The role of strategic online engagement and content curation in professional branding and career advancement on social media platforms

Pavel N. Ustin 1 * , Natalia N. Udina 2, Elena V. Grib 3, Roza L. Budkevich 4, Andrey V. Korzhuev 5, Nikolay N. Kosarenko 6
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1 Kazan Federal University, Kazan, RUSSIA
2 Peoples’ Friendship University of Russia, Moscow, RUSSIA
3 Moscow Aviation Institute (National Research University), Moscow, RUSSIA
4 Almetyevsk State Technological University «Petroleum High School», Almetyevsk, RUSSIA
5 I. M. Sechenov First Moscow State Medical University, Moscow, RUSSIA
6 Plekhanov Russian University of Economics, Moscow, RUSSIA
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 3, Article No: e202442.
OPEN ACCESS   156 Views   81 Downloads   Published online: 04 Jul 2024
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This study investigates role of social media user engagement metrics in predicting career success likelihoods using supervised machine learning techniques. With platforms like LinkedIn and VKontakte becoming pivotal for networking and advancement, user statistics have emerged as potential indicators of professional capability. However, research questions metric reliability considering impression management tactics and biases. While prior studies examined limited activity features, this analysis adopts a robust CatBoost model to gauge career success prediction from multifaceted social data combinations. The study utilizes user profiles of over 17,000 on a major Russian platform. Individuals are categorized by an algorithm accounting for factors like salaries, experience, and employment status. User statistics spanning engagement, content sharing, popularity, and profile completeness provide model inputs. Following comparative evaluation, CatBoost achieved superior performance in classification accuracy, precision, recall and ROC AUC score. Analysis of SHapley Additive exPlanations values provides explanatory modeling insights into influential metrics, thresholds, and patterns. Results reveal subscribers, reposts and interest pages as highly impactful, suggesting that influence and content resonance predict success better than sheer visibility indicators like multimedia volumes. Findings also point to optimal engagement ranges beyond which career prediction gains diminish. Additionally, profile completeness and regular posting are positive to a limit, while likes to have negligible effects. The study contributes more holistic, data-driven visibility into effective social media conduct for career advancement. It advocates prioritizing network cultivation, tactical self-presentation, shareable narratives and reciprocal relationships over metrics gaming. Findings largely validate strategic communication theory around impression management and relationship-building.


Ustin, P. N., Udina, N. N., Grib, E. V., Budkevich, R. L., Korzhuev, A. V., & Kosarenko, N. N. (2024). The role of strategic online engagement and content curation in professional branding and career advancement on social media platforms. Online Journal of Communication and Media Technologies, 14(3), e202442.


  • Amadoru, M., & Gamage, C. (2016). Evaluating effective use of social networks for recruitment. In Proceedings of the 2016 ACM SIGMIS Conference on Computers and People Research (pp. 125-133). ACM.
  • Asad, R., Altaf, S., Ahmad, S., Shah Noor Mohamed, A., Huda, S., & Iqbal, S. (2023). Achieving personalized precision education using the Catboost model during the COVID-19 lockdown period in Pakistan. Sustainability, 15(3), 2714.
  • Baima, G., Forliano, C., Santoro, G., & Vrontis, D. (2020). Intellectual capital and business model: A systematic literature review to explore their linkages. Journal of Intellectual Capital, 22(3), 653-679.
  • Bao, P. (2016). Modeling and predicting popularity dynamics via an influence-based self-excited Hawkes process. In Proceedings of the International Conference on Information and Knowledge Management (pp. 1897-1900).
  • Blaer, M., Frost, W., & Laing, J. (2020). The future of travel writing: Interactivity, personal branding and power. Tourism Management, 77, 104009.
  • Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210-230.
  • Brandtzaeg, P. B., & Chaparro-Domínguez, M. Á. (2020). From youthful experimentation to professional identity: Understanding identity transitions in social media. Young, 28(2), 157-174.
  • Brodovskaya, E. V., Dombrovskaya, A. Y., Pyrma, R. V., Sinyakov, A. V., & Azarov, A. A. (2019). The impact of digital communication on Russian youth professional culture: Results of a comprehensive applied study. Monitoring of Public Opinion: Economic and Social Changes, 1, 228-251.
  • Brown, C., Hooley, T., & Wond, T. (2020). Building career capital: Developing business leaders’ career mobility. Career Development International, 25(5), 445-459.
  • Buettner, R. (2017). Getting a job via career-oriented social networking markets: The weakness of too many ties. Electronic Markets, 27(4), 371-385.
  • Cakit, E., & Dagdeviren, M. (2022). Predicting the percentage of student placement: A comparative study of machine learning algorithms. Education and Information Technologies, 27(1), 997-1022.
  • Carpentier, M., Van Hoye, G., & Weijters, B. (2019). Attracting applicants through the organization’s social media page: Signaling employer brand personality. Journal of Vocational Behavior, 115, 103326.
  • Cerruto, F., Cirillo, S., Desiato, D., Gambardella, S. M., & Polese, G. (2022). Social network data analysis to highlight privacy threats in sharing data. Journal of Big Data, 9, 19.
  • Chen, X., Wei, S., Davison, R. M., & Rice, R. E. (2020). How do enterprise social media affordances affect social network ties and job performance? Information Technology and People, 33(1), 361-388.
  • Donelan, H. (2016). Social media for professional development and networking opportunities in academia. Journal of Further and Higher Education, 40(5), 706-729.
  • Donskoy, A. G., Sakhno, O. A., & Makashova, V. N. (2021). Professional network communities as a resource for informal professional development of teaching staff. Scientific Support of the Personnel Development System, 2(47), 15-30.
  • Duffy, B. E., & Pooley, J. D. (2017). “Facebook for academics”: The convergence of self-branding and social media logic on Social Media and Society, 3(1).
  • El Ouirdi, M., Segers, J., El Ouirdi, A., & Pais, I. (2015). Predictors of job seekers’ self-disclosure on social media. Computers in Human Behavior, 53, 1-12.
  • Enughwure, A. A., & Ogbise, M. E. (2020). Application of machine learning methods to predict student performance: A systematic literature review. International Research Journal of Engineering and Technology, 7(5), 3405-3415.
  • Jacobson, J. (2020). You are a brand: Social media managers’ personal branding and “the future audience.” Journal of Product and Brand Management, 29(6), 715-727.
  • Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59-68.
  • Khamis, S., Ang, L., & Welling, R. (2017). Self-branding, ‘micro-celebrity’ and the rise of social media influencers. Celebrity Studies, 8(2), 191-208.
  • Koch, T., Gerber, C., & De Klerk, J. J. (2018). The impact of social media on recruitment: Are you LinkedIn? SA Journal of Human Resource Management, 16, a861.
  • Koltsova, O., Koltсov, S., & Sinyavskaya, Y. (2017). When internet really connects across space: Communities of software developers in Vkontakte social networking site. In G. Ciampaglia, A. Mashhadi, & T. Yasseri (Eds.), Social informatics (pp. 431-442).
  • Kostin, D. V., & Shelukhin, O. I. (2016). Comparative analysis of machine learning algorithms for classification of encrypted network traffic. T-Comm-Telecommunications and Transport, 10(9), 43-52.
  • Kucharska, W. (2021). Leadership, culture, intellectual capital and knowledge processes for organizational innovativeness across industries: The case of Poland. Journal of Intellectual Capital, 22(7), 121-141.
  • Kuiper, K. (2021). Communication theory of identity: A fifth frame. Annals of the International Communication Association, 45(3), 175-187.
  • Labrecque, L. I., Markos, E., & Milne, G. R. (2011). Online personal branding: Processes, challenges, and implications. Journal of Interactive Marketing, 25(1), 37-50.
  • Lardo, A., Dumay, J., Trequattrini, R., & Russo, G. (2017). Social media networks as drivers for intellectual capital disclosure: Evidence from professional football clubs. Journal of Intellectual Capital, 18(1), 63-80.
  • Leite, F. P., & de Baptista, P. P. (2022). The effects of social media influencers’ self-disclosure on behavioral intentions: The role of source credibility, parasocial relationships, and brand trust. Journal of Marketing Theory and Practice, 30(3), 295-311.
  • Lin, J., Luo, Z., Cheng, X., & Li, L. (2019). Understanding the interplay of social commerce affordances and swift guanxi: An empirical study. Information and Management, 56(2), 213-224.
  • Luwie, L., & Pasaribu, L. H. (2021). The influence of personal branding in the establishment of social media influencer credibility and the effect on brand awareness and purchase intention. Enrichment: Journal of Management, 12(1), 917-925.
  • Macy, M. W. (2023). The antecedents and consequences of network mobility. PNAS, 120(28), e2306897120.
  • Mahmutova, D., & Gerasimova, D. (2023). Digital illustration marketing via blogs in the VKontakte social network. Virtual Communication and Social Networks, 2023(3), 160-166.
  • Marwick, A. E., & Boyd, D. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media and Society, 13(1), 114-133.
  • Merunková, L., & Šlerka, J. (2019). Goffman´s theory as a framework for analysis of self-presentation on online social networks. Masaryk University Journal of Law and Technology, 13(2), 243-276.
  • Mogaji, E. (2019). Student engagement with LinkedIn to enhance employability. In A. Diver(Ed.), Employability via higher education: Sustainability as scholarship (pp. 321-329). Springer.
  • Nikitkov, A., & Sainty, B. (2014). The role of social media in influencing career success. International Journal of Accounting and Information Management, 22(4), 273-294.
  • Orishev, A. B., Mamedov, A. A., Kotusov, D. V., Grigoriev, S. L., & Makarova, E. V. (2020). Digital education: VKontakte social network as a means of organizing the educational process. Journal of Physics: Conference Series, 1691, 012092.
  • Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48(3), 128-138.
  • Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590-595.
  • Popov, E. V., Simonova, V. L., & Komarova, O. V. (2021). The regional and substantial differentiation of social media. Applied Economics Letters, 28(16), 1386-1390.
  • Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. In Proceedings of the 32nd Conference on Neural Information Processing Systems (pp. 1-11).
  • Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon and Dchuster.
  • Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). On developing generic models for predicting student outcomes in educational data mining. Big Data and Cognitive Computing, 6(1), 6.
  • Ruparel, N., Dhir, A., Tandon, A., Kaur, P., & Islam, J. U. (2020). The influence of online professional social media in human resource management: A systematic literature review. Technology in Society, 63, 101335.
  • Sheer, V. C., & Rice, R. E. (2017). Mobile instant messaging use and social capital: Direct and indirect associations with employee outcomes. Information and Management, 54(1), 90-102.
  • Shetty, S. H., Shetty, S., Singh, C., & Rao, A. (2022). Supervised machine learning: Algorithms and applications. In P. Singh (Ed.), Fundamentals and methods of machine and deep learning (pp. 1-16). Wiley.
  • Singh, A. P. (2023). A study on impact of social media on recruitment process. International Journal of Scientific Research in Engineering and Management.
  • Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers and Education, 143, 103676.
  • Tsaturyan, M. M., & Matevosyan, A. P. (2022). Features of the functioning of the virtual communication language in the social network “VKontakte.” Bulletin of the Adyghe State University, Series “Philology and Art History,” 2(297), 107-115.
  • Umair, A., Masciari, E., & Ullah, M. H. (2023). Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches. Journal of Supercomputing, 79(15), 17355-17385.
  • Van Den Beemt, A., Thurlings, M., & Willems, M. (2020). Towards an understanding of social media use in the classroom: A literature review. Technology, Pedagogy and Education, 29(1), 35-55.
  • van Dijck, J. (2013). “You have one identity”: Performing the self on Facebook and LinkedIn. Media, Culture and Society, 35(2), 199-215.
  • Van Osch, W., & Steinfield, C. W. (2018). Strategic visibility in enterprise social media: Implications for network formation and boundary spanning. Journal of Management Information Systems, 35(2), 647-682.
  • Van Zoonen, W., Verhoeven, J. W. M., & Vliegenthart, R. (2016). How employees use Twitter to talk about work: A typology of work-related tweets. Computers in Human Behavior, 55, 329-339.
  • Vartanov, S., & Khvorostyanaya, A. (2023). Personal brand strategizing in digital mediatization: Game-theoretic and behavioral approaches. Strategizing: Theory and Practice, 3(2), 218-233.
  • Wang, X., Zhang, R., Wang, X., Xu, D., & Tian, F. (2022). How do mobile social apps matter for college students’ satisfaction in group-based learning? The mediation of collaborative learning. Frontiers in Psychology, 13.
  • Xie, X., & Liu, L. (2022). Exploring the antecedents of trust in electronic word-of-mouth platform: The perspective on gratification and positive emotion. Frontiers in Psychology, 13.
  • Xu, Z., & Qian, M. (2023). Predicting popularity of viral content in social media through a temporal-spatial cascade convolutional learning framework. Mathematics, 11(14), 3059.