Presenting a model for predicting the reaction of virtual media customers using artificial intelligence

Daraei, Abdollah; Heidary-Sharifabad, Ahmad; Mortazavi, Seyd Mahmoud Reza

doi:JABM.3.2.15564.654985.41518

Presenting a model for predicting the reaction of virtual media customers using artificial intelligence

Document Type : The scientific research paper

Authors

Abdollah Daraei ¹

Ahmad Heidary-Sharifabad ²

Seyd Mahmoud Reza Mortazavi ¹

¹ Department of Communications, May.C., Islamic Azad University, Maybod, Iran.

² Department of Computer Engineering, May.C., Islamic Azad University, Maybod, Iran.

JABM.3.2.15564.654985.41518

Abstract

Given the frequency and importance of presence in social networks in such a way that these media have almost replaced traditional media, the importance of audience reaction can be considered. Today, every positive or negative event in virtual media is met with a reaction from individuals and users, and the magnitude and extent of this reaction has complicated the issues. Therefore, the present study is trying to present a model for predicting the reaction of virtual media audiences using artificial intelligence. Based on a qualitative approach based on in-depth interviews, the desired model was designed and then analyzed using the grounded theory approach. The presented model has 41 indicators based on open coding, 17 components based on axial coding, and 8 dimensions based on selective coding. Informational, emotional, and social factors are considered as causal conditions, individual and temporal factors as intervening factors, and political and economic factors as background factors that affect the outcomes, that is, communication factors. The validity and reliability of the model using the Holstey and Cohen's Kappa indices indicate a high and desirable level of model validity. In the following, artificial intelligence algorithms were used to test the presented model, and the results showed that the support vector machine algorithm is able to predict the audience's reaction in cyberspace with an accuracy of 92 percent.

Keywords

Prediction

virtual media

artificial intelligence

Subjects

organizational behavior

سورین، ورنر و تانکارد، جیمز. نظریه‌های ارتباطات، ترجمه علیرضا دهقان. چاپ ششم. تهران: دانشگاه تهران، 1392.

ISBN 978-964-03-4555-9

زکی پور، مهدی و نعمتی زاده، سینا و افشار کاظمی، محمد علی(1398)، پیش بینی پاسخ مشتریان در بازاریابی مستقیم با شبکه های عصبی چند لایه، فصلنامه کاوش های مدیریت بازرگانی، دوره 11، شماره 22

زکی پور، مهدی و نعمتی زاده، سینا و افشار کاظمی، محمد علی(1397)،طراحی الگوریتمی پویا برای کاوش در داده ها و پیش بینی صحیح پاسخ مشتری،مجله مدیریت توسعه و تحول 35، 39-48

نیک فر دستکی، فاطمه(1400)، نقش ارزش تبلیغات ارائه شده در رسانه ها بر پاسخ مخاطبان با توجه به نقش نگرش مخاطب،فصلنامه تحقیقات، شماره 33، دوره اول، سال چهارم، صص 1-17

de Oliveira, N.R.; Pisa, P.S.; Lopez, M.A.; de Medeiros, D.S.V.; Mattos, D.M.F. Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges. Information 2021, 12, 38. https://doi.org/10.3390/info12010038

Parra, J. L., & Chatterjee, S. (2024). Social media and artificial intelligence: Critical conversations and where do we go from here?. Education Sciences, 14(1), 68.

Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN computer science, 3(1), 74. https://doi.org/10.1007/s42979-021-00958-1

Abrardi, L., Cambini, C., & Rondi, L. (2022). Artificial intelligence, firms and consumer behavior: A survey. Journal of Economic Surveys, 36(4), 969-991.

Sydul Arefin, Rezwanul Parvez, Tanvir Ahmed, Mostofa Ahsan, Fnu Sumaiya, Fariha Jahin, and Munjur Hasan, Retail Industry Analytics: Unraveling Consumer Behavior through RFM Segmentation and Machine Learning, 2024 IEEE International Conference on Electro Information Technology

Elahe Mohammadreza, Reza Safabakhsh, Lecture quality assessment based on the audience reactions using machine learning and neural networks, Computers and Education: Artificial Intelligence 2 (2021) 100022

Sujin Choi , Hyopil Shin & Seung-Shik Kang (2020): Predicting Audience-Rated News Quality: Using Survey, Text Mining, and Neural Network Methods, Digital Journalism, DOI: 10.1080/21670811.2020.1842777

Vrochidis, A., Dimitriou, N., Krinidis, S., Panagiotidis, S., Parcharidis, S., & Tzovaras, D. (2024). A Deep Learning Framework for Monitoring Audience Engagement in Online Video Events. International Journal of Computational Intelligence Systems, 17(1), 124. https://doi.org/10.1007/s44196-024-00512-w

Yadav, S., Banshiwal, N., & Yadav, P. (2024). " Artificial Intelligence Integration in Social Media Marketing: A Comprehensive Analysis. ResearchGate.

Shahzad, M. F., Xu, S., Lim, W. M., Yang, X., & Khan, Q. R. (2024). Artificial intelligence and social media on academic performance and mental well-being: Student perceptions of positive impact in the age of smart learning. Heliyon, 10(8).

Okeleke, P. A., Ajiga, D., Folorunsho, S. O., & Ezeigweneme, C. (2024). Predictive analytics for market trends using AI: A study in consumer behavior. International Journal of Engineering Research Updates, 7(1), 36-49.

Nader, K., Toprac, P., Scott, S., & Baker, S. (2024). Public understanding of artificial intelligence through entertainment media. AI & society, 39(2), 713-726. https://doi.org/10.1007/s00146-022-01427-w

Omeish, F., Al Khasawneh, M., & Khair, N. (2024). Investigating the impact of AI on improving customer experience through social media marketing: An analysis of Jordanian Millennials. Computers in human behavior reports, 15, 100464.

Toromade, A. S., Soyombo, D. A., Kupa, E., & Ijomah, T. I. (2024). Technological innovations in accounting for food supply chain management. Finance & Accounting Research Journal, 6(7), 1248-1258.

Paul, P. O., & Iyelolu, T. V. (2024). Anti-Money Laundering Compliance and Financial Inclusion: A Technical Analysis of Sub-Saharan Africa. GSC Advanced Research and Reviews, 19(3), 336-343.

Raji, E., Ijomah, T. I., & Eyieyien, O. G. (2024). Data-Driven decision making in agriculture and business: The role of advanced analytics. Computer Science & IT Research Journal, 5(7), 1565- 575.

Olatunji, A. O., Olaboye, J. A., Maha, C. C., Kolawole, T. O., & Abdul, S. (2024). Harnessing the human microbiome: Probiotic and prebiotic interventions to reduce hospital-acquired infections and enhance immunity. International Medical Science Research Journal, 4(7), 771-787.

Hasanuzzaman, M., Hossain, M., Rahman, M. M., Rabbi, M. M. K., Khan, M. M., Zeeshan, M. A. F., ... & Shakir, M. N. (2025). Understanding Social Media Behavior in the USA: AI-Driven Insights for Predicting Digital Trends and User Engagement. Journal of Ecohumanism, 4(4), 119-141.

DOI: https://doi.org/10.62754/joe.v4i4.6717