A Recommender System Based on the Analysis of Personality Traits in Telegram Social Network
Subject Areas : AI and RoboticsMohammad Javad shayegan 1 * , mohadeseh valizadeh 2
1 - University of Science and Culture
2 - Department of Computer Engineering, University of Science and Culture, Tehran,Iran
Keywords: Users' Behavior, Recommender Systems, Social Networks, Telegram, Personality Analysis,
Abstract :
Analysis of personality traits of individuals has always been one of the interesting research topics. In addition, achieving personality traits based on data obtained from individuals' behavior is a challenging issue. Most people spend most of their time on social media and may engage in behaviors that represent a character in cyberspace. There are many social networks today, one of which is the Telegram social network. Telegram also has a large audience in Iran and people use it to communicate, interact with others, educate, introduce products and so on. This research seeks to find out how a recommendation system can be built based on the personality traits of individuals. For this purpose, the personality of the users of a telegram group is identified using three algorithms, Cosine Similarity, MLP and Bayes, and finally, with the help of a recommending system, telegram channels tailored to each individual's personality are suggested to him. The research results show that this recommending system has attracted 65.42% of users' satisfaction.
[1] S. Han, H. Huang, and Y. Tang, "Knowledge of words: An interpretable approach for personality recognition from social media," Knowledge-Based Systems, vol. 194, p. 105550, 2020.
[2] Y. Mehta, S. Fatehi, A. Kazameini, C. Stachl, E. Cambria, and S. Eetemadi, "Bottom-up and top-down: Predicting personality with psycholinguistic and language model features," in 2020 IEEE International Conference on Data Mining (ICDM), 2020: IEEE, pp. 1184-1189.
[3] M. J. Shayegan and M. Valizadeh, "A Method for Identifying Personality Traits in Telegram," in 2022 8th International Conference on Web Research (ICWR), 2022: IEEE, pp. 88-93.
[4] C. Solinger, L. Hirshfield, S. Hirshfield, R. Friendman, and C. Leper, "Beyond Facebook Personality Prediction," in International Conference on Social Computing and Social Media, 2014: Springer, pp. 486-493.
[5] K.-H. Peng, L.-H. Liou, C.-S. Chang, and D.-S. Lee, "Predicting personality traits of Chinese users based on Facebook wall posts," in 2015 24th Wireless and Optical Communication Conference (WOCC), 2015: IEEE, pp. 9-14.
[6] G. Seidman, "Self-presentation and belonging on Facebook: How personality influences social media use and motivations," Personality and individual differences, vol. 54, no. 3, pp. 402-407, 2013.
[7] T. C. Marshall, K. Lefringhausen, and N. Ferenczi, "The Big Five, self-esteem, and narcissism as predictors of the topics people write about in Facebook status updates," Personality and Individual Differences, vol. 85, pp. 35-40, 2015.
[8] J. Golbeck, C. Robles, M. Edmondson, and K. Turner, "Predicting personality from twitter," in 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, 2011: IEEE, pp. 149-156.
[9] J. W. Pennebaker, M. E. Francis, and R. J. Booth, "Linguistic inquiry and word count: LIWC 2001," Mahway: Lawrence Erlbaum Associates, vol. 71, no. 2001, p. 2001, 2001.
[10] M. Coltheart, "The MRC psycholinguistic database," The Quarterly Journal of Experimental Psychology Section A, vol. 33, no. 4, pp. 497-505, 1981.
[11] D. Karanatsiou, P. Sermpezis, D. Gruda, K. Kafetsios, I. Dimitriadis, and A. Vakali, "My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social Networks," ACM Transactions on the Web (TWEB), vol. 16, no. 2, pp. 1-26, 2022.
[12] A. Khosravi and H. Abdolhosseini, "Personality in social networks using thematic modeling of user feedback," Soft Computing Journal, 2023.
[13] D. Wan, C. Zhang, M. Wu, and Z. An, "Personality prediction based on all characters of user social media information," in Chinese National Conference on Social Media Processing, 2014: Springer, pp. 220-230.
[14] R. Wald, T. Khoshgoftaar, and C. Sumner, "Machine prediction of personality from Facebook profiles," in 2012 IEEE 13th International Conference on Information Reuse & Integration (Iri), 2012: IEEE, pp. 109-115.
[15] L. Liu, D. Preotiuc-Pietro, Z. R. Samani, M. E. Moghaddam, and L. Ungar, "Analyzing personality through social media profile picture choice," in Tenth international AAAI conference on web and social media, 2016.
[16] L. C. Lukito, A. Erwin, J. Purnama, and W. Danoekoesoemo, "Social media user personality classification using computational linguistic," in 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE), 2016: IEEE, pp. 1-6.
[17] S. Kedar and D. Bormane, "Automatic personality assessment: A systematic review," in 2015 International Conference on Information Processing (ICIP), 2015: IEEE, pp. 326-331.
[18] R. Lambiotte and M. Kosinski, "Tracking the digital footprints of personality," Proceedings of the IEEE, vol. 102, no. 12, pp. 1934-1939, 2014.
[19] S. Ouni, F. Fkih, and M. N. Omri, "A survey of machine learning-based author profiling from texts analysis in social networks," Multimedia Tools and Applications, vol. 82, no. 24, pp. 36653-36686, 2023/10/01 2023, doi: 10.1007/s11042-023-14711-8.
[20] K. Biswas, P. Shivakumara, U. Pal, T. Chakraborti, T. Lu, and M. N. B. Ayub, "Fuzzy and genetic algorithm based approach for classification of personality traits oriented social media images," Knowledge-Based Systems, p. 108024, 2021.
[21] T. A. Mooradian and J. B. Nezlek, "Comparing the NEO-FFI and Saucier's Mini-Markers as measures of the Big Five," Personality and Individual Differences, vol. 21, no. 2, pp. 213-215, 1996.
[22] A. Furnham, "The big five versus the big four: the relationship between the Myers-Briggs Type Indicator (MBTI) and NEO-PI five factor model of personality," Personality and Individual Differences, vol. 21, no. 2, pp. 303-307, 1996.
[23] S. Jabri, A. Dahbi, T. Gadi, and A. Bassir, "Ranking of text documents using TF-IDF weighting and association rules mining," in 2018 4th International Conference on Optimization and Applications (ICOA), 2018: IEEE, pp. 1-6.
[24] B. Li and L. Han, "Distance weighted cosine similarity measure for text classification," in International Conference on Intelligent Data Engineering and Automated Learning, 2013: Springer, pp. 611-618.
[25] J. Bergstra, D. Yamins, and D. D. Cox, "Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms," in Proceedings of the 12th Python in science conference, 2013: Citeseer, pp. 13-20.
[26] M. W. Gardner and S. Dorling, "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences," Atmospheric environment, vol. 32, no. 14-15, pp. 2627-2636, 1998.