Information Support for Personalities Socialization Processes Based on Common Interests

: pp. 56 - 86
Lviv Polytechnic National University, Ukraine
Lviv Polytechnic National University, Information Systems and Networks Department; Osnabrück University, Institute of Computer Science

The main objective of this article is to create an information system project for socialization by personal interests on the basis of SEO-technologies and methods of machine learning. The main purpose of this information system is to identify the user within the system using neural networks and to select similar users by analysing the user's current information. An information system was created that, through Identity and JWT tokens, provides optimized and secure authorization, logging, and support functions for the current system user session. Finding a face in a user's photo and checking the presence of a similar user in the database are implemented using convolutional and Siamese neural networks. The analysis and formation of similar user beeps were implemented using fuzzy search algorithms, the Levenshtein algorithm and the Noisy Channel model, which made it possible to maximize the automation of the user selection process and to optimize the time spent in this process. Tools have also been created to view other users’ profiles, preferences and private correspondence. All private correspondence and information about it are stored in the current database. Each user of the system can view all information about sent and received messages. The created information system implements the process of user identification, analysis, selection and further socialization of system users.

  1.  Chu S. C. Using a consumer socialization framework to understand electronic word-of-mouth (eWOM) group membership among brand followers on Twitter. Electronic Commerce Research and Applications. 2016. № 14 (4). P. 251-260.
  2. Batiuk T., Vysotska V., Lytvyn V. Intelligent System for Socialization by Personal Interests on the Basis of SEO-Technologies and Methods of Machine Learning. Computational Linguistics and Intelligent Systems (COLINS 2020) : 4th International Conference, Lviv, 23-24 April 2020 : CEUR workshop proceedings. 2020. № 2604. P. 1237-1250.
  3. Vysotska V. Information Technology for Internet Resources Promotion in Search Systems Based on Content Analysis of Web-Page Keywords. Radio Electronics, Computer Science, Control. 2021. № 3. P. 133-151.
  4. De-Gregorio F., Sung Y. Understanding attitudes toward and behaviors in response to product placement. Journal of Advertising. 2010. № 39 (1). P. 83-96. DOI:
  5. Elaheebocus S. M., Weal M., Morrison L. Peer-based social media features in behavior change interventions: Systematic review. Journal of Medical Internet Research. 2018. № 20 (2). P. 1-20. DOI:
  6. Erkan I. The influence of e-WOM in social media on consumers' purchase intentions: An extended approach to information adoption. Computers in Human Behavior. 2016. № 4. P. 47-55.
  7. Ferrara E., Interdonato R., Tagarelli A. Online popularity and topical interests through the lens of Instagram. Hypertext and Social Media. 2014. № 2. P. 24-23. DOI:
  8. Gao L. Online consumer behavior and its relationship to website atmospheric induced flow: Insights into online travel agencies in China. Journal of Retailing and Consumer Services. 2014. № 21 (4). P. 653-655.
  9. Geurin-Eagleman A. N. Communicating via photographs: A gendered analysis of Olympic athletes' visual self -presentation on Instagram. Sport Management Review. 2015. № 19 (2). P. 133-145.
  10. Guidry J. D., Messner M., Jin Y. From McDonalds fail to Dominos sucks: An analysis of Instagram images about the 10 largest fast food companies. Corporate Communications: An International Journal. 2015. № 20 (3). P. 344-359.
  11. Hanna R., Rohm A., Crittenden V. L. We're all connected: The power of the social media ecosystem. Business Horizons. 2011. № 54 (3). P. 265-273. DOI:
  12. Salganik M. Social Research in the Digital Age. Journal of Interactive Marketing. 2019. № 2 (9). P. 345-358.
  13. Hudson S., Roth M., Madden T. J. The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management. 2015. № 2 (8). P 68-76.
  14. Jeff M., Jennifer R., Catherine J., Elke P. Managing brand presence through social media: The case of UK football clubs. Internet Research. 2014. № 24 (2). Р. 181-204.
  15. Kim E. Brand followers' retweeting behaviour on Twitter: How brand relationship influence brand electronic word-of-mouth. Computers in Human Behavior. 2014. № 38 (8). P. 18-25.
  16. Kudeshia C., Sikdar P., Mittal A. Spreading love through fan page liking: A perspective on small scale entrepreneurs. Computers in Human Behavior. 2016. № 8 (19). P. 257-270.
  17. Lueg J. E. Interpersonal communication in the consumer socialization process: Scale development and validation. Journal of Marketing Theory and Practice. 2007. № 15 (1). P. 25-39.
  18. Mousavijad M. The effect of socialization factors on decision making of teenagers consumers in schools. Journal of School Administration. 2017. № 5 (1). P. 217-234.
  19. Schnell R. Enhancing Surveys with Objective Measurements and Observer Ratings. Journal of Interactive Marketing. 2015. № 44 (1). С. 288-302.
  20. Parry M. E., Kawakami T., Kishiya K. The effect of personal and virtual word-of-mouth on technology acceptance. Journal of Product Innovation Management. 2012. № 29 (6). P. 952-966.
  21. Quan-Haase A., Sloan L. Introduction to the Handbook of Social Media Research Methods: Goals, Challenges and Innovations. The Sage Handbook of Social Media Research Methods. 2017. № 10 (5). P. 606-859.
  22. Murphy S. T. Affect, cognition, and awareness: Affective priming with optimal and suboptimal stimulus exposures. Journal of Personality and Social Psychology. 2001. № 8 (3). P. 723-739.
  23. Ntale P. D. Word of mouth communication: A mediator of relationship marketing and customer loyalty. Cogent Business and Management. 2019. № 6 (18). P. 3-36.
  24. Schmäh M., Wilke T., Rossmann A. Electronic word of mouth: A systematic literature analysis. Digital Enterprise Computing. 2017. P. 147-158..
  25. Ozdemir A., Tozlu B., Şen E., Ateşoğlu A. Analyses of word-of-mouth communication and its effect on students' university preferences. Procedia - Social and Behavioral Sciences. 2016. № 8 (5). P. 22-35.
  26. Park J., Ciampaglia G. L., Ferrara F. Style in the age of Instagram: Predicting success within the fashion industry using social media. Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. 2016. № 22 (8). P. 64-72.
  27. Ranjbaran B., Jamshidian M., Dehghan Z. A survey of identification of major factors influencing customers attitude toward machine made carpet brands. Journal of Business Strategies. 2007. № 5 (23). P. 109-118.