Redes sociales y su influencia en los jóvenes y niñosAnálisis en Instagram, Twitter y YouTube

  1. Raquel Lozano-Blasco 1
  2. Marta Mira-Aladrén 1
  3. Mercedes Gil-Lamata 1
  1. 1 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

Revue:
Comunicar: Revista Científica de Comunicación y Educación

ISSN: 1134-3478

Année de publication: 2023

Titre de la publication: Educación para la ciudadanía digital: Algoritmos, automatización y comunicación

Número: 74

Pages: 125-137

Type: Article

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DOI: 10.3916/C74-2023-10 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

D'autres publications dans: Comunicar: Revista Científica de Comunicación y Educación

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Résumé

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.

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Références bibliographiques

  • Anderson, M., & Jiang, J. (2018). 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. 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. https://doi.org/10.1016/j.pragma.2017.11.001
  • Bhatia, A. (2018). Interdiscursive performance in digital professions: The case of YouTube tutorials. Journal of Pragmatics, 124, 106-120.
  • 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), 102372-102372. https://doi.org/10.1016/j.ipm.2020.102372
  • Hartmann, T. (2016). Parasocial interaction, parasocial relationships, and well-being. In L. Reinecke, & M. 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 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.2017.01.02
  • 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, 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. https://doi.org/10.1016/j.techsoc.2021.101648
  • 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
  • Mcgoogan, C. (2017). Hashtag turns 10: Seven facts you didn’t know about the trending symbol. The Telegraph. https://bit.ly/3coL51b
  • 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). 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. 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). 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. 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, 919-926. https://doi.org/10.1016/j.dss.2012.12.028
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