aggregate sentiment follows distinct climate, temporal, and seasonal patterns. furthermore, @xcite used deep neural networks to detect climate change skeptics from tweet content and analyzed twitter's climate change discussions and influencing factors over time. they created a neural network model with an 88% accuracy in identifying deniers. extreme weather events and policy shifts were noted to influence public interest and attitudes toward climate change. studies that systematically explore intercultural differences are rare. @xcite show that ngos in china typically work to frame climate change within national chinese context, to highlight the relevance and impact for all chinese people. this common perspective is typical in collectivist cultures @xcite . in contrast, african ngo outlets often frame climate change discourse in terms of increased agricultural hardships and social hardships and place an emphasis on educating citizens on the matter @xcite . education is also a topic in other asian countries, including afghanistan, bhutan, kiribati, nepal, and tuvalu, where ngos additionally work towards implementing adaptive strategies to climate change risks and increasing scientific scholarship @xcite . all these studies are based on social science methodology and often rely on extensive manual annotation. in this paper we want to explore how linguistic features can overcome the burden of annotation. also, schäfer and painter, (2021) contrast climate journalism in the global north and south. the authors find that while climate coverage has changed globally over the last decade to move increasingly online, there are fewer journalists who specialise specifically on climate in the global south, which impacts public information in those regions accordingly. this paper proposes a new method to analyse linguistic features and cultural differences in climate-related tweets. our study combines transformer networks with linguistic feature analysis to address small dataset limitations and gain insights into cultural differences. we conclude by highlighting the importance of understanding cultural differences, particularly in the nigerian/african perspective, in climate discourse to facilitate effective action against climate change. the focus on the nigerian/african perspective adds a novel contribution to the existing literature. ## conclusion the study employed linguistic feature analysis supported by transformer networks to investigate cultural differences in climate-related discourse between nigerian and british tweets, aiming to identify trends in their respective lexicons. through this approach, we were able to achieve a respective baseline classification performance and mitigate the limitations of working with small datasets. our findings suggest that studying linguistic patterns and word use are crucial areas of research in socio-cultural analysis tasks, particularly in the classification of tweets based on their location. this is particularly useful in quickly addressing climate change disasters in specific geographic areas, as well as gauging public interest in climate change, and characterising discourse in different cultures even with limited data availability. our experiments with distilbert on small data yielded promising results, with an accuracy of 83% in correctly classifying the country of origin for climate discourse tweets. based on these findings, we could recommend that those interested in identifying the country of origin of climate discourse tweets using linguistics patterns should focus on language that conveys a clear positive or negative sentiment and complex language in a way that is perceived as authoritative. additionally, while the level of analytical thinking and linguistic complexity may also be important in predicting the country of origin of climate discourse tweets, they may not have as significant an impact as the overall sentiment and complexity of language. future research will examine tweets in languages other than english as well as tweets from other countries. in addition to the linguistics pattern, we will look at the emotional dynamics surrounding climate change in these countries over an extended period.