Climate change sentiment on Twitter: An unsolicited public opinion poll (1505.03804v2)

Abstract: The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word "climate" collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness.

Overview of "Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll"

The paper "Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll" presents a comprehensive analysis of climate change-related discussions on Twitter over a six-year period from September 2008 to July 2014. The authors employ the Hedonometer, a sentiment analysis tool, to investigate the emotional tone of tweets containing the word "climate." This paper provides insights into public sentiment regarding climate change as expressed on social media, identifying trends and shifts in emotional response corresponding to significant climate-related events.

Methodology

The authors utilize the Hedonometer, an instrument designed to measure the happiness of text corpora based on the emotional valence of frequently used words. The analysis is conducted on approximately 1.5 million tweets containing "climate," filtered from a larger dataset accruing from Twitter’s gardenhose API. The sentiment scores are calculated and compared against a reference set of all tweets collected, allowing the authors to assess shifts in sentiment specifically tied to climate discussions.

Key Findings

The analysis reveals that tweets mentioning "climate" consistently exhibit lower happiness scores compared to the general body of tweets. This lower sentiment is primarily attributed to the use of emotionally charged language, typically negative, in reference to climate change. Words such as "threat," "deny," and "crisis" appear more frequently in climate discourse, suggesting that discussions are often framed within the context of urgency and conflict.

The paper identifies distinct sentiment patterns in response to natural disasters and pivotal climate-related events. Notably, events like Hurricane Sandy and the Forward on Climate Rally correspond with significant deviations in sentiment. For example, the aftermath of Hurricane Sandy saw a marked increase in climate-related discourse, with sentiment reflecting heightened concern and negativity. Conversely, sentiment around the Forward on Climate Rally showed a positive shift due to the rally's optimistic and proactive discourse.

Implications and Future Research

The findings demonstrate Twitter’s role as a barometer for public sentiment on climate change, providing a valuable, real-time lens into collective societal attitudes. The predominance of activist voices within the discourse suggests Twitter could serve as an effective platform for climate change advocacy and public awareness. This highlights potential for more structured engagement strategies using social media to effect policy support and drive behavioral change.

For future developments in this research area, coupling sentiment analysis with geospatial data could further enhance understanding of regional variations in climate sentiment. Additionally, integrating machine learning models to better parse sentiment nuances and actor-specific narratives could refine the granularity of insights derived from social media datasets.

Overall, this paper contributes to the expanding field of computational social science by leveraging large-scale social media data to elucidate public opinion dynamics on climate change. The paper underscores the interplay between digital platforms and public discourse, illustrating the potential for Twitter to shape and reflect climate change and related dialogues.