Social Measurement of Iranian VODs among Persian Twitter Users

Document Type : Call for Special Issue on Development of Creative Industries

Authors

1 Imam Sadiq University

2 IRIB University

Abstract

With the advent of digital technologies and the emergence of VOD media services, the landscape of cultural and creative industries has undergone a dramatic transformation. In particular, the market of these media in Iran has witnessed significant growth and offers a diverse range of content to meet the preferences of Persian-speaking audiences around the world. This research deals with the social assessment of Iranian VOD media and their opinion analysis among Persian Twitter users. The purpose of this study is to measure the influence and dynamics of Iranian VODs in the framework of Persian Twitter using data mining methods, computational social sciences, sentiment analysis and theme analysis. Through thematic analysis, six overarching themes were extracted: 1. Dark strains of VOD, 2. Broadcasting monitoring of VOD, 3. Popularity of VOD and its effective factors, 4. Filter and closure of VOD, 5. Actors appearing on VOD after riots in 1401, and 6. Purchasing VOD subscriptions. These themes reflect diverse views and conversations about Iranian VODs.

Using social science computational methods, sentiment analysis, and user surveys, a deeper insight into the social measurement of Iranian VODs is obtained. Sentiment analysis of tweets showed that negative sentiments towards these media among Persian Twitter users were 59%, positive sentiments 39%, and neutral sentiments 2%. In addition, data mining techniques were used to investigate the percentage of attention towards Iranian VODs and the feelings of users towards each of them.

Keywords


Alvin Hilmy, M. (2023). “Analisis Tingkat Kepuasan Pengguna pada Platform Video on Demand (VOD) Disney+ Hotstar Menggunakan Model DeLone & McLean yang Dimodifikasi”. Bachelor's thesis, Fakultas Sains dan Teknologi UIN Syarif Hidayatullah Jakarta.
Amani Hamdani, M. H. (2019). “Data Mining Capabilities Of Big Data In The Development Of Advertising-Oriented Business Model Of Video On Demand Services; Case Study: Filimo”. Master's Thesis, Soore University, Faculty Of Culture And Communication. {In Persian}
Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research, vol. 1. London: SAGE.
Bail C.A. (2014). “The cultural environment: measuring culture with big data”. Theory and Society, 43, pp. 465–482.
Baladron, M., and Rivero, E. (2019). “Video-on-demand services in Latin America: Trends and challenges towards access, concentration and regulation”. Journal of Digital Media & Policy, 10 (1), pp. 109-126.
Baldassarri D., and Bearman P. (2007). “Dynamics of political polarization”. American sociological review, 72(5), pp. 784-811.  
Borzooei, M., Jahanbazi, R., and Tamaddon, A. (2022). “Comparative Twiplomacy of the Islamic Republic of Iran and the United States of America: Case Study of the Martyrdom of General Qassem Soleimani”. Journal of Interdisciplinary Studies in Communication and Media, 5(16), pp. 5-42. doi: 10.22085/jiscm.2022.312333.1282 {In Persian}
Braun, V., and Clarke, V. (2006). “Using thematic analysis in psychology”. Qualitative Research in Psychology, 3 (2), pp. 77-101.
Bruch E., and Atwell, J. (2015). “Agent-based models in empirical social research”. Sociological methods & research, 44(2), pp. 186-221. 
Centola D., and Macy, M. (2007). “Complex contagions and the weakness of long ties”. American journal of Sociology, 113(3), pp. 702-734. 
Coletta, L., da Silva, N., and Hruschka, F. (2014). “Combining classification and clustering for tweet sentiment analysis”. In 2014 Brazilian conference on intelligent systems (pp. 210-215). IEEE.
Common media terms (2016). Secretariat of the Supreme Council of New Media. {In Persian}
Debiran, M. H. (2019). "Future Research On The Role Of VOD In The Distribution And Marketing Of Movies From The Point Of View Of Film Office Producers, Owners Of Interactive Video Media And Media Specialists". Master's Thesis, Allameh Tabatabai University, Faculty Of Communication Sciences. {In Persian}
Evans, J. A., and Aceves, P. (2016). “Machine translation: mining text for social theory”. Annual review of sociology, 42, pp. 21–50.
Farahani, M., Gharachorloo, M., Farahani, M., and Manthouri, M. (2021). “ParsBERT: Transformer-based model for persian language understanding”. Neural Processing Letters, 53, pp. 3831-3847. https://doi.org/10.1007/s11063-021-10528-4
Flick, U. (2018). An introduction to qualitative research, translated by Hadi Jalili. Tehran: Ney Publishing. {In Persian}
Frow, J., and Bennett, T. (2008). The SAGE Handbook of Cultural Analysis, London: Sage.
Golder S., and Macy M. (2011). “Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures”. Science 333(6051): pp. 1878–1881.
Helbing D., Farkas, I., and Vicsek, T. (2000). “Simulating dynamical features of escape panic”. Nature 407: pp. 487–490
jalilvand khosravi, M., Maghsoudi, M., and Salavatian, S. (2022). “Identifying and Clustering Users of VOD Platforms Using SNA Technique: A case study of Cinemamarket”. New Marketing Research Journal, 11(4), pp. 1-20. {In Persian} doi: 10.22108/nmrj.2021.126442.2324
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., … and Van Alstyne, M. (2009). “Computational social science”. Science 323 (5915), pp. 721– 23.
Leist, A. K., Klee, M., Kim, J. H., Rehkopf, D. H., Bordas, S. P., Muniz-Terrera, G., and Wade, S. (2022). “Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences”. Science Advances, 8 (42).
Macy M. W., and Willer R. (2002). “From factors to actors: computational sociology and agent-based modeling”. Annual review of sociology, 28 (1), PP. 143–166.
Messaoudi, C., Guessoum, Z. and Ben Romdhane, L. (2022). “Opinion mining in online social media: a survey”. Social Network Analysis and Mining, 12 (1), 25.  https://doi.org/10.1007/s13278-021-00855-8
Molina, M., and Garip, F. (2019). “Machine learning for sociology”. Annual Review of Sociology. 45, pp. 27–45.
Movahed Majd, M., Niknejat, Z., and Abbasi shovazi, M. (2015). “Representation of Muharram Rituals in West Media; Semiotic Analysis of TotallyCoolPix Websites Photos of Muharam and Ashura”. Journal of Iranian Cultural Research, 8(3), pp. 31-59. {In Persian} doi: 10.7508/ijcr.2015.31.002
Nelson, L. K. (2017). “Computational grounded theory: a methodological framework”. Sociological Methods & Research, 49 (1), pp. 3–42.
Pentland, A. (2015). Social Physics: How Social Networks Can Make Us Smarter. New York, NY: Penguin Books.
Popescu, O., and Strapparava, C. (2014). “Time corpora: epochs, opinions and changes”. Knowl Based Syst, 69, pp. 3–13.
Ravi, K., and Ravi, V. (2015). “A survey on opinion mining and sentiment analysis: tasks, approaches and applications”. Knowl Based Syst, 89, pp. 14–46.
Salganik, M. (2018). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton Univ. Press.
Satra (2021). The path taken and the paths not taken, Available In: https://satra.ir/2022/9968. {In Persian}
Schober, M., Pasek, J., Guggenheim, L., Lampe, C., and Conrad, F. (2016). “Social Media Analyses for Social Measurement”. Public Opinion Quarterly, 80 (1), pp. 180–211. https://doi.org/10.1093/poq/nfv048
Small, T. A. (2011). “What the hashtag? A content analysis of Canadian politics on Twitter”. Information, Communication & Society, 14(6), pp. 872–895.
Social Media (2015). Oxford Reference. Available at: http://www.oxfordreference.com.pallas2.tcl.sc.edu/view/10.1093/acref/9780191744150.001.0001/acref-9780191744150-e-4542.
Social Media (2022). Available In: https://www.britannica.com/topic/social-media
Tellez, E., Miranda-Jim´enez, S., Graff, M., Moctezuma, D., Siordia, O., and Villase˜nor, E. (2017). “A case study of spanish text transformations for twitter sentiment analysis”. Expert Systems with Applications, 81, pp. 457–471.
Watts, D. J. (1999). “Networks, dynamics, and the small-world phenomenon”. American Journal of sociology, 105 (2), pp. 493–527.