Information Sharing and Communication Through Climate Change Images on Social Media
Bibliographic record
Abstract
In today’s world, the most popular and powerful tool to disseminate information on complex topics including climate change is social media. Information shared through various forms of texts and visual content on social media makes complicated topics easier to comprehend. The emotions, thoughts, and feelings expressed through images carry values that are far greater than those of texts. These features of images turn them into more meaningful content. People can be motivated to take action to establish and manage sustainable development. This research attempts to analyze the images posted on social media to build awareness among its users on climate change in Bangladesh. The accounts of Bangladeshi social media users are analyzed to find the various forms of images posted on social media and how people are engaging with these images. Also, social media users’ perspectives and awareness of climate change are studied here. Content analysis of 63 images on social media and semi-structured interviews with 10 social media users suggest that Bangladeshi social media users are focusing mostly on the impact of climate change, and less on its causes and solutions. Images containing the general public are more popular than images with political leaders, celebrities, and scientists which contradicts other findings. However social media users expressed less concern about the issue and believed that it is a matter of concern for the authority. Nevertheless, the younger generation in Bangladesh is taking a more active part in initiating actions to reduce the impact of climate change. As this younger generation ages, the future may bring positive changes if policies are made to support their initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.029 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".