ADS Capstone Chronicles Revised

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However, scientific voices often struggle to convey information to diverse audiences, including the general public, politicians, and key stakeholders in climate change. The conclusions from this study suggest that individuals are more inclined to act when they perceive a direct relevance to themselves. 3.2 Rethinking Climate Communications and the “Psychological Climate Paradox” There are five psychological barriers that prevent the facts about climate change from being internalized and influencing behavior: (a) climate seems distant in time, space, and influence, (b) incorrect framings backfire on the message, (c) dissonance (i.e., lack of meaningful action weakens attitudes), (d) doubt and dissonance strengthens denial, and (e) climate message is filtered through cultural identity (Stoknes, 2014). Current factual scientific information campaigns and economic cost-effectiveness have not been sufficient in convincing the public to support climate policies and although most countries have access to the necessary solutions, documents, and resources to solve the climate problem, politicians have been reluctant to the costs and prefer stronger demands from citizens. We must develop a multidisciplinary approach to climate communication that incorporates evidence-based practical communication that actively addresses the five psychological barriers to create a more personal message (Stoknes, 2014). Our work seeks to address these concerns in the media’s climate communication through topic modeling and sentiment analysis. Through topic modeling, we can detect phrase patterns to characterize the excerpts into one of the stated psychological

barriers, and sentiment analysis will provide us with subjectivity and objectivity identification. In other words, sentiment analysis allows us to determine if the author was writing to adhere to the audience’s emotions. 3.3 Trend and Thoughts: Understanding Climate Change Concerns Using Machine Learning and Social Media Data Shangguan et al. (2021) analyzed the number of tweets during major climate events using a Twitter dataset of tweets discussing climate change. The team performs two primary methods of analysis: topic modeling and sentiment analysis. For topic modeling, the team uses a latent dirichlet allocation (LDA) approach to summarize climate topics and calculate the probabilities of various words appearing in each topic. Shangguan et al. found many tweets either discussed the importance of climate change, aspects of climate change, or possible solutions to climate change. Their sentiment analysis using a pretrained RoBERTa-base model to classify sentiments as negative, neutral, or positive, produced results that were heavily skewed toward negative and neutral and very few positive sentiments. Though we are not analyzing Twitter data (climate change news article excerpts), we will use a similar approach for topic modeling. However, LDA and pretrained models will serve as a baseline for evaluating large language model (LLM) results. LLMs provide new flexibilities in text-based analysis and with prompt engineering becoming more prevalent, we will be able to accomplish both tasks with a single input instead of two separate methods (Shangguan et al., 2021).

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