تخمین اعتماد به نفس کاربران شبکه اجتماعی بر اساس مشخصات حساب کاربری آنها

صالحی, هانیه; کائدی, مرجان; ستاری, محمد

doi:10.22052/scj.2024.254082.1210

تخمین اعتماد به نفس کاربران شبکه اجتماعی بر اساس مشخصات حساب کاربری آنها

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ دانشکده مهندسی کامپیوتر، دانشگاه اصفهان، اصفهان، ایران.

² گروه مدیریت و فناوری اطلاعات سلامت، دانشکده مدیریت و اطلاع رسانی پزشکی، دانشگاه علوم پزشکی اصفهان، اصفهان، ایران.

10.22052/scj.2024.254082.1210

چکیده

چگونگی تعاملات هر کاربر در شبکه اجتماعی، می‌تواند شخصیت او را منعکس کند. بنابراین، با تحلیل داده‌های تعاملات کاربران در شبکه اجتماعی می‌توان ویژگی‌های شخصیتی آنها را تخمین زد و از این شخصیت تخمینی برای اهداف مختلف از جمله پایش سلامت روان کاربران، شخصی‌سازی ارائه محتواها و خدمات به کاربر، فعالیت‌های بازاریابی و تبلیغات استفاده کرد. در این پژوهش، بر ویژگی شخصیتی اعتماد به نفس تمرکز شده است و مدل‌هایی برای تخمین اعتماد به نفس کاربران ایرانی شبکه اجتماعی اینستاگرام ارائه می‌گردد. برای این منظور، ابتدا جمعیتی از کاربران شبکه اینستاگرام در نظر گرفته شدند. این کاربران پرسشنامه روزنبرگ را تکمیل کردند تا اعتماد به نفس آنها سنجیده شود. سپس ویژگی‌های مختلفی نظیر تعداد دنبال‌کنندگان و دنبال‌شوندگان کاربران، مشخصات تصویر پروفایل آنها و غیره نیز از حساب کاربری این افراد استخراج شد و در کنار میزان اعتماد به نفس آنها قرار گرفت. با اعمال الگوریتم‌های یادگیری ماشین بر روی این داده‌ها، مدل‌هایی برای تخمین اعتماد به نفس کاربران به دست آمد و مورد ارزیابی قرار گرفت. نشان داده شد که روش پیشنهادی می‌تواند با دریافت اطلاعات حساب کاربری کاربران اینستاگرام، با صحت0.81 و دقت 0.77، اعتماد به نفس کاربران را تخمین بزند. همچنین نتایج نشان داد که در صورتی که اعتماد به نفس کاربران خانم و آقا به صورت جداگانه مدل‌سازی گردد، مدل‌های دقیق‌تری به دست خواهد آمد.

کلیدواژه‌ها

موضوعات

داده کاوی

عنوان مقاله [English]

Estimating the Self-esteem of Social Network Users Based on Their Account Information

نویسندگان [English]

Hanieh Salehi ¹
Marjan Kaedi ¹
Mohammad Sattari ²

¹ Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.

² Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran.

چکیده [English]

The users’ interactions in social networks can reflect their personality traits. Therefore, by analyzing the data of users' interactions, their personality traits can be estimated. Then, the estimated personality traits can be used for various purposes, including monitoring the mental health of users, personalizing content and services to the users, marketing, and advertising activities. In this study, the focus is on the self-esteem personality trait, and models are presented to estimate the self-esteem of Iranian users of the Instagram social network. For this purpose, first, a population of Instagram users was considered. These users completed the Rosenberg questionnaire and their self-esteem was measured. Then, various features (e.g., the number of followers and followers of users, and their profile image specifications) were also extracted from the account of these users to be used together with their self-esteem level. By applying machine learning algorithms to these data, models were extracted to estimate users' confidence. The evaluation shows that the proposed method can estimate users' self-esteem based on the information of users’ Instagram accounts with the accuracy of 0.81 and the precision of 0.77. Also, the results showed that if the self-esteem of males and females is modeled separately, more accurate models will be obtained.

کلیدواژه‌ها [English]

Online social networks
User’s personality
Self-esteem
Machine learning
User account

مراجع

[1] J. Zhang, M.S. Ackerman, and L. Adamic, “Expertise networks in online communities: Structure and algorithms,” in Proc. 16th Int. Conf. World Wide Web (WWW), 2007, pp. 221-230, doi: 10.1145/1242572.1242603.
[2] C. Wilson, B. Boe, A. Sala, K.P.N. Puttaswamy, and B.Y. Zhao, “User interactions in social networks and their implications,” in Proc. 4th ACM Eur. Conf. Comput. Syst. (EuroSys), 2009, pp. 205-218, doi: 10.1145/1519065.1519089.
[3] M. Shirkhodai, M. shahi, S. Nejat, and S. Mahmudi Nasab, “The Effect of Social Media on Trust and Brand Loyalty Formation in the Brand Community (Case Study: The Social Network of Instagram),” New Mark. Res. J., vol. 7, no. 3, pp. 107-124, 2018, doi: 10.22108/nmrj.2017.103055.1181 [In Persian].
[4] A. Zareie, A. Sheikhahmadi, and M. Jalili, “Identification of influential users in social networks based on users’ interest,” Inf. Sci., vol. 493, pp. 217-231, 2019, doi: 10.1016/j.ins.2019.04.033.
[5] I. Golkar Amnieh and M. Kaedi, “Using estimated personality of social network members for finding influential nodes in viral marketing,” Cybern. Syst., vol. 46, no. 5, pp. 355-378, Jul. 2015, doi: 10.1080/01969722.2015.1029769.
[6] A. Ellis and M. Abrams, Personality Theories: Critical Perspectives, 1st Ed. SAGE Publications, 2008, doi: 10.4135/9781452231617.
[7] R.B. Ewen, An Introduction to Theories of Personality, 7th Ed. Psychology Press, 2009, doi: 10.1192/S000712500004962X.
[8] L. PourMohammadBagher, M. Kaedi, N. Ghasem-Aghaee, and T.I. Oren, “Anger evaluation for fuzzy agents with dynamic personality,” Math. Comput. Model. Dyn. Syst., vol. 15, no. 6, pp. 535-553, 2009, doi: 10.1080/13873950903139585.
[9] R.R. McCrae, “An introduction to the five‐factor model and its applications,” J. Pers., vol. 70, no. 2, pp. 175-215, 1992, doi: 10.1111/j.1467-6494.1992.tb00970.x.
[10] A. Kazeminia, M. Kaedi, and B. Ganji, “Personality-based personalization of online store features using genetic programming: Analysis and experiment,” J. Theor. Appl. Electron. Commer. Res., vol. 14, no. 1, pp. 16-29, 2019, doi: 10.4067/S0718-18762019000100103.
[11] S. Yousefian Jazi, M. Kaedi, and A. Fatemi, “An emotion-aware music recommender system: Bridging the user’s interaction and music recommendation,” Multimedia Tools Appl., vol. 80, pp. 13559-13574, 2021, doi: 10.1007/s11042-020-10386-7.
[12] N. Moosarrezayi Golyan and J. Hamidzadeh, “Design a Hybrid Recommender System Solving Cold-start Problem Using Clustering and Chaotic PSO Algorithm,” Soft Comput. J., vol. 7, no. 1, pp. 50-61, 2018, doi: 10.22052/7.1.50 [In Persian].
[13] N. Branden, The Six Pillars of Self-esteem. New York, NY, USA: Bantam, 1994.
[14] P.H. Arnoux, A. Xu, N. Boyette, J. Mahmud, R. Akkiraju, and V. Sinha, “25 tweets to know you: A new model to predict personality with social media,” in Proc. 11th Int. AAAI Conf. Web Social Media (ICWSM), 2017, pp. 472-475.
[15] D. Karanatsiou, P. Sermpezis, J.O.N. 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 Trans. Web, vol. 16, no. 10, pp. 1-26, 2022, doi: 10.1145/3523749.
[16] B. Ferwerda and M. Tkalcic, “Predicting users’ personality from instagram pictures: Using visual and/or content features?” in Proc. 26th Conf. User Model. Adapt. Pers. (UMAP), 2018, pp. 157-161, doi: 10.1145/3209219.3209248.
[17] M. Skowron, B. Ferwerda, M. Tkalcic, and M. Schedl, “Fusing social media cues: Personality prediction from Twitter and Instagram,” in Proc. 25th Int. Conf. Companion World Wide Web (WWW), 2016, pp. 107-108, doi: 10.1145/2872518.2889368.
[18] M. Hassanein, “Predicting personality trait from social media using text semantics,” in Proc. 13th Int. Conf. Comput. Eng. Syst. (ICCES), 2018, pp. 184-189, doi: 10.1109/ICCES.2018.8639408.
[19] I. Golkar and M. Kaedi, “Developing a model for estimating the extraversion degree of social network members using the information extracted from the graph structure,” J. Modeling Eng., vol. 13, no. 43, pp. 91-106, 2015, doi: 10.22075/jme.2017.1742.
[20] S. El Bahy, N. Aboutabit, and I. Hafidi, “Analyzing Instagram images to predict personality traits,” in Proc. Int. Conf. Intell. Comput. Multimedia Intell. Appl. (ICMICSA), Morocco, 2022, pp. 349-360, doi: 10.1007/978-3-031-29313-9_31.
[21] M.A. Kosan, H. Karacan, and B.A. Urgen, “Personality traits prediction model from Turkish contents with semantic structures,” Neural Comput. Appl., vol. 35, pp. 17147-17165, 2023, doi: 10.1007/s00521-023-08603-z.
[22] M.A. Kosan, H. Karacan, and B.A. Urgen, “Predicting personality traits with semantic structures and LSTM-based neural networks,” Alexandria Eng. J., vol. 61, no. 10, pp. 8007-8025, 2022, doi: 10.1016/j.aej.2022.01.050.
[23] H. Mustafa, S. Yaqoob, and T. Mansoor, “Real time personality analysis by Tweet mining,” J. Innov. Comput. Emerging Technol., vol. 3, no. 1, 2023, doi: 10.56536/jicet.v3i1.60.
[24] A. Khosravi and H. Abdolhosseini, “Personality in social networks using thematic modelling of user feedback,” Soft Comput. J., vol. 11, no. 2, pp. 50-61, 2023, doi: 10.22052/scj.2023.243197.1006 [In Persian].
[25] M. Dandash and M. Asadpour, “Recognizing personality traits using Twitter & Facebook for Arabic speaking users in Lebanon,” Int. J. Inf. Commun. Technol. Res., vol. 15, no. 1, pp. 45-55, 2023, doi: 10.61186/itrc.15.1.45.
[26] X. Qin et al., “User OCEAN personality model construction method using a BP neural network,” Electronics, vol. 11, no. 22, p. 3022, 2022, doi: 10.3390/electronics11193022.
[27] A.A. Marouf, M.K. Hasan, and H. Mahmud, “Comparative analysis of feature selection algorithms for computational personality prediction from social media,” IEEE Trans. Comput. Social Syst., vol. 7, no. 3, pp. 587-599, 2020, doi: 10.1109/TCSS.2020.2966910.
[28] Z. Hassanzadeh, B.M. Nezhady, M. Zavar, P. Alipour, and R. Branch, “A comparative survey on the effects of rhinoplasty on confidence and happiness,” Researcher Bull. Med. Sci., vol. 23, no. 1, pp. 1-4, 2018.
[29] T. Jo, Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning. Cham, Switzerland: Springer Nature, 2021, doi: 10.1007/978-3-030-65900-4.
[30] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd Ed. Waltham, MA, USA: Morgan Kaufmann, 2012, doi: 10.1016/C2009-0-61819-5.
[31] M. Kaedi and N. Ghasem-Aghaee, “Improving case-based reasoning in solving optimization problems using Bayesian optimization algorithm,” Intell. Data Anal., vol. 16, no. 2, pp. 199-210, 2012, doi: 10.3233/IDA-2012-0519.
[32] L. Lu, T. Zhou, Q. M. Zhang, and H.E. Stanley, “The H-index of a network node and its relation to degree and coreness,” Nature Commun., vol. 7, no. 1, p. 10168, 2016, doi: 10.1038/ncomms10168.
[33] S.A. Sheikhahmadi, S.A. Sheikhahmadi, and S. Mohamadimajd, “Identifying nodes with high spreader power in social networks by considering positive and negative influence weights,” Soft Comput. J., vol. 10, no. 1, pp. 122-133, 2021, doi: 10.22052/scj.2022.243233.1019 [In Persian].
[34] A. Keypour, M. Barari, and H. Shirazi, “Link prediction in social networks through classifiers combination," Soft Comput. J., vol. 4, no. 2, pp. 2-17, 2016, dor: 20.1001.1.23223707.1394.4.2.54.4 [In Persian].