SOCIAL MEDIA ANALYTICS IN CONTEMPORARY ART PROMOTION

Authors

  • Dr. Pooja Bhatt Assistant Professor, Department of Computer science and Engineering, Faculty of Engineering and Technology, Parul institute of Engineering and Technology, Parul University, Vadodara, Gujarat, India
  • Swati Chaudhary Assistant Professor, School of Business Management, Noida international University 203201
  • Dr. Shweta Joglekar Assistant professor, Bharati Vidyapeeth(Deemed to be University), Institute of Management and Entrepreneurship Development,Pune-411038
  • Vidyashree S M Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
  • Sakshi Pandey Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Ashmeet Kaur Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6738

Keywords:

Social Media Analytics, Contemporary Art Promotion, Audience Engagement, Digital Marketing, Data-Driven Strategy

Abstract [English]

Social media is a new phenomenon in the marketing and distribution of modern art in the digital era. Instagram, X (previously Twitter), Tik Tok and Facebook have transformed the experience of artist, gallery and institution interaction with the global audience. This research examines how the social media analytics support modern art promotion with a particular focus on how data-driven insights can be used to make creative and marketing choices. Knowing the audience behaviour, sentiment and engagement metrics, artists and curators are able to optimise on what they communicate, get maximum visibility and judge the campaign success in real time. In the article, the current literature on the topic of digital art marketing is reviewed and the way analytics tools (Meta Insights and Google Analytics) can be utilized to monitor the performance in terms of the primary metrics, such as impressions, reach, shares, and click-through rates. The paper discusses the place of analytics in fuelling artistic outreach and commercial success by employing a mixed methods research design incorporating content analysis, surveys and interviews to examine the connection between this concept and the other components of organizational strategy. Results indicate that the effective application of analytics is not only a better way to engage, but also attains inclusive and sustainable digital art communities. Yet, there are still difficulties associated with the interpretation of data, algorithmic bias, and other ethical aspects of personal data use. The conclusion of the paper is that the incorporation of social media analytics into art marketing systems can help artists and institutions to make decisions based on information, meet the demands of the audience, and survive in the changing digital landscape of modern art advertising.

References

Agüero-Torales, M. M., Salas, J. I. A., and López-Herrera, A. G. (2021). Deep Learning and Multilingual Sentiment Analysis on Social Media Data: An Overview. Applied Soft Computing, 107, Article 107373. https://doi.org/10.1016/j.asoc.2021.107373 DOI: https://doi.org/10.1016/j.asoc.2021.107373

Corradini, E., Virsino, D., and Virgili, L. (2021a). Investigating Negative Reviews and Detecting Negative Influencers in Yelp Through a Multi-Dimensional Social Network-Based Model. International Journal of Information Management, 60, Article 102377. https://doi.org/10.1016/j.ijinfomgt.2021.102377 DOI: https://doi.org/10.1016/j.ijinfomgt.2021.102377

Corradini, E., Virsino, D., and Virgili, L. (2021b). Investigating the Phenomenon of NSFW Posts in Reddit. Information Sciences, 566, 140–164. https://doi.org/10.1016/j.ins.2021.01.062 DOI: https://doi.org/10.1016/j.ins.2021.01.062

Du, K., Xing, F., Mao, R., and Cambria, E. (2024). Financial Sentiment Analysis: Techniques and Applications. ACM Computing Surveys, 56, 1–42. https://doi.org/10.1145/3649451 DOI: https://doi.org/10.1145/3649451

Gudka, M., Gardiner, K. L. K., and Lomas, T. (2023). Towards a Framework for Flourishing Through Social Media: A Systematic Review of 118 Research Studies. The Journal of Positive Psychology, 18, 86–105. https://doi.org/10.1080/17439760.2021.1991447 DOI: https://doi.org/10.1080/17439760.2021.1991447

Jang, H., Rempel, E., Roth, D., Carenini, G., and Janjua, N. Z. (2021). Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis. Journal of Medical Internet Research, 23, e25431. https://doi.org/10.2196/25431 DOI: https://doi.org/10.2196/25431

Li, X., Chan, S., Zhu, X., Pei, Y., Ma, Z., Liu, X., and Shah, S. (2023). Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks. arXiv. DOI: https://doi.org/10.18653/v1/2023.emnlp-industry.39

Mahalingham, T., McEvoy, P. M., and Clarke, P. J. F. (2023). Assessing the Validity of Self-Report Social Media Use: Evidence of no Relationship with Objective Smartphone Use. Computers in Human Behavior, 140, Article 107567. https://doi.org/10.1016/j.chb.2022.107567 DOI: https://doi.org/10.1016/j.chb.2022.107567

Pang, H., Qin, K., and Ji, M. (2022). Can Social Network Sites Facilitate Civic Engagement? Assessing Dynamic Relationship Between Social Media and Civic Activities Among Young People. Online Information Review, 46, 79–94. https://doi.org/10.1108/OIR-10-2020-0453 DOI: https://doi.org/10.1108/OIR-10-2020-0453

Ronzhyn, A., Cardenal, A. S., and Rubio, A. B. (2023). Defining Affordances in Social Media Research: A Literature Review. New Media and Society, 25, 3165–3188. https://doi.org/10.1177/14614448221135187 DOI: https://doi.org/10.1177/14614448221135187

Shen, R.-P., Liu, D., Wei, X., and Zhang, M. (2022). Your Posts Betray You: Detecting Influencer-Generated Sponsored Posts by Finding the Right Clues. Information and Management, 59, Article 103719. https://doi.org/10.1016/j.im.2022.103719 DOI: https://doi.org/10.1016/j.im.2022.103719

Wang, Z., Xie, Q., Feng, Y., Ding, Z., Yang, Z., and Xia, R. (2024). Is ChatGPT a Good Sentiment Analyzer? A Preliminary Study. Arxiv.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv.

Xing, F. (2024). Designing Heterogeneous LLM Agents for Financial Sentiment Analysis. arXiv.

Yang, L., Chen, H., Li, Z., Ding, X., and Wu, X. (2024). Give us the Facts: Enhancing Large Language Models With Knowledge Graphs for Fact-Aware Language Modeling. IEEE Transactions on Knowledge and Data Engineering, 36, 3091–3110. https://doi.org/10.1109/TKDE.2024.3360454 DOI: https://doi.org/10.1109/TKDE.2024.3360454

Zhang, W., Deng, Y., Liu, B., Pan, S., and Bing, L. (2023). Sentiment analysis in the era of large language models: A reality check. arXiv. https://doi.org/10.18653/v1/2024.findings-naacl.246 DOI: https://doi.org/10.18653/v1/2024.findings-naacl.246

Zhang, W., Li, X., Deng, Y., Bing, L., and Lam, W. (2022). A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges. IEEE Transactions on Knowledge and Data Engineering, 35, 11019–11038. https://doi.org/10.1109/TKDE.2022.3230975 DOI: https://doi.org/10.1109/TKDE.2022.3230975

Zhong, Q., Ding, L., Liu, J., Du, B., and Tao, D. (2023). Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-Tuned BERT. arXiv.

Downloads

Published

2025-12-16

How to Cite

Bhatt, P., Chaudhary, S., Joglekar, S., Vidyashree S M, Pandey, S., & Kaur, A. (2025). SOCIAL MEDIA ANALYTICS IN CONTEMPORARY ART PROMOTION. ShodhKosh: Journal of Visual and Performing Arts, 6(2s), 356–366. https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6738