CLIMATE CHANGE AS VISUAL DISCOURSE: AN INTERDISCIPLINARY STUDY OF NEWSPAPER REPRESENTATION AND AUDIENCE PERCEPTION IN HIMACHAL PRADESH

Aditi Sharma; Dr. Vinod

doi:10.29121/shodhkosh.v7.i4s.2026.7604

Authors

Aditi Sharma Research Scholar, University Institute of Media Studies, Chandigarh University, Mohali, Punjab, India
Dr. Vinod Associate Professor, University Institute of Media Studies, Chandigarh University, Mohali, Punjab, India

DOI:

https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7604

Keywords:

Climate Change Communication, Visual Discourse, Newspaper Representation, Audience Perception, Environmental Media, Himachal Pradesh, Visual Framing, Media Influence, Environmental Awareness, Climate Communication Strategies

Abstract [English]

Climate change has become a significant issue of global concern and it has become important to employ effective communication strategies to increase the awareness and participation of people. In different media outlets, the newspapers still have a major role to play in terms of spreading environmental information especially in places where the print media still hold sway. This paper explores climate change as a visual discourse through an examination of visual representations of climate change in newspaper images and how such representations are perceived by the audience in Himachal Pradesh which is a climate-sensitive area within the Indian Himalayan ecosystem. The study assumes the mixed-method approach, which involves visual content analysis of the chosen newspapers and the analysis of audience perception through surveys. The results indicate that the newspaper coverage is mainly characterized with disaster oriented images, including floods, landslides and forest fires that highlight the immediate effects of climate change. Although these images are helpful in attracting attention and creating emotional appeal, they can be rather shallow and cannot convey the processes and solutions to climate in the long-term perspective. Audience analysis shows that there is a high level of awareness and emotional response with a high correlation between visual perception and exposure to visuals. Nevertheless, the study also finds out that there is a disconnection between awareness and behavior change, since more awareness does not necessarily lead to sustainable environmental behavior. The paper highlights the need to have a more balanced visual representation that incorporates informative as well as solution-oriented visuals to increase the level of knowledge and encourage people to act. The work is an interdisciplinary contribution as it combines the ideas of the media studies, environmental science, and social psychology. It also provides valuable information to policy makers, environmental communicators and journalists to enhance effective communication of climate change. This study underlines the significance of strategic media representation in the context of tackling one of the most urgent issues of our days by focusing on the role of visual discourse.

References

Bodha, K. D., Yadav, V. K., and Mukherjee, V. (2020). Formulation and Application of Quantum-Inspired Tidal Firefly Technique for Multiple-Objective Mixed Cost-Effective Emission Dispatch. Neural Computing and Applications, 32, 9217–9232. https://doi.org/10.1007/s00521-019-04433-0

Bogl, M., Aigner, W., Filzmoser, P., Lammarsch, T., Miksch, S., and Rind, A. (2013). Visual Analytics for Model Selection in Time Series Analysis. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2237–2246. https://doi.org/10.1109/TVCG.2013.222

Bryant, R. A. (2019). Post-Traumatic Stress Disorder: A State-of-the-Art Review of Evidence and Challenges. World Psychiatry, 18(3), 259–269. https://doi.org/10.1002/wps.20656

Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D. S., and Ertl, T. (2012). Spatiotemporal Social Media Analytics for Abnormal Event Detection and Examination Using Seasonal-Trend Decomposition. In Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST) (143–152). IEEE. https://doi.org/10.1109/VAST.2012.6400557

Gao, T., Hullman, J. R., Adar, E., Hecht, B., and Diakopoulos, N. (2014). Newsviews: An Automated Pipeline for Creating Custom Geovisualizations for News. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (3005–3014). ACM. https://doi.org/10.1145/2556288.2557228

Kumar, S., Rani, S., Jain, A., Kumar, M., and Jaglan, P. (2023). Automatic Face Mask Detection Using Deep Learning-Based Mobilenet Architecture. In Proceedings of IC3I 2023 (1075–1080). IEEE. https://doi.org/10.1109/IC3I59117.2023.10397772

McLaughlin, K. A., Koenen, K. C., Bromet, E. J., Karam, E. G., Liu, H., Petukhova, M., Ruscio, A. M., Sampson, N. A., Stein, D. J., Aguilar-Gaxiola, S., Alonso, J., and others. (2017). Childhood Adversities and Posttraumatic Stress Disorder: Evidence for Stress Sensitisation in the World Mental Health Surveys. The British Journal of Psychiatry, 211(5), 280–288. https://doi.org/10.1192/bjp.bp.116.197640

Panicker, S. S., and Gayathri, P. (2019). A Survey of Machine Learning Techniques in Physiology-Based Mental Stress Detection Systems. Biocybernetics and Biomedical Engineering, 39(2), 444–469. https://doi.org/10.1016/j.bbe.2019.01.004

Poongodi, M., Sharma, A., Hamdi, M., Maode, M., and Chilamkurti, N. (2021). Smart Healthcare in Smart Cities: Wireless Patient Monitoring System Using IOT. The Journal of Supercomputing, 77(11), 12230–12255. https://doi.org/10.1007/s11227-021-03765-w

Rani, S., Lakhwani, K., and Kumar, S. (2023). Knowledge Vector Representation of Three-Dimensional Convex Polyhedrons and Reconstruction of Medical Images Using Knowledge Vector. Multimedia Tools and Applications, 82, 36449–36477. https://doi.org/10.1007/s11042-023-14894-0

Sahoo, S. (2022). Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management. International Journal of Photoenergy, 2022, 1–12. https://doi.org/10.1155/2022/9353470

Venkatanaresh, M., and Kullayamma, I. (2022). A New Approach for Crop Type Mapping in Satellite Images Using Hybrid Deep Capsule Auto Encoder. Knowledge-Based Systems, 256, 109881. https://doi.org/10.1016/j.knosys.2022.109881