PRINT TO PLATFORM A VISUAL-DESIGN COMPARISON OF CREDIBILITY CUES IN INDIAN NEWSPAPERS AND WEB NEWS PORTALS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i2.2025.6682Keywords:
Visual Credibility, News Design, Indian Newspapers, Web Portals, Trust CuesAbstract [English]
This paper uses a comparative visual-design overview of credibility cues in Indian print newspapers and web-based news portals and how the use of layout, typography, color- schemes and imagery contribute to the overall perception of trust in the audience. Since news consumption is becoming less and less print-based and more and more digital-based, the visual grammar of credibility has started to develop in a manner that is both media-specifically constrained, and new design philosophically inclined. The research, based on mixed content-analytic and comparative research design, assesses various newspaper sources including The Hindu, The Times of India, and Dainik Bhaskar as well as digital portals, including NDTV, Scroll, and The Indian Express. It is the case those credibility cues are classified in terms of structural (layers of layout hierarchy, grid discipline), linguistic (headline typography, serif vs sans-serif orientation), and symbolic (color temperature, image framing, iconography) layers. It has been found that print newspapers are heavily dependent on visual traditions that are stable column structures, serif type dominance, and limited use of color to convey authority and editorial seriousness. Digital portals, in their turn, rely on vibrant layouts, more vivid colours, interactive types and multimedia additions to produce a sense of urgency and affordability. However, the contrasts do not hinder the incessant bargaining of the ratio between aesthetics, clarity and perceived reliability by both the media. The study will contribute to the ongoing discussions on the field of media design, digital journalism and visual communication given the fact that it offers a systematic way of approaching the process of visual encoding and decoding of credibility across media. It also indicates divergences created by mediums that affect trust to the reader and the need to have a design conscious editorial processes in the age of fast changing news systems.
References
Bontridder, N., and Poullet, Y. (2021). The Role of Artificial Intelligence in Disinformation. Data and Policy, 3, e32. https://doi.org/10.1017/dap.2021.20 DOI: https://doi.org/10.1017/dap.2021.20
Ghorbanpour, F., Ramezani, M., Fazli, M., and Rabiee, H. (2023). FNR: A Similarity- and Transformer-Based Approach to Detect Multimodal Fake News in Social Media. Social Network Analysis and Mining, 13, Article 56. https://doi.org/10.1007/s13278-023-01065-0 DOI: https://doi.org/10.1007/s13278-023-01065-0
Hangloo, S., and Arora, B. (2022). Combating Multimodal Fake News on Social Media: Methods, Datasets, and Future Perspective. Multimedia Systems, 28, 2391–2422. https://doi.org/10.1007/s00530-022-00966-y DOI: https://doi.org/10.1007/s00530-022-00966-y
Hartzog, W., Selinger, E., and Gunawan, J. (2023). Privacy Nicks: How the Law Normalizes Surveillance (SSRN Working Paper No. 4384541). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4384541 DOI: https://doi.org/10.2139/ssrn.4384541
Hua, J., Cui, X., Li, X., Tang, K., and Zhu, P. (2023). Multimodal Fake News Detection Through Data Augmentation-Based Contrastive Learning. Applied Soft Computing, 136, Article 110125. https://doi.org/10.1016/j.asoc.2023.110125 DOI: https://doi.org/10.1016/j.asoc.2023.110125
Li, W., Gu, C., Chen, J., Ma, C., Zhang, X., Chen, B., and Wan, S. (2024). DLS-GAN: Generative Adversarial Nets for Defect Location-Sensitive Data Augmentation. IEEE Transactions on Automation Science and Engineering, 21, 5173–5189. https://doi.org/10.1109/TASE.2023.3309629 DOI: https://doi.org/10.1109/TASE.2023.3309629
Lindsay, G. (2020). Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future. Journal of Cognitive Neuroscience, 33, 2017–2031. https://doi.org/10.1162/jocn_a_01544 DOI: https://doi.org/10.1162/jocn_a_01544
Palani, B., Elango, S., and Viswanathan, V. K. (2022). CB-Fake: A Multimodal Deep Learning Framework for Automatic Fake News Detection Using Capsule Neural Network and BERT. Multimedia Tools and Applications, 81, 5587–5620. https://doi.org/10.1007/s11042-021-11782-3 DOI: https://doi.org/10.1007/s11042-021-11782-3
Peng, X., and Xintong, B. (2022). An Effective Strategy for Multi-Modal Fake News Detection. Multimedia Tools and Applications, 81, 13799–13822. https://doi.org/10.1007/s11042-022-12290-8 DOI: https://doi.org/10.1007/s11042-022-12290-8
Ruffo, G., Semeraro, A., Giachanou, A., and Rosso, P. (2023). Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: A Tale of Networks and Language. Computer Science Review, 47, Article 100531. https://doi.org/10.1016/j.cosrev.2022.100531 DOI: https://doi.org/10.1016/j.cosrev.2022.100531
Segura-Bedmar, I., and Alonso-Bartolome, S. (2022). Multimodal Fake News Detection. Information, 13, Article 284. https://doi.org/10.3390/info13060284 DOI: https://doi.org/10.3390/info13060284
Uppada, S. K., and Patel, P. (2023). An Image- and Text-Based Multimodal Model for Detecting Fake News in OSNs. Journal of Intelligent Information Systems, 61, 367–393. https://doi.org/10.1007/s10844-022-00764-y DOI: https://doi.org/10.1007/s10844-022-00764-y
Xue, J., Wang, Y., Tian, Y., Li, Y., Shi, L., and Wei, L. (2021). Detecting Fake News by Exploring the Consistency of Multimodal Data. Information Processing and Management, 58, Article 102610. https://doi.org/10.1016/j.ipm.2021.102610 DOI: https://doi.org/10.1016/j.ipm.2021.102610
Zhao, J., Zhao, Z., Shi, L., Kuang, Z., and Liu, Y. (2023). Collaborative Mixture-of-Experts Model for Multi-Domain Fake News Detection. Electronics, 12, Article 3440. https://doi.org/10.3390/electronics12163440 DOI: https://doi.org/10.3390/electronics12163440
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Copyright (c) 2025 Ankit Kumar, Dr. Jyotsana Thakur, Dr. Kaushik Mishra, Rahul Gupta, Abhishika Sharma

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