Building the dream online: Does participation in luxury brand's social media affect brand experience, brand affect, brand trust, and brand loyalty?

The global market for luxury goods has witnessed a phenomenal growth over the past decades. Along with the increasing demand that stems from increased purchasing power, emerging markets, and new wider consumer groups, traditional luxury brands have faced a fierce competition caused by new forms of luxury such as masstige and luxurious fashion. Likewise, the rapid growth of social networks and social media has fundamentally transformed the business environment, and the whole society. Digital networks have facilitated companies and consumers to build online consumption communities, which supports the recent shift of marketing focus on relationships and co-creation of value. Consequently, luxury brands have started to use social media for advertising and relationship marketing. Due to the dynamic and interactive digital environment the importance of brand stories has become even more apparent. While brand communities and online communities are widely studied, luxury brands and social media based brand communities (SMBBCs) have not received yet much academic attention. This study takes the approach of SMBBCs to investigate the influence of consumers' participation in luxury brand's social media on brand experience, and on key dependent variables in consumer behavior research: brand affect, brand trust, and brand loyalty. The purpose of this study is to examine the effectiveness of social media, and to contribute to the research on social media brand communities and brand-consumer relationships, as well as luxury brands. The study proposes a theoretical framework that combines two empirically developed constructs: brand experience, and brand affect/trust-brand loyalty constructs, and tests the model within a social media based luxury brand community context. The data were collected as an online survey from various social media, which resulted in 333 valid responses from consumers who follow a luxury brand's social media. The study is quantitative by nature, and uses structural equation modeling (SEM) as the main method of analysis. To further examine the influence of participation on the focal construct, brand experience, analysis of variance (ANOVA) was also conducted. The results support the reasoning that participation in luxury brand's social media affect consumer behavior. Social media following influences brand experience that accumulates in the long run, but participation affects also rapidly consumers new to the brand. Further, active participation and passive participation appear to have equal influence on brand experience. The findings reveal the chain of effects from brand related stimuli to brand affect, brand trust, and brand loyalty, and confirm the importance of affect in building brand loyalty

Finding influential users of web event in social media

Users of social media have different influences on the evolution of a Web event. Finding influential users could benefit such information services as recommendation and market analysis. However, most of the existing methods are only based on social networks of users or user behaviors while the role of the contents contributed by users in social media is ignored. In fact, a Web event evolves with both user behaviors and the contents. This paper proposes an approach to find influential users by extracting user behavior network and association network of words within the contents and then uses PageRank algorithm and HITS algorithm to calculate the influence of users on the integration of two networks. The proposed approach is effective on several real-world datasets

The Impact of Formal Hierarchies on Enterprise Social Networking Behavior

With more and more companies using enterprise social networks (ESN) for employee communication and collaboration, the influence of ESN on organizational hierarchies has been subject of countless discussions in practice-oriented media and first academic studies. Conversely, the question whether and how formal organizational hierarchies influence ESN usage behavior has not yet been addressed. Drawing on a rich data set comprising 2.5 years of relationship building via direct messages, confirmed contact requests, and group messages, we are able to show that formal hierarchies have an important impact on social networking behavior. By applying means of social network analysis and supported by statements from interviews, we illustrate how deeply formal hierarchy impacts the three examined types of relationships. Our results motivate academics to further study the interrelation between hierarchy und ESN and hierarchy’s effects regarding the sociotechnical design and implementation of related systems

A Comprehensive Review of Impacts of Social Media and Information Technology on Decision Making of General People

Social media platforms and information technology have revolutionized the way individuals obtain, use, and share information in the digital age. This has affected people's decision-making processes in a variety of fields. This thorough analysis investigates the complex effects of information technology and social media on the way the public makes decisions. The impact of social media and information technology on decision-making in domains like politics, healthcare, education, consumer behavior, and interpersonal relationships is examined in this review, which draws on an extensive array of scholarly literature, empirical investigations, and theoretical frameworks. According to the research, information technology and social media can influence decisions in both favorable and unfavorable ways. On the one hand, these platforms give users access to a multitude of information, empowering them to take part in social movements, activism, and civic engagement with greater knowledge and understanding. Social media platforms also enable peer-to-peer communication, cooperation, and knowledge sharing, giving people the confidence to ask their social networks for guidance, affirmation, and support. In the digital age, the review emphasizes how psychological variables, cognitive biases, and sociocultural influences shape decision-making processes. In online environments, people's information processing, judgment-making, and decision-making processes are greatly influenced by social comparison, confirmation bias, FOMO, and social validation, among other factors. This thorough analysis advances our knowledge of the intricate interactions among decision-making, information technology, and social media. Informed decision making, digital literacy, and the moral application of social media and information technology in society are all goals of this review, which synthesizes the literature and highlights new trends to guide future research, legislative efforts, and hands-on interventions.&nbsp

Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework

[EN] The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system that automates the process of gathering data from users activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in online events based on network theory metrics. We evaluated its functionality analyzing users activity in events on Twitter.This work is partially supported by the PROME-TEOII/2013/019, TIN2014-55206-R, TIN2015-65515-C4-1-R, H2020-ICT-2015-688095.Del Val Noguera, E.; Martínez, C.; Botti, V. (2016). Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework. Soft Computing. 20(11):4331-4345. https://doi.org/10.1007/s00500-016-2301-0S433143452011Ahn Y-Y, Han S, Kwak H, Moon S, Jeong H (2007) Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th WWW, pp 835–844Bastiaensens S, Vandebosch H, Poels K, Cleemput KV, DeSmet A, Bourdeaudhuij ID (2014) Cyberbullying on social network sites. an experimental study into behavioural intentions to help the victim or reinforce the bully. Comput Hum Behav 31:259–271Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference. ACM, pp 49–62Borge-Holthoefer J, Rivero A, García I, Cauhé E, Ferrer A, Ferrer D, Francos D, Iñiguez D, Pérez MP, Ruiz G et al (2011) Structural and dynamical patterns on online social networks: the Spanish may 15th movement as a case study. PLoS One 6(8):e23883Borondo J, Morales AJ, Losada JC, Benito RM (2013) Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish presidential election as a case studyCatanese SA, De Meo P, Ferrara E, Fiumara G, Provetti A (2011) Crawling facebook for social network analysis purposes. In: Proceedings of the international conference on web intelligence, mining and semantics. ACM, p 52Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th international conference on World Wide Web. ACM, pp 721–730del Val E, Martínez C, Botti V (2015a) A multi-agent framework for the analysis of users behavior over time in on-line social networks. In: 10th International conference on soft computing models in industrial and environmental applications. Springer, Berlin, pp 191–201del Val E, Rebollo M, Botti V (2015b) Does the type of event influence how user interactions evolve on twitter? PLOS One 10(5):e0124049Eurostat (2016a) Internet use statistics—individuals. http://ec.europa.eu/eurostat/statistics-explained/index.php/Internet_use_statistics_-_individuals . Accessed 29 April 2016Eurostat (2016b) Social media—statistics on the use by enterprises. http://ec.europa.eu/eurostat/statistics-explained/index.php/Social_media_-_statistics_on_the_use_by_enterprises#Further_Eurostat_information . Accessed 29 April 2016García Fornes AM, Rodrigo Solaz M, Terrasa Barrena AM, Inglada J, Javier V, Jorge Cano J, Mulet Mengual L, Palomares Chust A, Búrdalo Rapa LA, Giret Boggino AS et al (2015) Magentix 2 user’s manualGolbeck J, Robles C, Turner K (2011) Predicting personality with social media. In: CHI’11, pp 253–262Guimerà R, Llorente A, Moro E, Sales-Pardo M (2012) Predicting human preferences using the block structure of complex social networks. PloS One 7(9):e44620Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. arXiv preprint arXiv:0812.1045Jamali M, Abolhassani H (2006) Different aspects of social network analysis. In: 2006 IEEE/WIC/ACM international conference on web intelligence (WI 2006 main conference proceedings)(WI’06). IEEE, pp 66–72Jiang Y, Jiang J (2014) Understanding social networks from a multiagent perspective. Parallel Distrib Syst IEEE Trans 25(10):2743–2759Kossinets G, Watts D (2006) Empirical analysis of an evolving social network. Science 311(5757):88–90Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Yu PS, Han J, Faloutsos C (eds) Link mining: models, algorithms, and applications. Springer, New York, pp 337–357Lazer D (2009) Life in the network: the coming age of computational social science. Science 323(5915):721–723Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 1(1):5Licoppe C, Smoreda Z (2005) Are social networks technologically embedded? How networks are changing today with changes in communication technology. Soc Netw 27(4):317–335Lotan G, Graeff E, Ananny M, Gaffney D, Pearce I, Boyd D (2011) The revolutions were tweeted: information flows during the 2011 tunisian and egyptian revolutions. Int J Commun 5:1375–1405Peña-López I, Congosto M, Aragón P (2013) Spanish indignados and the evolution of 15M: towards networked para-institutions. Big data: challenges and opportunities, pp 25–26Perliger A, Pedahzur A (2011) Social network analysis in the study of terrorism and political violence. PS Polit Sci Polit 44:45–50Romero DM, Galuba W, Asur S, Huberman BA (2011a) Influence and passivity in social media. In: Proceedings of the 20th WWW, pp 113–114Romero DM, Meeder B, Kleinberg J (2011b) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In: Proceedings of the 20th WWW, pp 695–704Stockman FN, Doreian P, (1997) Evolution of social networks: processes and principles. In: Doreian P, Stokman FN (eds) Evolution of social networks. Routledge, London, pp 233–250Traud AL, Mucha PJ, Porter MA (2012) Social structure of facebook networks. Phys A Stat Mech Its Appl 391(16):4165–4180Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the Facebook social graph. arXiv preprint arXiv:1111.4503Valero S, del Val E, Alemany J, Botti V (2015) Using magentix2 in smart-home environments. In: 10th International conference on soft computing models in industrial and environmental applications. Springer, Berlin, pp 27–37Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, CambridgeWersm (2015) How much data is generated every minute on social media? http://wersm.com/how-much-data-is-generated-every-minute-on-social-media/ . Accessed 29 April 201

Identifying Opinion Leaders on Twitter during Sporting Events: Lessons from a Case Study

[EN] Social media platforms have had a significant impact on the public image of sports in recent years. Through the relational dynamics of the communication on these networks, many users have emerged whose opinions can exert a great deal of influence on public conversation online. This research aims to identify the influential Twitter users during the 2016 UCI Track Cycling World Championships using different variables which, in turn, represent different dimensions of influence (popularity, activity and authority). Mathematical variables of the social network analysis and variables provided by Twitter and Google are compared. First, we calculated the Spearman¿s rank correlation coefficient among all users (n = 20,175) in pairwise comparisons. Next, we performed a qualitative analysis of the top 25 influential users ranked by each variable. As a result, no single variable assessed is sufficient to identify the different kinds of influential Twitter users. The reason that some variables vary so greatly is that the components of influence are very different. Influence is a contextualised phenomenon. Having a certain type of account is not enough to make a user an influencer if they do not engage (actively or passively) in the conversation. Choosing the influencers will depend on the objectives pursued.Lamirán-Palomares, JM.; Baviera, T.; Baviera-Puig, A. (2019). Identifying Opinion Leaders on Twitter during Sporting Events: Lessons from a Case Study. Social Sciences. 8(5):1-18. https://doi.org/10.3390/socsci8050141S11885Abeza, G., Pegoraro, A., Naraine, M. L., Séguin, B., O’, N., & Reilly, N. A. (2014). Activating a global sport sponsorship with social media: an analysis of TOP sponsors, Twitter, and the 2014 Olympic Games. International Journal of Sport Management and Marketing, 15(3/4), 184. doi:10.1504/ijsmm.2014.072010Agre, P. E. (2002). Real-Time Politics: The Internet and the Political Process. The Information Society, 18(5), 311-331. doi:10.1080/01972240290075174Anagnostopoulos, C., Parganas, P., Chadwick, S., & Fenton, A. (2018). Branding in pictures: using Instagram as a brand management tool in professional team sport organisations. European Sport Management Quarterly, 18(4), 413-438. doi:10.1080/16184742.2017.1410202Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Barnes, J. A., & Harary, F. (1983). Graph theory in network analysis. Social Networks, 5(2), 235-244. doi:10.1016/0378-8733(83)90026-6Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113-120. doi:10.1080/0022250x.1972.9989806Borgatti, S. P., & Everett, M. G. (2006). A Graph-theoretic perspective on centrality. Social Networks, 28(4), 466-484. doi:10.1016/j.socnet.2005.11.005Bouguessa, M., & Romdhane, L. B. (2015). Identifying Authorities in Online Communities. ACM Transactions on Intelligent Systems and Technology, 6(3), 1-23. doi:10.1145/2700481BROSIUS, H.-B., & WEIMANN, G. (1996). Who Sets the Agenda. Communication Research, 23(5), 561-580. doi:10.1177/009365096023005002Carter, D. (2016). Hustle and Brand: The Sociotechnical Shaping of Influence. Social Media + Society, 2(3), 205630511666630. doi:10.1177/2056305116666305Chew, S., Metheney, E., & Teague, T. (2017). Modelling and Simulation of the Formation of Social Networks. Social Sciences, 6(3), 79. doi:10.3390/socsci6030079Clavio, G., Burch, L. M., & Frederick, E. L. (2012). Networked Fandom: Applying Systems Theory to Sport Twitter Analysis. International Journal of Sport Communication, 5(4), 522-538. doi:10.1123/ijsc.5.4.522Cleland, J. (2013). Racism, Football Fans, and Online Message Boards. Journal of Sport and Social Issues, 38(5), 415-431. doi:10.1177/0193723513499922Dahlgren, P. (2005). The Internet, Public Spheres, and Political Communication: Dispersion and Deliberation. Political Communication, 22(2), 147-162. doi:10.1080/10584600590933160Dart, J. (2012). New Media, Professional Sport and Political Economy. Journal of Sport and Social Issues, 38(6), 528-547. doi:10.1177/0193723512467356Campo-Ávila, J. del, Moreno-Vergara, N., & Trella-López, M. (2013). Bridging the Gap Between the Least and the Most Influential Twitter Users. Procedia Computer Science, 19, 437-444. doi:10.1016/j.procs.2013.06.059Delia, E. B., & Armstrong, C. G. (2015). #Sponsoring the #FrenchOpen: An Examination of Social Media Buzz and Sentiment. Journal of Sport Management, 29(2), 184-199. doi:10.1123/jsm.2013-0257Demir, R., & Söderman, S. (2015). Strategic sponsoring in professional sport: a review and conceptualization. European Sport Management Quarterly, 15(3), 271-300. doi:10.1080/16184742.2015.1042000Dubois, E., & Gaffney, D. (2014). The Multiple Facets of Influence. American Behavioral Scientist, 58(10), 1260-1277. doi:10.1177/0002764214527088Filo, K., Lock, D., & Karg, A. (2015). Sport and social media research: A review. Sport Management Review, 18(2), 166-181. doi:10.1016/j.smr.2014.11.001Freberg, K., Graham, K., McGaughey, K., & Freberg, L. A. (2011). Who are the social media influencers? A study of public perceptions of personality. Public Relations Review, 37(1), 90-92. doi:10.1016/j.pubrev.2010.11.001Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. doi:10.1016/0378-8733(78)90021-7Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141-154. doi:10.1016/0378-8733(91)90017-nGayo-Avello, D. (2013). Nepotistic relationships in Twitter and their impact on rank prestige algorithms. Information Processing & Management, 49(6), 1250-1280. doi:10.1016/j.ipm.2013.06.003Gibbs, C., O’Reilly, N., & Brunette, M. (2014). Professional Team Sport and Twitter: Gratifications Sought and Obtained by Followers. International Journal of Sport Communication, 7(2), 188-213. doi:10.1123/ijsc.2014-0005Hambrick, M. E. (2012). Six Degrees of Information: Using Social Network Analysis to Explore the Spread of Information Within Sport Social Networks. International Journal of Sport Communication, 5(1), 16-34. doi:10.1123/ijsc.5.1.16Hambrick, M. E., & Mahoney, T. Q. (2011). «It»s incredible trust me’: exploring the role of celebrity athletes as marketers in online social networks. International Journal of Sport Management and Marketing, 10(3/4), 161. doi:10.1504/ijsmm.2011.044794Hambrick, M. E., & Pegoraro, A. (2014). Social Sochi: using social network analysis to investigate electronic word-of-mouth transmitted through social media communities. International Journal of Sport Management and Marketing, 15(3/4), 120. doi:10.1504/ijsmm.2014.072005Hambrick, M. E., & Sanderson, J. (2013). Gaining Primacy in the Digital Network: Using Social Network Analysis to Examine Sports Journalists’ Coverage of the Penn State Football Scandal via Twitter. Journal of Sports Media, 8(1), 1-18. doi:10.1353/jsm.2013.0003Hambrick, M. E., Simmons, J. M., Greenhalgh, G. P., & Greenwell, T. C. (2010). Understanding Professional Athletes’ Use of Twitter: A Content Analysis of Athlete Tweets. International Journal of Sport Communication, 3(4), 454-471. doi:10.1123/ijsc.3.4.454Hofer, M., & Aubert, V. (2013). Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees. Computers in Human Behavior, 29(6), 2134-2142. doi:10.1016/j.chb.2013.04.038Hull, K., & Schmittel, A. (2014). A Fumbled Opportunity? A Case Study of Twitter’s Role in Concussion Awareness Opportunities During the Super Bowl. Journal of Sport and Social Issues, 39(1), 78-94. doi:10.1177/0193723514558928Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59-68. doi:10.1016/j.bushor.2009.09.003Kassing, J. W., & Sanderson, J. (2010). Fan–Athlete Interaction and Twitter Tweeting Through the Giro: A Case Study. International Journal of Sport Communication, 3(1), 113-128. doi:10.1123/ijsc.3.1.113Katz, E. (1957). The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis. Public Opinion Quarterly, 21(1, Anniversary Issue Devoted to Twenty Years of Public Opinion Research), 61. doi:10.1086/266687Khan, H. U., Daud, A., Ishfaq, U., Amjad, T., Aljohani, N., Abbasi, R. A., & Alowibdi, J. S. (2017). Modelling to identify influential bloggers in the blogosphere: A survey. Computers in Human Behavior, 68, 64-82. doi:10.1016/j.chb.2016.11.012Koenig-Lewis, N., Asaad, Y., & Palmer, A. (2017). Sports events and interaction among spectators: examining antecedents of spectators’ value creation. European Sport Management Quarterly, 18(2), 193-215. doi:10.1080/16184742.2017.1361459Kolyperas, D., Maglaras, G., & Sparks, L. (2018). Sport fans’ roles in value co-creation. European Sport Management Quarterly, 19(2), 201-220. doi:10.1080/16184742.2018.1505925Kunkel, T., Walker, M., & Hodge, C. M. (2018). The influence of advertising appeals on consumer perceptions of athlete endorser brand image. European Sport Management Quarterly, 19(3), 373-395. doi:10.1080/16184742.2018.1530688Lahuerta-Otero, E., & Cordero-Gutiérrez, R. (2016). Looking for the perfect tweet. The use of data mining techniques to find influencers on twitter. Computers in Human Behavior, 64, 575-583. doi:10.1016/j.chb.2016.07.035Lewin, K. (1939). Field Theory and Experiment in Social Psychology: Concepts and Methods. American Journal of Sociology, 44(6), 868-896. doi:10.1086/218177McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415-444. doi:10.1146/annurev.soc.27.1.415Meenaghan, T., McLoughlin, D., & McCormack, A. (2013). New Challenges in Sponsorship Evaluation Actors, New Media, and the Context of Praxis. Psychology & Marketing, 30(5), 444-460. doi:10.1002/mar.20618Misener, L., & Mason, D. S. (2006). Creating community networks: Can sporting events offer meaningful sources of social capital? Managing Leisure, 11(1), 39-56. doi:10.1080/13606710500445676Morone, F., & Makse, H. A. (2015). Influence maximization in complex networks through optimal percolation. Nature, 524(7563), 65-68. doi:10.1038/nature14604Naraine, M. L., & Parent, M. M. (2016). Illuminating Centralized Users in the Social Media Ego Network of Two National Sport Organizations. Journal of Sport Management, 30(6), 689-701. doi:10.1123/jsm.2016-0067Naraine, M. L., Schenk, J., & Parent, M. M. (2016). Coordination in International and Domestic Sports Events: Examining Stakeholder Network Governance. Journal of Sport Management, 30(5), 521-537. doi:10.1123/jsm.2015-0273Norris, P., & Curtice, J. (2008). Getting the Message Out: A Two-Step Model of the Role of the Internet in Campaign Communication Flows During the 2005 British General Election. Journal of Information Technology & Politics, 4(4), 3-13. doi:10.1080/19331680801975359Pegoraro, A. (2010). Look Who’s Talking—Athletes on Twitter: A Case Study. International Journal of Sport Communication, 3(4), 501-514. doi:10.1123/ijsc.3.4.501Perić, M. (2018). Estimating the Perceived Socio-Economic Impacts of Hosting Large-Scale Sport Tourism Events. Social Sciences, 7(10), 176. doi:10.3390/socsci7100176Quatman, C., & Chelladurai, P. (2008). Social Network Theory and Analysis: A Complementary Lens for Inquiry. Journal of Sport Management, 22(3), 338-360. doi:10.1123/jsm.22.3.338Quatman, C., & Chelladurai, P. (2008). The Social Construction of Knowledge in the Field of Sport Management: A Social Network Perspective. Journal of Sport Management, 22(6), 651-676. doi:10.1123/jsm.22.6.651Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5), 949-975. doi:10.1016/j.ipm.2016.04.003Santomier, J. (2008). New media, branding and global sports sponsorship. International Journal of Sports Marketing and Sponsorship, 10(1), 9-22. doi:10.1108/ijsms-10-01-2008-b005Small, T. A. (2011). WHAT THE HASHTAG? Information, Communication & Society, 14(6), 872-895. doi:10.1080/1369118x.2011.554572Towner, T., & Lego Munoz, C. (2016). Boomers versus Millennials: Online Media Influence on Media Performance and Candidate Evaluations. Social Sciences, 5(4), 56. doi:10.3390/socsci5040056Veglis, A., & Maniou, T. A. (2018). The Mediated Data Model of Communication Flow: Big Data and Data Journalism. KOME, 6(2), 32-43. doi:10.17646/kome.2018.23Wäsche, H. (2015). Interorganizational cooperation in sport tourism: A social network analysis. Sport Management Review, 18(4), 542-554. doi:10.1016/j.smr.2015.01.003Wäsche, H., Dickson, G., Woll, A., & Brandes, U. (2017). Social network analysis in sport research: an emerging paradigm. European Journal for Sport and Society, 14(2), 138-165. doi:10.1080/16138171.2017.1318198Yan, G., Pegoraro, A., & Watanabe, N. M. (2018). Student-Athletes’ Organization of Activism at the University of Missouri: Resource Mobilization on Twitter. Journal of Sport Management, 32(1), 24-37. doi:10.1123/jsm.2017-0031Yan, G., Watanabe, N. M., Shapiro, S. L., Naraine, M. L., & Hull, K. (2018). Unfolding the Twitter scene of the 2017 UEFA Champions League Final: social media networks and power dynamics. European Sport Management Quarterly, 19(4), 419-436. doi:10.1080/16184742.2018.1517272Yu, Y., & Wang, X. (2015). World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets. Computers in Human Behavior, 48, 392-400. doi:10.1016/j.chb.2015.01.07

Communications as an ESG factor of sustainable development: analysis of the media discourse of social networks

The problematization of the study is based on the identification and analysis of social factors of sustainable development. In the ESG system, communication factors help shape the public space. The problems of ESG principles and factors are increasingly reflected in today's media news, which is manifested in the growth of publicity and replication of this topic in social media.. The article examines the influence of new media on public relations and relations, on the processes of socialization of new generations entering life. The modern media industry is transforming business models and management decisions driven by trends for sustainable development, significant changes are taking place in socio-cultural life, in the field of media and communications. Social media plays an important role in ensuring sustainable development. Communications that are initiated in the information space make it possible to convey the meanings of the values of sustainable development to audiences using various tools and media channels. The accounts of various social networks generate and retransmit content that can contribute to the promotion of the principles of sustainable development. The analysis of the media discourse of social networks made it possible to identify the pain points of this process. The research is aimed at identifying the specifics of public relations, the formation of a culture of media behavior in social networks of representatives of different generations and the importance of communication in order to implement the concept of sustainable development

Media dependence as a factor of socialization of adolescents

В роботі проведено аналіз процесу соціалізації підлітків під впливом медіапростору. З’ясовано вплив соціальних мереж на формування внутрішнього «Я» особистості, її цінностей та поведінку. Обґрунтовано фактори соціалізації та описано механізми, що впливають на процес соціалізації. Проаналізовано переваги соціальних мереж та ефективність впливу медіаосвіти.The analysis of the process of socialization of adolescents under the influence of media space is carried out in the thezes. The influence of social networks on the formation of the inner "I" of the individual, his values and behavior is clarified. The factors of socialization are substantiated and the mechanisms influencing the process of socialization are described. The advantages of social networks and the effectiveness of the impact of media education are analyzed