Social media influence on young people and children: Analysis on Instagram, Twitter and YouTube

Lozano-Blasco, Raquel, Mira-Aladrén, Marta and Gil-Lamata, Mercedes Social media influence on young people and children: Analysis on Instagram, Twitter and YouTube. Comunicar, 2023, vol. 31, n. 74, pp. 125-137. [Journal article (Paginated)]

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English abstract

Social networking sites are a new ecosystem of social relations in which adolescents follow public figures or influencers: instagrammers, tweeters and youtubers. Their behaviour in the posts they publish become a trend and a model for the new generations. In order to explore these behaviours and their consequences, it is useful to study the behaviour of the 10 instagramers, 10 tweeters and 10 youtubers with the largest number of followers in the world. A mixed method was employed, combining: social media analysis (SNA) methodology executed by monitoring Twitter, Instagram and YouTube accounts and their publications (300 posts with the highest number of likes). The FanapageKarma tool was used to capture data by applying data mining techniques. Subsequently, sentiment analysis was performed using Meaning Cloud software, determining sentiment polarity analysis quantitatively. Finally, a semantic analysis of the content was performed using Nvivo. The results of multi-regression and sentiment’s analysis show clear differences between social networking sites. Twitter is a space for critical analysis of information and social movements, especially climate change. In this space adolescents defend their values and ideology. Instagram is a showcase for fashion and beauty, where brands support an idealised and desirable lifestyle. YouTube is a space for entertainment and comedy. It concludes that despite their differences there is one univocal feature, the effort of influencers to capture audiences and establish parasocial relationships.

Spanish abstract

Las redes sociales son un nuevo ecosistema de relaciones sociales en el que los adolescentes siguen a personajes públicos o «influencers»: «instagramers», «twitteros» y «youtubers». Su comportamiento en los posts que publican se convierte en una tendencia y un modelo para las nuevas generaciones. Para profundizar en estos comportamientos y sus consecuencias, resulta de utilidad estudiar el comportamiento de los 10 «instagramers», 10 «twitteros» y 10 «youtubers» con mayor número de seguidores en el mundo mediante sus publicaciones (300 post con mayor cantidad de likes). Se empleó un método mixto, combinando: la metodología de análisis de medios sociales (SNA) ejecutada mediante la monitorización de cuentas de Twitter, Instagram y YouTube. Se empleó el instrumento de FanapageKarma para captar los datos aplicando técnicas de minería de datos. Posteriormente, se realizó un análisis de sentimiento mediante el software «Meaning Cloud», este determinó el análisis de la polaridad de los sentimientos de forma cuantitativa. Finalmente, se realizó un análisis semántico de los contenidos mediante Nvivo. Los resultados de la multirregresión y el análisis de sentimientos muestran claras diferencias entre las redes sociales. Twitter es un espacio de análisis crítico de la información y de los movimientos sociales, especialmente del cambio climático. En este espacio los adolescentes defienden sus valores e ideología. Instagram es un escaparate de moda y belleza, donde las marcas apoyan un estilo de vida idealizado y deseable. YouTube es un espacio para el entretenimiento y la comedia. Se concluye que a pesar de sus diferencias hay una característica unívoca, el esfuerzo de los «influencers» por captar audiencias y establecer relaciones parasociales.

Item type: Journal article (Paginated)
Keywords: Adolescence; youth; polarity; Twitter; YouTube; Instagram; Adolescencia; juventud; polaridad; Twitter; YouTube; Instagram
Subjects: B. Information use and sociology of information > BJ. Communication
G. Industry, profession and education.
G. Industry, profession and education. > GH. Education.
Depositing user: Alex Ruiz
Date deposited: 09 Jan 2023 06:25
Last modified: 09 Jan 2023 06:25
URI: http://hdl.handle.net/10760/43885

References

Anderson, M., & Jiang, J. (2018, May 31). Teens, social media & technology. Pew Research Center. https://pewrsr.ch/3aRyOSL

Aran-Ramspott, S., Fedele, M., & Tarragó, A. (2018). YouTubers' social functions and their influence on pre-adolescence. [Funciones sociales de los Youtubers y su influencia en la preadolescencia]. Comunicar, 57, 71-80. https://doi.org/10.3916/C57-2018-07

Ashman, R., Patterson, A., & Brown, S. (2018). ‘Don’t forget to like, share and subscribe’: Digital autopreneurs in a neoliberal world. Journal of Business Research, 92, 474-483. https://doi.org/10.1016/j.jbusres.2018.07.055

Bakir, A., Gentina, E., & de-Araújo-Gil, L. (2020). What shapes adolescents’ attitudes toward luxury brands? The role of self-worth, self-construal, gender and national culture. Journal of Retailing and Consumer Services, 57, 102208. https://doi.org/10.1016/j.jretconser.2020.102208

Barton, A.H., & Lazarsfeld, P.F. (1955). Some functions of qualitative analysis in social research. Bobbs Merrill.

Bhatia, A. (2018). Interdiscursive performance in digital professions: The case of YouTube tutorials. Journal of Pragmatics, 124, 106-120. https://doi.org/10.1016/j.pragma.2017.11.001

Blasco-García, J. (2020). Nuevas formas de ausencia: Las redes sociales. [Doctoral Dissertation, Universitat Politécnica de Valencia]. https://bit.ly/3JplAJN

Boerman, S.C. (2020). The effects of the standardized Instagram disclosure for micro- and meso-influencers. Computers in Human Behavior, 103, 199-207. https://doi.org/10.1016/j.chb.2019.09.015

Burgess, J., & Green, J., (2009). YouTube: Online video and participatory culture. Cambridge Polity Press. https://bit.ly/3Qfi1rs

Burnette, C.B., Kwitowski, M.A., & Mazzeo, S.E. (2017). “I don’t need people to tell me I’m pretty on social media:” A qualitative study of social media and body image in early adolescent girls. Body Image, 23, 114-125. https://doi.org/10.1016/j.bodyim.2017.09.001

Castillo-Abdul, B., Romero-Rodríguez, L.M., & Larrea-Ayala, A. (2020). Kid influencers in Spain: Understanding the themes they address and preteens’ engagement with their YouTube channels. Heliyon, 6(9). https://doi.org/10.1016/j.heliyon.2020.e05056

Davis, K. (2012). Friendship 2.0: Adolescents’ experiences of belonging and self-disclosure online. Journal of Adolescence, 35(6), 1527-1536. https://doi.org/10.1016/j.adolescence.2012.02.013

De-Bérail, P., Guillon, M., & Bungener, C. (2019). The relations between YouTube addiction, social anxiety and parasocial relationships with Youtubers: A moderated-mediation model based on a cognitive-behavioral framework. Computers in Human Behavior, 99, 190-204. https://doi.org/10.1016/j.chb.2019.05.007

Du?cu, M., & Günneç, D. (2020). Polarity classification of Twitter messages using audio processing. Information Processing & Management, 57(6), 102346. https://doi.org/10.1016/j.ipm.2020.102346

Erz, A., Marder, B., & Osadchaya, E. (2020). Hashtags: Motivational drivers, their use, and differences between influencers and followers. Computers in Human Behavior, 89, 48-60. https://doi.org/10.1016/j.chb.2018.07.030

Ferchaud, A., Grzeslo, J., Orme, S., & LaGroue, J. (2018). Parasocial attributes and YouTube personalities: Exploring content trends across the most subscribed YouTube channels. Computers in Human Behavior, 80, 88-96. https://doi.org/10.1016/j.chb.2017.10.041

Genç, M., & Öksüz, B. (2019). An analysis on collaborations between Turkish beauty YouTubers and cosmetic brands. Procedia Computer Science, 158, 745-750. https://doi.org/10.1016/j.procs.2019.09.110

Harb, J., Ebeling, R., & Becker, K. (2020). A framework to analyze the emotional reactions to mass violent events on Twitter and influential factors. Information Processing & Management, 57(6), 102372. https://doi.org/10.1016/j.ipm.2020.102372

Hartmann, T. (2016). Parasocial interaction, parasocial relationships, and well-being. In L. Reinecke, & M.B. Oliver (Eds.), The Routledge handbook of media use and well-being: International perspectives on theory and research on positive media effects (pp. 131-144). Routledge. https://bit.ly/3zP0GjL

Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04) (pp. 168-177). Association for Computing Machinery. https://doi.org/10.1145/1014052.1014073

Jerslev, A. (2016). Media times. In the time of the microcelebrity: Celebrification and the YouTuber zoella. International Journal of Communication, 10, 5233-5251. https://bit.ly/3ySPG3r

Kale, G., & Jayanth, J. (2019). Introduction to research. In V. Bairagi, & M. Munot (Eds.), Research Methodology. A Practical and Scientific Approach. CRC Press. https://doi.org/10.1201/9781351013277-1

Keegan, B.J., & Rowley, J. (2017). Evaluation and decision making in social media marketing. Management Decision, 55(1), 15-31. https://doi.org/10.1108/MD-10-2015-0450

Kim, D.H., Seely, N.K., & Jung, J.H. (2017). Do you prefer, Pinterest or Instagram? The role of image-sharing SNSs and self-monitoring in enhancing ad effectiveness. Computers in Human Behavior, 70, 535-543. https://doi.org/10.1016/j.chb.2017.01.022

Kim, J., & Kim, Y. (2019). Instagram user characteristics and the color of their photos: Colorfulness, color diversity, and color harmony. Information Processing & Management, 56(4), 1494-1505. https://doi.org/10.1016/j.ipm.2018.10.018

Krippendorff, K. (1980). Content analysis: An introduction to its methodology. Sage. https://bit.ly/3bmaPv0

Lange, P.G. (2014). Commenting on YouTube rants: Perceptions of inappropriateness or civic engagement? Journal of Pragmatics, 73, 53-65. https://doi.org/10.1016/j.pragma.2014.07.004

Latorre-Martínez, P., Orive-Serrano, V., & Íñiguez-Dieste, D. (2018). Measurement and analysis of the presence in Facebook and Twitter in the regional television broadcaster’s context in Spain. Profesional de la Información, 27(5), 1061-1070. https://doi.org/10.3145/epi.2018.sep.10

León, O.G., & Montero, I. (2015). Métodos de investigación en Psicología y Educación. Las tradiciones cuantitativa y cualitativa. McGraw Hill. https://bit.ly/3BCmP6w

Lipsman, A., Mudd, G., Rich, M., & Bruich, S. (2012). The power of “like”: How brands reach (and influence) fans through social-media marketing. Journal of Advertising Research, 52(1), 40-52. https://doi.org/10.2501/JAR-52-1-040-052

Lozano-Blasco, R., Quilez-Robres, A., Delgado-Bujedo, D., & Latorre-Martínez, M.P. (2021). YouTube's growth in use among children 0–5 during COVID19: The Occidental European case. Technology in society, 66, 101648. https://doi.org/10.1016/j.techsoc.2021.101648

McGoogan, C. (2017, August 17). Hashtag turns 10: Seven facts you didn't know about the trending symbol. The Telegraph. https://bit.ly/3coL51b

Mäntymäki, M., & Riemer, K. (2014). Digital natives in social virtual worlds: A multi-method study of gratifications and social influences in Habbo Hotel. International Journal of Information Management, 34(2), 210-220. https://doi.org/10.1016/j.ijinfomgt.2013.12.010

Neu, D., Saxton, G., Rahaman, A., & Everett, J. (2019). Twitter and social accountability: Reactions to the Panama Papers. Critical Perspectives on Accounting, 61, 38-53. https://doi.org/10.1016/j.cpa.2019.04.003

Nguyen, H., & Le-Nguyen, M. (2018). Multilingual opinion mining on YouTube – A convolutional N-gram BiLSTM word embedding. Information Processing & Management, 54(3), 451-462. https://doi.org/10.1016/j.ipm.2018.02.001

Ofcom (Eds.) (2017). Children and parents: Media use and attitudes report. Ofcom. https://bit.ly/3IRiG05

Oramas-Bustillos, R., Zatarain-Cabada, R., Barrón-Estrada, M.L., & Hernández-Pérez, Y. (2019). Opinion mining and emotion recognition in an intelligent learning environment. Computer Applications in Engineering Education, 27(1), 90-101. https://doi.org/10.1002/cae.22059

Peres, R., Talwar, S., Alter, L., Elhanan, M., & Friedmann, Y. (2020). Narrowband influencers and global icons: Universality and media compatibility in the communication patterns of political leaders worldwide. Journal of International Marketing, 28(1), 48-65. https://doi.org/10.1177/1069031X19897893

Reyes-Menéndez, A., Saura, J.R., & Alvarez-Alonso, C. (2018). Understanding #WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach. International Journal of Environmental Research and Public Health, 15(11), 2537. https://doi.org/10.3390/ijerph15112537

Saura, J.R., Debasa, F., & Reyes-Menendez, A. (2019). Does user generated content characterize Millennials’ generation behavior? Discussing the relation between SNS and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 5(4), 1-15. https://doi.org/10.3390/joitmc5040096

Scannell, P. (2000). For-anyone-as-someone structures. Media, Culture & Society, 22(1), 5-24. https://doi.org/10.1177/016344300022001001

Schmuck, D., Karsay, K., Matthes, J., & Stevic, A. (2019). ‘Looking up and feeling down’. The influence of mobile social networking site use on upward social comparison, self-esteem, and well-being of adult smartphone users. Telematics and Informatics, 42, 101240. https://doi.org/10.1016/j.tele.2019.101240

Schouten, A.P., Janssen, L., & Verspaget, M. (2020). Celebrity vs. influencer endorsements in advertising: The role of identification, credibility, and product-endorser fit. International Journal of Advertising, 39(2) 258-281. https://doi.org/10.1080/02650487.2019.1634898

Shane-Simpson, C., Manago, A., Gaggi, N., & Gillespie-Lynch, K. (2018). Why do college students prefer Facebook, Twitter, or Instagram? Site affordances, tensions between privacy and self-expression, and implications for social capital. Computers in Human Behavior, 86, 276-288. https://doi.org/10.1016/j.chb.2018.04.041

Sharma, S.K., & Hoque, X. (2017). Sentiment predictions using support vector machines for odd-even formula in Delhi. International Journal of Intelligent Systems and Applications, 9(7), 61-69. https://doi.org/10.5815/ijisa.2017.07.07

Smith, A., & Anderson, M. (2018, March 1). Social media use in 2018. Pew Research Center. https://pewrsr.ch/3v4hmBn

Song, L., Li, R.Y.M., & Yao, Q. (2022). An informal institution comparative study of occupational safety knowledge sharing via French and English Tweets: Languaculture, weak-strong ties and AI sentiment perspectives. Safety Science, 147, 105602. https://doi.org/10.1016/j.ssci.2021.105602

Stockdale, L.A., & Coyne, S.M. (2020). Bored and online: Reasons for using social media, problematic social networking site use, and behavioral outcomes across the transition from adolescence to emerging adulthood. Journal of Adolescence, 79, 173-183. https://doi.org/10.1016/j.adolescence.2020.01.010

Throuvala, M.A., Griffiths, M.D., Rennoldson, M., & Kuss, D.J. (2019). Motivational processes and dysfunctional mechanisms of social media use among adolescents: A qualitative focus group study. Computers in Human Behavior, 93, 164-175. https://doi.org/10.1016/j.chb.2018.12.012

Van-Reijmersdal, E.A., Rozendaal, E., Hudders, L., Vanwesenbeeck, I., Cauberghe, V., & van-Berlo, Z.M.C. (2020). Effects of disclosing influencer marketing in videos: An eye tracking study among children in early adolescence. Journal of Interactive Marketing, 49(1), 94-106. https://doi.org/10.1016/j.intmar.2019.09.001

Vannucci, A., & McCauley-Ohannessian, C. (2019). Social media use subgroups differentially predict psychosocial well-being during early adolescence. Journal of Youth and Adolescence, 48, 1469-1493. https://doi.org/10.1007/s10964-019-01060-9

Verrastro, V., Fontanesi, L., Liga, F., Cuzzocrea, F., & Gugliandolo, M.C. (2020). Fear the Instagram: Beauty stereotypes, body image and Instagram use in a sample of male and female adolescents. Qwerty, 15(1), 31-49. https://doi.org/10.30557/QW000021

Vizcaíno-Verdú, A., & Aguaded, I. (2020). Análisis de sentimiento en Instagram: Polaridad y subjetividad de cuentas infantiles. ZER, 25(48), 213-229. https://doi.org/10.1387/zer.21454

Weismueller, J., Harrigan, P., Wang, S., & Soutar, G.N. (2020). Influencer endorsements: How advertising disclosure and source credibility affect consumer purchase intention on social media. Australasian Marketing Journal, 28(4), 160-170. https://doi.org/10.1016/j.ausmj.2020.03.002

Xu, Q.A., Chang, V., & Jayne, C. (2022). A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal, 3, 100073. https://doi.org/10.1016/j.dajour.2022.100073

Yau, J.C., & Reich, S.M. (2019). “It’s just a lot of work”: Adolescents’ self-presentation norms and practices on Facebook and Instagram. Journal of Research on Adolescence, 29(1), 196-209. https://doi.org/10.1111/jora.12376

Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919-926. https://doi.org/10.1016/j.dss.2012.12.028


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