{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/applying-deep-machine-learning-for-psycho","title":"Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality","arxiv_id":"1703.06914","date":"2017-03-07","proceeding":null,"authors":["Iaroslav Omelianenko"],"abstract":"In the modern era, each Internet user leaves enormous amounts of auxiliary\ndigital residuals (footprints) by using a variety of on-line services. All this\ndata is already collected and stored for many years. In recent works, it was\ndemonstrated that it's possible to apply simple machine learning methods to\nanalyze collected digital footprints and to create psycho-demographic profiles\nof individuals. However, while these works clearly demonstrated the\napplicability of machine learning methods for such an analysis, created simple\nprediction models still lacks accuracy necessary to be successfully applied for\npractical needs. We have assumed that using advanced deep machine learning\nmethods may considerably increase the accuracy of predictions. We started with\nsimple machine learning methods to estimate basic prediction performance and\nmoved further by applying advanced methods based on shallow and deep neural\nnetworks. Then we compared prediction power of studied models and made\nconclusions about its performance. Finally, we made hypotheses how prediction\naccuracy can be further improved. As result of this work, we provide full\nsource code used in the experiments for all interested researchers and\npractitioners in corresponding GitHub repository. We believe that applying deep\nmachine learning for psycho-demographic profiling may have an enormous impact\non the society (for good or worse) and provides means for Artificial\nIntelligence (AI) systems to better understand humans by creating their\npsychological profiles. Thus AI agents may achieve the human-like ability to\nparticipate in conversation (communication) flow by anticipating human\nopponents' reactions, expectations, and behavior.","url_abs":"http://arxiv.org/abs/1703.06914v2","url_pdf":"http://arxiv.org/pdf/1703.06914v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"applying-deep-machine-learning-for-psycho","repo_url":"https://github.com/NewGround-LLC/psistats","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}