Abstract
Public healthcare is a big priority for society. The ability to diagnose and monitor various aspects of public health through social networks is one of the new problems that are of interest to researchers. In this paper, we consider the task of automatically classifying people who lead a healthy lifestyle and users who do not lead a healthy lifestyle by processing text messages and other profile information from the Russian-speaking social network VKontakte. We describe the process of extracting relevant data from user profiles for our dataset. We evaluate several machine learning methods and report experimental results. The best performance in our experiments was achieved by the model that was trained on a combination of N-gram features retrieved from user original posts and reposts.
The reported study was funded by RFBR according to the research project 18-29-22041.
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References
Khodorchenko, M., Butakov, N.: Developing an approach for lifestyle identification based on explicit and implicit features from social media. Procedia Comput. Sci. 136, 236–245 (2018)
Ilakkuvan, V., Johnson, A., Villanti, A.C., Evans, W.D., Turner, M.: Patterns of social media use and their relationship to health risks among young adults. J. Adolesc. Health 64(2), 158–164 (2019)
Furini, M., Menegoni, G.: Public health and social media: language analysis of vaccine conversations. In: 2018 International Workshop on Social Sensing (SocialSens), Orlando, FL, pp. 50–55 (2018)
Eichstaedt, J.C., et al.: Facebook language predicts depression in medical records. In: Proceedings of the National Academy of Sciences, October 2018, vol. 115, no. 44, pp. 11203–11208 (2018). https://doi.org/10.1073/pnas.1802331115
Ryan, R.M., Deci, E.L.: Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psychol. 25(1), 54–67 (2000)
McLachlan, S., Hagger, M.S.: Do people differentiate between intrinsic and extrinsic goals for physical activity? J. Sport Exerc. Psychol. 33(2), 273–288 (2011)
Straka, M., Straková, J.: Tokenizing, POS tagging, lemmatizing and parsing UD 2.0 with UDPipe. In: Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pp. 88–99 (2017)
Stankevich, M., Latyshev, A., Kuminskaya, E., Smirnov, I., Grigoriev, O.: Depression detection from social media texts (2019)
Stankevich, M., Latyshev, A., Kiselnikova, N., Smirnov, I.: Predicting personality traits from social network profiles. In: Kuznetsov, S.O., Panov, A.I. (eds.) RCAI 2019. CCIS, vol. 1093, pp. 177–188. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30763-9_15
Pennebaker, J.W.: The secret life of pronouns. New Sci. 211(2828), 42–45 (2011)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794, August 2016
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Khalil, K., Stankevich, M., Smirnov, I., Danina, M. (2020). Detection of Social Media Users Who Lead a Healthy Lifestyle. In: Kuznetsov, S.O., Panov, A.I., Yakovlev, K.S. (eds) Artificial Intelligence. RCAI 2020. Lecture Notes in Computer Science(), vol 12412. Springer, Cham. https://doi.org/10.1007/978-3-030-59535-7_17
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DOI: https://doi.org/10.1007/978-3-030-59535-7_17
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