Mapping the Climate Change Landscape on TikTok

• URL: http://arxiv.org/abs/2505.03813v1
• Date: Fri, 02 May 2025 13:21:33 GMT
• Title: Mapping the Climate Change Landscape on TikTok
• Authors: Alessia Galdeman, Luca Maria Aiello,
• Abstract summary: We collect the first TikTok dataset on climate topics.<n>We map the topics discussed on the platform on a climate taxonomy.<n>Results show TikTok creators primarily approach climate through the angle of lifestyle and dietary choices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media platforms shape climate action discourse. Mapping these online conversations is essential for effective communication strategies. TikTok's climate discussions are particularly relevant given its young, climate-concerned audience. In this work, we collect the first TikTok dataset on climate topics. We collected 590K videos from 14K creators along with their follower networks. By applying topic modeling to the video descriptions, we map the topics discussed on the platform on a climate taxonomy that we construct by consolidating existing categorizations. Results show TikTok creators primarily approach climate through the angle of lifestyle and dietary choices. By examining semantic connections between topics, we identified non-climate "gateway" topics that could draw new audiences into climate discussions.

Related papers

• ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method [61.76389719956301]
  We contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns time series climate data from ERA5, extreme weather events data from NOAA, and satellite image data from NASA.<n>Under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks.
  arXiv  Detail & Related papers  (2025-04-10T02:22:23Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM [77.17254959695218]
  Large Language Models (LLMs) like ChatGPT and Bard have shown impressive conversational abilities and excel in a wide variety of NLP tasks. We propose a light-weight Arabic Mini-ClimateGPT that is built on an open-source LLM and is specifically fine-tuned on a conversational-style instruction tuning Arabic dataset Clima500-Instruct. Our model surpasses the baseline LLM in 88.3% of cases during ChatGPT-based evaluation.
  arXiv  Detail & Related papers  (2023-12-14T22:04:07Z)
• Shifting Climates: Climate Change Communication from YouTube to TikTok [0.0]
  We studied the video content produced by 21 prominent YouTube creators who have expanded their influence to TikTok as information disseminators. We found that creators use a more emotionally resonant, self-referential, and action-oriented language compared to YouTube. We also observed a strong semantic alignment between videos and comments, with creators who excel at diversifying their TikTok content from YouTube typically receiving responses that more closely align with their produced content.
  arXiv  Detail & Related papers  (2023-12-08T11:10:10Z)
• Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging [29.413444722550356]
  We analyze how industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. We propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook.
  arXiv  Detail & Related papers  (2023-05-06T16:43:29Z)
• Towards Answering Climate Questionnaires from Unstructured Climate Reports [26.036105166376284]
  Activists and policymakers need NLP tools to process the vast and rapidly growing unstructured textual climate reports into structured form. We introduce two new large-scale climate questionnaire datasets and use their existing structure to train self-supervised models. We then use these models to help align texts from unstructured climate documents to the semi-structured questionnaires in a human pilot study.
  arXiv  Detail & Related papers  (2023-01-11T00:22:56Z)
• Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet [98.97255654573662]
  This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
  arXiv  Detail & Related papers  (2022-03-10T13:32:31Z)
• Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data [3.7384509727711923]
  We constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic modeling and natural language processing, we show the relationship between the number of tweets about climate change and major climate events. Our dataset was published on Kaggle and can be used in further research.
  arXiv  Detail & Related papers  (2021-11-06T19:59:03Z)
• ClimaText: A Dataset for Climate Change Topic Detection [2.9767565026354186]
  We introduce textscClimaText, a dataset for sentence-based climate change topic detection. We find that popular keyword-based models are not adequate for such a complex and evolving task. Our analysis reveals a great potential for improvement in several directions.
  arXiv  Detail & Related papers  (2020-12-01T13:42:37Z)
• Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
  In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context. This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG) We present this tool and the methodology that we used to develop it in the present article.
  arXiv  Detail & Related papers  (2020-11-03T21:22:42Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)

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.