[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126269-en":3,"doc-seo-126269-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126269,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Personality Prediction Model - An Enhanced Machine Learning Approach","Social media platforms generate massive streams of publicly accessible content that can support research into human behavior and personality, yet unstructured data creates major obstacles, including sparsity, noise, and privacy ethics. This study applies machine learning to infer Big Five traits from Instagram content. A scalable, non-intrusive system is built by extracting visual features via two pretrained CNNs and training five random-forest models for Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness, achieving an average mean absolute error of 0.1867 and competitive results versus PAN-2015, enabling personalization, mental health monitoring, and human–computer interaction.","Personality prediction model: an enhanced machine learning approach  \nAshawa, Moses; Bryan, Joshua David; Owoh, Nsikak  \nPublished in: Electronics  \nDOI:  \n10.3390/electronics14132558  \nPublication date:  \n2025  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nAshawa, M, Bryan, JD & Owoh, N 2025, 'Personality prediction model: an enhanced machine learning approach', Electronics, vol. 14, no. 13, 2558. 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Jul. 2025  \nArticle  \nPersonality Prediction Model: An Enhanced Machine Learning Approach  \nMoses Ashawa 1, *, Joshua David Bryan 2 and Nsikak Owoh 1  \nReceived: 26 May 2025  \nRevised: 16 June 2025  \nAccepted: 18 June 2025  \nPublished: 24 June 2025  \nCitation: Ashawa, M.; Bryan, J.D.; Owoh, N. Personality Prediction Model: An Enhanced Machine Learning Approach. Electronics 2025, 14, 2558. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)electronics14132558  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK; [nsikak.owoh@gcu.ac.uk](nsikak.owoh@gcu.ac.uk)  \n2 Scottish Enterprise Technology Park, East Kilbride, Glasgow G75 0QD, UK; [jbryan201@caledonian.ac.uk](jbryan201@caledonian.ac.uk)  \n* [Correspondence: moses.ashawa@gcu.ac.uk](Correspondence: moses.ashawa@gcu.ac.uk)  \nAbstract  \nIn today’s digital era, social media platforms like Instagram have become deeply embedded in daily life, generating billions of content items each day. This vast stream of publicly accessible data presents a unique opportunity for researchers to gain insights into human behaviour and personality. However, leveraging such unstructured and highly variable data for psychological analysis introduces significant challenges, including data sparsity, noise, and ethical considerations around privacy. This study addresses these challenges by exploring the potential of machine learning to infer personality traits from Instagram content. Motivated by the growing demand for scalable, non-intrusive methods of psychological assessment, we developed a personality prediction system combining convolutional neural networks (CNNs) and random forest (RF) algorithms. Our model is grounded in the Big Five Personality framework, which includes Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using data collected with informed consent from 941 participants, we extracted visual features from their Instagram images using two pretrained CNNs, which were then used to train five RF models, each targeting a specific trait. The proposed system achieved an average mean absolute error of 0.1867 across all traits. Compared to the PAN-2015 benchmark, our method demonstrated competitive performance. These results highlight that using social media data for personality prediction offers potential applications in personalized content del","cbCaiePoXba4SyUZ","https://ap.wps.com/l/cbCaiePoXba4SyUZ","pdf",1377494,4,1,22,"English","en",105,"# 1. Introduction\n## Motivation and data opportunity\n## Key challenges and lack of standardization","[{\"question\":\"What data source does the personality prediction system use?\",\"answer\":\"It uses Instagram content collected from 941 participants, focusing on visual features extracted from their Instagram images.\"},{\"question\":\"Which personality framework and traits are targeted?\",\"answer\":\"The system is grounded in the Big Five Personality framework, predicting Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness.\"},{\"question\":\"How is the machine learning model built?\",\"answer\":\"Two pretrained convolutional neural networks extract visual features, which are then used to train five separate random-forest models, one per trait.\"}]","Personality Prediction Model - An Enhanced Machine Learning Approach | PDF",1785904163,55,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"personality-prediction-model-an-enhanced-machine-learning-approach","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/personality-prediction-model-an-enhanced-machine-learning-approach/126269/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What data source does the personality prediction system use?","Question",{"text":76,"@type":77},"It uses Instagram content collected from 941 participants, focusing on visual features extracted from their Instagram images.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which personality framework and traits are targeted?",{"text":81,"@type":77},"The system is grounded in the Big Five Personality framework, predicting Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the machine learning model built?",{"text":85,"@type":77},"Two pretrained convolutional neural networks extract visual features, which are then used to train five separate random-forest models, one per trait.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]