Modeling Social Television Analytics and Twitter-Enabled Audience Engagement – A study of Cross-Platform Television Audience in Nigeria
Keywords:
Social Television Analytics, Twitter, Television Ratings, Audience Measurement, NigeriaAbstract
The shift to multi-platform television means that an understanding of the social interactions of television audience and measurement of audience engagement across all television viewing platforms are necessary to understand the behavioural pattern of television audience. The study is an attempt to bridge the scholarship gap of social television analytics and industry practice of understanding television audience by proposing an analytical model to audience research of digital television. As a result, the study asks what are the relationships between television audience experience and audience engagement of social media-enabled communication by television services? To understand the relationship between television viewership trends of the selected demography and social interactions of television audience on social media, we used correlation and regressions models to examine the relationship between television audience ratings and twitter data of audience engagement. The results of the correlation analysis are indicative of an entangled relationship between the audience ratings and social interactions of television audience on Twitter. Also, the regression analysis implies that a change in the value of audience ratings may not necessarily affect social interactions of television audience or the pattern of content consumption on social media.
References
Aina, T., Ye, Z., Dai, Z., & Jianghui, C. (2014). Field tests of two-way television audience measurement system. Paper presented at the 2014 IEEE international symposium on broadband multimedia systems and broadcasting.
Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools and platforms. Ai & Society, 30(1), 89-116.
Baym, N. K. (2013). Data not seen: The uses and shortcomings of social media metrics. First Monday, 18(10).
Bekmamedova, N., & Shanks, G. (2014). Social media analytics and business value: a theoretical framework and case study. Paper presented at the 2014 47th Hawaii international conference on system sciences.
Buschow, C., Schneider, B., & Ueberheide, S. (2014). Tweeting television: Exploring communication activities on Twitter while watching TV. In: De Gruyter.
Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74-81.
Gallego, F. (2013). Social TV Analytics: Nuevas métricas para una nueva forma de ver televisión. Index. Comunicación: Revista científica en el ámbito de la Comunicación Aplicada, 3(1), 13-39.
Ghashghai, J., & Barton, J. M. (2015). Audience measurement system. In: Google Patents.
Guo, C., & Saxton, G. D. (2014). Tweeting social change: How social media are changing nonprofit advocacy. Nonprofit and voluntary sector quarterly, 43(1), 57-79.
Guo, M., & Chan-Olmsted, S. M. (2015). Predictors of social television viewing: How perceived program, media, and audience characteristics affect social engagement with television programming. Journal of Broadcasting & Electronic Media, 59(2), 240-258.
Hayes, D. (2014). Don’t Confuse a TV Show’s Twitter Impact with Its Ratings Power. Forbes, November, 30.
Hill, S. (2014). TV audience measurement with big data. Big data, 2(2), 76-86.
Hu, H., Huang, J., Zhao, H., Wen, Y., Chen, C. W., & Chua, T.-S. (2014). Social tv analytics: a novel paradigm to transform tv watching experience. Paper presented at the Proceedings of the 5th ACM Multimedia Systems Conference.
Hu, H., Wen, Y., Gao, Y., Chua, T.-S., & Li, X. (2015). Toward an SDN-enabled big data platform for social TV analytics. IEEE network, 29(5), 43-49.
Kelly, J. (2019). Television by the numbers: the challenges of audience measurement in the age of Big Data. Convergence, 25(1), 113-132.
Kenix, L. J., & Abikanlu, F. (2019). A comparative analysis of social media messaging by African-centred LGBT refugee NGOs. Journal of African Media Studies, 11(3), 313-329.
Kosterich, A., & Napoli, P. M. (2016a). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. 17(3), 254-271.
Kosterich, A., & Napoli, P. M. (2016b). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254-271.
Larsen, H. H., Forsberg, J. M., Hemstad, S. V., Mukkamala, R. R., Hussain, A., & Vatrapu, R. (2016). Tv ratings vs. social media engagement: Big social data analytics of the scandinavian tv talk show skavlan. Paper presented at the 2016 IEEE International Conference on Big Data (Big Data).
Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., & Shook, E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday.
Lu, D., Kempter, P., & Feininger, W. (2002). Audience measurement system for digital television. In: Google Patents.
Méadel, C. (2015). Moving to the peoplemetered audience: A sociotechnical approach. European Journal of Communication, 30(1), 36-49.
Moe, H., Poell, T., & van Dijck, J. (2016). Rearticulating audience engagement: Social media and television. Television & New Media, 17(2), 99-107.
Moor, L., & Lury, C. (2011). Making and measuring value: comparison, singularity and agency in brand valuation practice. Journal of Cultural Economy, 4(4), 439-454.
Murschetz, P.-C., & Schlütz, D. (2018). Big Data and Television Broadcasting: a Critical Reflection on Big Data's Surge to Become a New Techno-Economic Paradigm and its Impacts on the Concept of the Addressable Audience. Big Data and Television Broadcasting: a Critical Reflection on Big Data's Surge to Become a New Techno-Economic Paradigm and its Impacts on the Concept of the Addressable Audience, 23-38.
Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal on Media Management, 18(1), 9-24.
Nielsen. (2013). NIELSEN LAUNCHES ‘NIELSEN TWITTER TV RATINGS’. Retrieved from www.nielsen.com/us/en/press-releases/2013/nielsen-launches-nielsen-twitter-tv-ratings/
Nielsen. (2020). ABOUT NIELSEN SOCIAL. Retrieved from https://www.nielsensocial.com/about/
Oh, C., Sasser, S., & Almahmoud, S. (2015). Social media analytics framework: the case of Twitter and Super Bowl ads. Journal of Information Technology Management, 26(1), 1-18.
Oh, C., Yergeau, S., Woo, Y., Wurtsmith, B., & Vaughn, S. (2015). Is Twitter psychic? Social media analytics and television ratings. Paper presented at the 2015 Second International Conference on Computing Technology and Information Management (ICCTIM).
Oyeyemi, T. (2020). Minister Appoints Team To Design Framework For Audience Measurement System. Retrieved from www.fmic.gov.ng/minister-appoints-team-to-design-framework-for-audience-measurement-system/
Pensa, R. G., Sapino, M. L., Schifanella, C., & Vignaroli, L. J. I. C. I. M. (2016). Leveraging cross-domain social media analytics to understand TV topics popularity. 11(3), 10-21.
Pynta, P., Seixas, S. A., Nield, G. E., Hier, J., Millward, E., & Silberstein, R. B. J. J. o. A. R. (2014). The Power of Social Television: Can Social Media Build Viewer Engagement?: A New Approach to Brain Imaging of Viewer Immersion. 54(1), 71-80.
Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3(4), 1277-1291.
Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13-16.
Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools and platforms. Ai & Society, 30(1), 89-116.
Baym, N. K. (2013). Data not seen: The uses and shortcomings of social media metrics. First Monday, 18(10).
Bekmamedova, N., & Shanks, G. (2014). Social media analytics and business value: a theoretical framework and case study. Paper presented at the 2014 47th Hawaii international conference on system sciences.
Buschow, C., Schneider, B., & Ueberheide, S. (2014). Tweeting television: Exploring communication activities on Twitter while watching TV. In: De Gruyter.
Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74-81.
Gallego, F. (2013). Social TV Analytics: Nuevas métricas para una nueva forma de ver televisión. Index. Comunicación: Revista científica en el ámbito de la Comunicación Aplicada, 3(1), 13-39.
Ghashghai, J., & Barton, J. M. (2015). Audience measurement system. In: Google Patents.
Guo, C., & Saxton, G. D. (2014). Tweeting social change: How social media are changing nonprofit advocacy. Nonprofit and voluntary sector quarterly, 43(1), 57-79.
Guo, M., & Chan-Olmsted, S. M. (2015). Predictors of social television viewing: How perceived program, media, and audience characteristics affect social engagement with television programming. Journal of Broadcasting & Electronic Media, 59(2), 240-258.
Hayes, D. (2014). Don’t Confuse a TV Show’s Twitter Impact with Its Ratings Power. Forbes, November, 30.
Hill, S. (2014). TV audience measurement with big data. Big data, 2(2), 76-86.
Hu, H., Huang, J., Zhao, H., Wen, Y., Chen, C. W., & Chua, T.-S. (2014). Social tv analytics: a novel paradigm to transform tv watching experience. Paper presented at the Proceedings of the 5th ACM Multimedia Systems Conference.
Hu, H., Wen, Y., Gao, Y., Chua, T.-S., & Li, X. (2015). Toward an SDN-enabled big data platform for social TV analytics. IEEE network, 29(5), 43-49.
Kelly, J. (2019). Television by the numbers: the challenges of audience measurement in the age of Big Data. Convergence, 25(1), 113-132.
Kenix, L. J., & Abikanlu, F. (2019). A comparative analysis of social media messaging by African-centred LGBT refugee NGOs. Journal of African Media Studies, 11(3), 313-329.
Kosterich, A., & Napoli, P. M. (2016a). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. 17(3), 254-271.
Kosterich, A., & Napoli, P. M. (2016b). Reconfiguring the audience commodity: The institutionalization of social TV analytics as market information regime. Television & New Media, 17(3), 254-271.
Larsen, H. H., Forsberg, J. M., Hemstad, S. V., Mukkamala, R. R., Hussain, A., & Vatrapu, R. (2016). Tv ratings vs. social media engagement: Big social data analytics of the scandinavian tv talk show skavlan. Paper presented at the 2016 IEEE International Conference on Big Data (Big Data).
Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., & Shook, E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday.
Lu, D., Kempter, P., & Feininger, W. (2002). Audience measurement system for digital television. In: Google Patents.
Méadel, C. (2015). Moving to the peoplemetered audience: A sociotechnical approach. European Journal of Communication, 30(1), 36-49.
Moe, H., Poell, T., & van Dijck, J. (2016). Rearticulating audience engagement: Social media and television. Television & New Media, 17(2), 99-107.
Moor, L., & Lury, C. (2011). Making and measuring value: comparison, singularity and agency in brand valuation practice. Journal of Cultural Economy, 4(4), 439-454.
Murschetz, P.-C., & Schlütz, D. (2018). Big Data and Television Broadcasting: a Critical Reflection on Big Data's Surge to Become a New Techno-Economic Paradigm and its Impacts on the Concept of the Addressable Audience. Big Data and Television Broadcasting: a Critical Reflection on Big Data's Surge to Become a New Techno-Economic Paradigm and its Impacts on the Concept of the Addressable Audience, 23-38.
Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal on Media Management, 18(1), 9-24.
Nielsen. (2013). NIELSEN LAUNCHES ‘NIELSEN TWITTER TV RATINGS’. Retrieved from www.nielsen.com/us/en/press-releases/2013/nielsen-launches-nielsen-twitter-tv-ratings/
Nielsen. (2020). ABOUT NIELSEN SOCIAL. Retrieved from https://www.nielsensocial.com/about/
Oh, C., Sasser, S., & Almahmoud, S. (2015). Social media analytics framework: the case of Twitter and Super Bowl ads. Journal of Information Technology Management, 26(1), 1-18.
Oh, C., Yergeau, S., Woo, Y., Wurtsmith, B., & Vaughn, S. (2015). Is Twitter psychic? Social media analytics and television ratings. Paper presented at the 2015 Second International Conference on Computing Technology and Information Management (ICCTIM).
Oyeyemi, T. (2020). Minister Appoints Team To Design Framework For Audience Measurement System. Retrieved from www.fmic.gov.ng/minister-appoints-team-to-design-framework-for-audience-measurement-system/
Pensa, R. G., Sapino, M. L., Schifanella, C., & Vignaroli, L. J. I. C. I. M. (2016). Leveraging cross-domain social media analytics to understand TV topics popularity. 11(3), 10-21.
Pynta, P., Seixas, S. A., Nield, G. E., Hier, J., Millward, E., & Silberstein, R. B. J. J. o. A. R. (2014). The Power of Social Television: Can Social Media Build Viewer Engagement?: A New Approach to Brain Imaging of Viewer Immersion. 54(1), 71-80.
Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3(4), 1277-1291.
Zeng, D., Chen, H., Lusch, R., & Li, S.-H. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13-16.
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2021-05-28
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