{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:37:05Z","timestamp":1773952625835,"version":"3.50.1"},"reference-count":142,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Recent advancements in Natural Language Processing (NLP) and widely available social media data have made it possible to predict human personalities in various computational applications. In this context, pre-trained Large Language Models (LLMs) have gained recognition for their exceptional performance in NLP benchmarks. However, these models require substantial computational resources, escalating their carbon and water footprint. Consequently, a shift toward more computationally efficient smaller models is observed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study compares a small model ALBERT (11.8M parameters) with a larger model, RoBERTa (125M parameters) in predicting big five personality traits. It utilizes the PANDORA dataset comprising Reddit comments, processing them on a Tesla P100-PCIE-16GB GPU. The study customized both models to support multi-output regression and added two linear layers for fine-grained regression analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results are evaluated on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), considering the computational resources consumed during training. While ALBERT consumed lower levels of system memory with lower heat emission, it took higher computation time compared to RoBERTa. The study produced comparable levels of MSE, RMSE, and training loss reduction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This highlights the influence of training data quality on the model's performance, outweighing the significance of model size. Theoretical and practical implications are also discussed.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdata.2024.1387325","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:24:46Z","timestamp":1726201486000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Navigating pathways to automated personality prediction: a comparative study of small and medium language models"],"prefix":"10.3389","volume":"7","author":[{"given":"Fatima","family":"Habib","sequence":"first","affiliation":[]},{"given":"Zeeshan","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Akbar","family":"Azam","sequence":"additional","affiliation":[]},{"given":"Komal","family":"Kamran","sequence":"additional","affiliation":[]},{"given":"Fahad Mansoor","family":"Pasha","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"B1","first-page":"6","article-title":"\u201cPersonality traits recognition on social network - facebook,\u201d","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media","author":"Alam","year":"2013"},{"key":"B2","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1002\/per.2305","article-title":"Using big data and machine learning in personality measurement: opportunities and challenges","volume":"34","author":"Alexander","year":"2020","journal-title":"Eur. J. Pers."},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1908.10063","article-title":"FinBERT: financial sentiment analysis with pre-trained language models","author":"Araci","year":"2019","journal-title":"arXiv"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.3390\/s19091983","article-title":"Review on wearable technology sensors used in consumer sport applications","volume":"19","author":"Aroganam","year":"2019","journal-title":"Sensors"},{"key":"B5","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1177\/0894439319882896","article-title":"Predicting voting behavior using digital trace data","volume":"39","author":"Bach","year":"2021","journal-title":"Soc. Sci. Comput. Rev."},{"key":"B6","doi-asserted-by":"publisher","first-page":"112465","DOI":"10.1016\/j.paid.2023.112465","article-title":"The generalizability of machine learning models of personality across two text domains","volume":"217","author":"Berggren","year":"2024","journal-title":"Pers. Individ. Dif."},{"key":"B7","unstructured":"Language models are few-shot learners\n            BrownT. B.\n            MannB.\n            RyderN.\n            SubbiahM.\n            KaplanJ.\n            DhariwalP.\n          Adv. Neural Inf. Process. Syst.2020"},{"key":"B8","doi-asserted-by":"publisher","first-page":"5723","DOI":"10.1109\/TVT.2016.2639550","article-title":"MA-SSR: a memetic algorithm for skyline scenic routes planning leveraging heterogeneous user-generated digital footprints","volume":"66","author":"Chen","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00459-1","article-title":"Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging","volume":"8","author":"Christian","year":"2021","journal-title":"J. Big Data"},{"key":"B10","doi-asserted-by":"publisher","first-page":"103108","DOI":"10.1016\/j.im.2018.09.008","article-title":"Dissecting emotion and user influence in social media communities: an interaction modeling approach","volume":"57","author":"Chung","year":"2020","journal-title":"Inf. Manag."},{"key":"B11","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1017\/S1351324921000322","article-title":"Emerging trends: a gentle introduction to fine-tuning","volume":"27","author":"Church","year":"2021","journal-title":"Nat. Lang. Eng."},{"key":"B12","unstructured":"CuiB.\n            QiC.\n          Survey analysis of machine learning methods for natural language processing for MBTI personality type prediction2017"},{"key":"B13","doi-asserted-by":"publisher","first-page":"3811","DOI":"10.1007\/s11063-022-10787-9","article-title":"Contextualized multidimensional personality recognition using combination of deep neural network and ensemble learning","volume":"54","author":"Deilami","year":"2022","journal-title":"Neural Process. Lett."},{"key":"B14","first-page":"4171","article-title":"\u201cBERT: pre-training of deep bidirectional transformers for language understanding,\u201d","volume-title":"Proceedings of NAACL-HLT","author":"Devlin","year":"2019"},{"key":"B15","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.eij.2020.09.001","article-title":"Psychological human traits detection based on universal language modeling","volume":"22","author":"El-Demerdash","year":"2021","journal-title":"Egypt. Informatics J."},{"key":"B16","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.eij.2021.05.004","article-title":"Deep learning based fusion strategies for personality prediction","volume":"23","author":"El-Demerdash","year":"2022","journal-title":"Egypt. Informatics J"},{"key":"B17","doi-asserted-by":"publisher","first-page":"17760","DOI":"10.1109\/ACCESS.2024.3359115","article-title":"Deep learning for multi-output regression using gradient boosting","volume":"12","author":"Emami","year":"2024","journal-title":"IEEE Access"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1037\/apl0001082","article-title":"How well can an AI chatbot infer personality? Examining psychometric properties of machine-inferred personality scores. J","volume":"108","author":"Fan","year":"","journal-title":"Appl. Psychol."},{"key":"B19","unstructured":"A bibliometric review of large language models research from 2017 to 2023\n            FanL.\n            LiL.\n            MaZ.\n            LeeS.\n            YuH.\n            HemphillL.\n          arXiv"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.1007\/s42001-022-00178-4","article-title":"Text-based automatic personality prediction: a bibliographic review","volume":"5","author":"Feizi-Derakhshi","year":"2022","journal-title":"J. Comput. Soc. Sci."},{"key":"B21","unstructured":"\u201cSpecializing smaller language models towards multi-step reasoning,\u201d\n            FuY.\n            PengH.\n            OuL.\n            SabharwalA.\n            KhotT.\n          Proceedings of the 40th International Conference on Machine Learning2023"},{"key":"B22","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1177\/0963721412445309","article-title":"Accurate personality judgment","volume":"21","author":"Funder","year":"2012","journal-title":"Curr. Dir. Psychol. Sci."},{"key":"B23","unstructured":"\u201cDemographic-aware language model fine-tuning as a bias mitigation technique,\u201d\n            GarimellaA.\n            ResearchA.\n            AmarnathA.\n            MihalceaR.\n          Association for Computational Linguistics (Short Papers)2022"},{"key":"B24","first-page":"138","article-title":"\u201cPANDORA talks : personality and demographics on Reddit,\u201d","volume-title":"Soc. 2021 - 9th Int. Work. Nat. Lang. Process. Soc. Media, Proc. Work","author":"Gjurkovic","year":"2021"},{"key":"B25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.18653\/v1\/W18-1112","article-title":"\u201cReddit: a gold mine for personality prediction,\u201d","volume-title":"Proceedings of the Second Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media","author":"Gjurkovi\u0107","year":"2018"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1177\/0956797619849435","article-title":"Can psychological traits be inferred from spending? Evidence from transaction data","volume":"30","author":"Gladstone","year":"2019","journal-title":"Psychol. Sci."},{"key":"B27","first-page":"203","article-title":"From ace to zombie: some explorations in the language of personality","volume":"1","author":"Goldberg","year":"1982","journal-title":"Adv. Personal. Assess."},{"key":"B28","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1146\/annurev-soc-071913-043145","article-title":"Digital footprints: opportunities and challenges for online social research","volume":"40","author":"Golder","year":"2014","journal-title":"Annu. Rev. Sociol."},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.17632\/3sndbd4p84.1v","author":"Habib","year":"2024","journal-title":"\u201cAutomated Personality Prediction\u201d, Mendeley Data, V1"},{"key":"B30","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1177\/1745691616650285","article-title":"Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges","volume":"11","author":"Harari","year":"2016","journal-title":"Perspect. Psychol. Sci."},{"key":"B31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.2002.05651","article-title":"Towards the systematic reporting of the energy and carbon footprints of machine learning","volume":"21","author":"Henderson","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"B32","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1177\/0963721419827849","article-title":"Human and computer personality prediction from digital footprints","volume":"28","author":"Hinds","year":"2019","journal-title":"Curr. Dir. Psychol. Sci."},{"key":"B33","doi-asserted-by":"publisher","first-page":"e0207112","DOI":"10.1371\/journal.pone.0207112","article-title":"What demographic attributes do our digital footprints reveal? A systematic review","volume":"13","author":"Hinds","year":"2018","journal-title":"PLoS ONE"},{"key":"B34","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1177\/0956797611436349","article-title":"Personalized persuasion: tailoring persuasive appeals to recipients' personality traits","volume":"23","author":"Hirsh","year":"2012","journal-title":"Psychol. Sci."},{"key":"B35","doi-asserted-by":"crossref","first-page":"8003","DOI":"10.18653\/v1\/2023.findings-acl.507","article-title":"\u201cDistilling step-by-step! Outperforming larger language models with less training data and smaller model sizes,\u201d","volume-title":"Findings of the Association for Computational Linguistics: ACL 2023","author":"Hsieh","year":"2023"},{"key":"B36","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1037\/cpb0000106","article-title":"The new technologies in personality assessment: a review","volume":"70","author":"Ihsan","year":"2018","journal-title":"Consult. Psychol. J."},{"key":"B37","first-page":"13821","article-title":"\u201cAutomatic text-based personality recognition on monologues and multiparty dialogues using attentive networks and contextual embeddings,\u201d","volume-title":"AAAI 2020 - 34th AAAI Conference on Artificial Intelligence","author":"Jiang","year":"2020"},{"key":"B38","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/s42979-023-01670-y","article-title":"An aspect-aware enhanced psycholinguistic knowledge graph-based personality detection using deep learning","volume":"4","author":"Johnson","year":"2023","journal-title":"SN Comput. Sci."},{"key":"B39","doi-asserted-by":"publisher","first-page":"3986","DOI":"10.18653\/v1\/2022.findings-emnlp.294","article-title":"\u201cYou are what you talk about: inducing evaluative topics for personality analysis,\u201d","author":"Juki\u0107","year":"2022","journal-title":"Findings of the Association for Computational Linguistics: EMNLP"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1145\/3523749","article-title":"\u201cMy Tweets bring all the traits to the yard: predicting personality and relational traits in online social networks,\u201d","author":"Karanatsiou","year":"2022","journal-title":"ACM Transactions on the"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.01309","article-title":"Personality trait detection using bagged SVM over BERT word embedding ensembles","author":"Kazameini","year":"2020","journal-title":"arXiv"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1907.06333","article-title":"Myers-Briggs personality classification and personality-specific language generation using pre-trained language models","author":"Keh","year":"2019","journal-title":"arXiv"},{"key":"B43","doi-asserted-by":"publisher","first-page":"111479","DOI":"10.1016\/j.paid.2021.111479","article-title":"Evaluation of tree-based ensemble algorithms for predicting the big five personality traits based on social media photos: evidence from an Iranian sample","volume":"188","author":"Khorrami","year":"2022","journal-title":"Pers. Individ. Dif."},{"key":"B44","unstructured":"KimJ.\n            LeeB.\n            BearmanP.\n            BaldassarriD.\n            BachJ.\n            BonikowskiB.\n          AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction2023"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.31234\/osf.io\/yfd8g","article-title":"Beyond rating scales: with care for validation large language models are poised to change psychological assessment","author":"Kjell","year":"2023","journal-title":"arXiv"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13636-022-00269-0","article-title":"Beyond the Big Five personality traits for music recommendation systems","volume":"2023","author":"Kle\u0107","year":"2023","journal-title":"Eurasip J. Audio Speech Music Process."},{"key":"B47","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1007\/s10994-013-5415-y","article-title":"Manifestations of user personality in website choice and behaviour on online social networks","volume":"95","author":"Kosinski","year":"2014","journal-title":"Mach. Learn."},{"key":"B48","doi-asserted-by":"publisher","first-page":"5802","DOI":"10.1073\/pnas.1218772110","article-title":"Private traits and attributes are predictable from digital records of human behavior","volume":"110","author":"Kosinski","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA."},{"key":"B49","doi-asserted-by":"publisher","first-page":"e0201703","DOI":"10.1371\/journal.pone.0201703","article-title":"Latent human traits in the language of social media: an open-vocabulary approach","volume":"13","author":"Kulkarni","year":"2018","journal-title":"PLoS ONE"},{"key":"B50","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1201\/9781003348689-3","article-title":"\u201cTransformers: State-of-the-Art natural language processing,\u201d","volume-title":"Deep Learning Approach for Natural Language Processing, Speech, and Computer Vision","author":"Kumar","year":"2023"},{"key":"B51","unstructured":"\u201cALBERT: a lite BERT for self-supervised learning of language Representations,\u201d\n            LanZ.\n            ChenM.\n            GoodmanS.\n            GimpelK.\n            SharmaP.\n            SoricutR.\n          International Conference on Learning Representations, ICLR8th International Conference on Learning Representations, ICLR 20202019"},{"key":"B52","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"B53","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1080\/14626268.2015.1087410","article-title":"Impact of digital traces on the appreciation of movie contents","volume":"26","author":"Lee","year":"2015","journal-title":"Digit. Creat."},{"key":"B54","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1111\/jopy.12075","article-title":"An examination of information quality as a moderator of accurate personality judgment","volume":"82","author":"Letzring","year":"2014","journal-title":"J. Pers."},{"key":"B55","doi-asserted-by":"crossref","DOI":"10.1109\/DSAA.2015.7344887","article-title":"\u201cUsing emotions to predict user interest areas in online social networks,\u201d","volume-title":"Proc. 2015 IEEE Int. Conf. Data Sci. Adv. Anal. DSAA 2015","author":"Lewenberg","year":"2015"},{"key":"B56","doi-asserted-by":"publisher","first-page":"9459","DOI":"10.48550\/arXiv.2005.11401","article-title":"Retrieval-augmented generation for knowledge-intensive NLP tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B57","unstructured":"Making AI less \u201cthirsty\u201d: uncovering and addressing the secret water footprint of AI models\n            LiP.\n            YangJ.\n            IslamM. A.\n            RenS.\n          arXiv2023"},{"key":"B58","unstructured":"Multitask learning for emotion and personality detection\n            LiY.\n            KazameiniA.\n            MehtaY.\n            CambriaE.\n          arXiv2021"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-62922-y","article-title":"BEHRT: transformer for electronic health records","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"B60","doi-asserted-by":"publisher","first-page":"e13421","DOI":"10.2196\/13421","article-title":"Validation of the mobile app-recorded circadian rhythm by a digital footprint","volume":"7","author":"Lin","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"B61","doi-asserted-by":"publisher","first-page":"100017","DOI":"10.1016\/j.metrad.2023.100017","article-title":"Summary of ChatGPT-related research and perspective towards the future of large language models","volume":"1","author":"Liu","year":"2023","journal-title":"Meta-Radiology"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1907.11692","article-title":"RoBERTa: a robustly optimized BERT pretraining approach","author":"Liu","year":"2019","journal-title":"arXiv"},{"key":"B63","doi-asserted-by":"publisher","first-page":"e93803","DOI":"10.15446\/ing.investig.93803","article-title":"Automatic personality evaluation from transliterations of YouTube vlogs using classical and state-of-the-art word embeddings","volume":"42","author":"L\u00f3pez-Pab\u00f3n","year":"2022","journal-title":"Ing. Investig."},{"key":"B64","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/MIS.2017.23","article-title":"Deep learning-based document modeling for personality detection from text","volume":"32","author":"Majumder","year":"2017","journal-title":"IEEE Intell. Syst."},{"key":"B65","first-page":"23","article-title":"\u201cMining facebook data for predictive personality modeling,\u201d","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media","author":"Markovikj","year":"2013"},{"key":"B66","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1710966114","article-title":"Psychological targeting as an effective approach to digital mass persuasion","author":"Matz","year":"2017","journal-title":"Proc. Natl. Acad. Sci. USA."},{"key":"B67","doi-asserted-by":"publisher","DOI":"10.31234\/osf.io\/rn97c","article-title":"The potential of generative AI for personalized persuasion at scale","author":"Matz","year":"2023","journal-title":"PsyArXiv"},{"key":"B68","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1111\/j.1467-6494.1992.tb00970.x","article-title":"An introduction to the five-factor model and its applications","volume":"60","author":"McCrae","year":"1992","journal-title":"J. Pers."},{"key":"B69","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.18653\/v1\/2022.findings-naacl.151","article-title":"\u201cGreat power, great responsibility: recommendations for reducing energy for training language models,\u201d","volume-title":"Find. Assoc. Comput. Linguist. NAACL 2022","author":"McDonald","year":"2022"},{"key":"B70","first-page":"1184","article-title":"\u201cBottom-up and top-down: predicting personality with psycholinguistic and language model features,\u201d","volume-title":"Proceedings - IEEE International Conference on Data Mining, ICDM","author":"Mehta","year":""},{"key":"B71","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1007\/s10462-019-09770-z","article-title":"Recent trends in deep learning based personality detection","volume":"53","author":"Mehta","year":"","journal-title":"Artif. Intell. Rev."},{"key":"B72","first-page":"27","article-title":"\u201cUsing nuances of emotion to identify personality,\u201d","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media","author":"Mohammad","year":"2013"},{"key":"B73","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.jrp.2017.12.004","article-title":"Phone-based metric as a predictor for basic personality traits","volume":"74","author":"M\u00f8nsted","year":"2018","journal-title":"J. Res. Pers."},{"key":"B74","doi-asserted-by":"publisher","first-page":"110818","DOI":"10.1016\/j.paid.2021.110818","article-title":"Can personality traits be measured analyzing written language? A meta-analytic study on computational methods","volume":"177","author":"Moreno","year":"2021","journal-title":"Pers. Individ. Dif."},{"key":"B75","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1177\/0956797618761659","article-title":"Musical preferences predict personality: evidence from active listening and facebook likes","volume":"29","author":"Nave","year":"2018","journal-title":"Psychol. Sci."},{"key":"B76","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.jcss.2013.03.008","article-title":"Predicting user personality by mining social interactions in facebook","volume":"80","author":"Ortigosa","year":"2014","journal-title":"J. Comput. Syst. Sci."},{"key":"B77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-022-00603-5","article-title":"NLP-based platform as a service: a brief review","volume":"9","author":"Pais","year":"2022","journal-title":"J. Big Data"},{"key":"B78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533378","article-title":"Challenges in deploying machine learning: a survey of case studies","volume":"55","author":"Paleyes","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"B79","doi-asserted-by":"publisher","DOI":"10.1101\/2023.07.17.549421","article-title":"Identification and description of emotions by current large language models","author":"Patel","year":"2023","journal-title":"bioRxiv"},{"key":"B80","unstructured":"PattersonD.\n            GonzalezJ.\n            LeQ.\n            LiangC.\n            MunguiaL.-M.\n            RothchildD.\n          Carbon Emissions and Large Neural Network Training2021"},{"key":"B81","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1109\/WOCC.2015.7346106","article-title":"\u201cPredicting personality traits of Chinese users based on Facebook wall posts,\u201d","volume-title":"2015 24th Wireless and Optical Communication Conference, WOCC","author":"Peng","year":"2015"},{"key":"B82","first-page":"11326","article-title":"\u201cCustomising general large language models for specialised emotion recognition tasks,\u201d","volume-title":"ICASSP 2024 - 2024 IEEE Int. Conf. Acoust. Speech Signal Process.","author":"Peng","year":"2024"},{"key":"B83","doi-asserted-by":"crossref","unstructured":"PetersH.\n            MatzS.\n          38948324Large Language Models Can Infer Psychological Dispositions of Social Media Users2023","DOI":"10.1093\/pnasnexus\/pgae231"},{"key":"B84","doi-asserted-by":"publisher","first-page":"e12624","DOI":"10.1111\/spc3.12624","article-title":"Personality computing: new frontiers in personality assessment","volume":"15","author":"Phan","year":"2021","journal-title":"Soc. Personal. Psychol. Compass"},{"key":"B85","unstructured":"Shapeshifter networks: cross-layer parameter sharing for scalable and effective deep learning\n            PlummerB. A.\n            DrydenN.\n            Z\u00fcrichE.\n            FrostJ.\n            HoeflerT.\n            SaenkoK.\n          arXiv2020"},{"key":"B86","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1037\/0021-9010.88.5.879","article-title":"Common method biases in behavioral research: a critical review of the literature and recommended remedies","volume":"88","author":"Podsakoff","year":"2003","journal-title":"J. Appl. Psychol."},{"key":"B87","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1037\/a0014996","article-title":"A meta-analysis of the five-factor model of personality and academic performance","volume":"135","author":"Poropat","year":"2009","journal-title":"Psychol. Bull."},{"key":"B88","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep03141","article-title":"Quantifying the digital traces of hurricane sandy on flickr","volume":"3","author":"Preis","year":"2013","journal-title":"Sci. Rep."},{"key":"B89","first-page":"1","article-title":"\u201cRoBERTa as semantic approach for Big Five personality prediction using artificial neural network on Twitter,\u201d","volume-title":"2022 International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS)","author":"Putra","year":"2022"},{"key":"B90","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv:1904.07531","article-title":"Understanding the behaviors of BERT in ranking","author":"Qiao","year":"2019","journal-title":"arXiv"},{"key":"B91","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/PASSAT\/SocialCom.2011.26","article-title":"\u201cOur twitter profiles, our selves: predicting personality with twitter,\u201d","author":"Quercia","year":"2011","journal-title":"Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT\/SocialCom"},{"key":"B92","doi-asserted-by":"publisher","first-page":"102665","DOI":"10.1016\/j.simpat.2022.102665","article-title":"Shooter video games for personality prediction using five factor model traits and machine learning","volume":"122","author":"Quwaider","year":"2023","journal-title":"Simul. Model. Pract. Theory"},{"key":"B93","doi-asserted-by":"publisher","first-page":"4506","DOI":"10.3390\/app13074506","article-title":"Personality types and traits\u2014examining and leveraging the relationship between different personality models for mutual prediction","volume":"13","author":"Radisavljevi\u0107","year":"2023","journal-title":"Appl. Sci."},{"key":"B94","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1177\/0963721419861410","article-title":"Digital traces: new data, resources, and tools for psychological-science research","volume":"28","author":"Rafaeli","year":"2019","journal-title":"Curr. Dir. Psychol. Sci."},{"key":"B95","first-page":"673","article-title":"\u201cLanguage detection using natural language processing,\u201d","volume-title":"2023 9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023","author":"Rajanak","year":"2023"},{"key":"B96","doi-asserted-by":"publisher","first-page":"154704","DOI":"10.1109\/ACCESS.2021.3128742","article-title":"BERT, XLNet or RoBERTa: the best transfer learning model to detect clickbaits","volume":"9","author":"Rajapaksha","year":"2021","journal-title":"IEEE Access"},{"key":"B97","doi-asserted-by":"publisher","first-page":"21453","DOI":"10.1038\/s41598-022-25955-z","article-title":"Text-based automatic personality prediction using KGrAt-Net: a knowledge graph attention network classifier","volume":"12","author":"Ramezani","year":"2022","journal-title":"Sci. Rep."},{"key":"B98","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1037\/0022-3514.84.6.1236","article-title":"The do re mi's of everyday life: the structure and personality correlates of music preferences","volume":"84","author":"Rentfrow","year":"2003","journal-title":"J. Pers. Soc. Psychol."},{"key":"B99","doi-asserted-by":"publisher","first-page":"3464","DOI":"10.1021\/acs.est.3c01106","article-title":"Risks and benefits of large language models for the environment","volume":"57","author":"Rillig","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"B100","unstructured":"RothmanD.\n          Transformers for Natural Language Processing Build innovative deep neural network architectures for NLP & Python, PyTorch, TensorFlow, BERT, RoBERTa & more2021"},{"key":"B101","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1910.01108","article-title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","author":"Sanh","year":"2019","journal-title":"arXiv"},{"key":"B102","first-page":"2339","article-title":"\u201cIt's not just size that matters: small language models are also few-shot learners,\u201d","volume-title":"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Schick","year":"2020"},{"key":"B103","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1109\/TAFFC.2016.2516994","article-title":"The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits","volume":"8","author":"Segalin","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"B104","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.48550\/arXiv.2305.14195","article-title":"HumBEL: a human-in-the-loop approach for evaluating demographic factors of language models in human-machine conversations","volume":"1","author":"Sicilia","year":"2024","journal-title":"Assoc. Comput. Linguist."},{"key":"B105","doi-asserted-by":"publisher","first-page":"13693","DOI":"10.1609\/aaai.v34i09.7123","article-title":"Energy and policy considerations for modern deep learning research","volume":"34","author":"Strubell","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"B106","first-page":"325","article-title":"\u201cGarbage in, garbage out? Do machine learning application papers in social computing report where human-labeled training data comes from?\u201d","volume-title":"FAT","author":"Stuart Geiger","year":"2020"},{"key":"B107","first-page":"1","article-title":"\u201cWho am I? Personality detection based on deep learning for texts,\u201d","volume-title":"IEEE International Conference on Communications","author":"Sun","year":"2018"},{"key":"B108","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.emj.2012.12.004","article-title":"Personality type and work-related outcomes: an exploratory application of the Enneagram model","volume":"31","author":"Sutton","year":"2013","journal-title":"Eur. Manag. J."},{"key":"B109","doi-asserted-by":"publisher","first-page":"61959","DOI":"10.1109\/ACCESS.2018.2876502","article-title":"Personality predictions based on user behavior on the Facebook social media platform","volume":"6","author":"Tadesse","year":"2018","journal-title":"IEEE Access"},{"key":"B110","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/philosophies7010004","article-title":"The AI carbon footprint and responsibilities of AI scientists","volume":"7","author":"Tamburrini","year":"2022","journal-title":"Philosophies"},{"key":"B111","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.procs.2017.10.016","article-title":"Personality prediction system from Facebook users","volume":"116","author":"Tandera","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"B112","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1108\/ITP-04-2016-0076","article-title":"How to keep brand fan page followers? The lens of person-environment fit theory. Inf","volume":"31","author":"Tang","year":"2018","journal-title":"Technol. People"},{"key":"B113","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/0261927X09351676","article-title":"The psychological meaning of words: liwc and computerized text analysis methods","volume":"29","author":"Tausczik","year":"2010","journal-title":"J. Lang. Soc. Psychol."},{"key":"B114","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1609\/icwsm.v17i1.22192","article-title":"Top-down influence? Predicting CEO personality and risk impact from speech transcripts","volume":"17","author":"Theil","year":"2023","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"B115","unstructured":"LaMDA: language models for dialog applications\n            ThoppilanR.\n            De FreitasD.\n            HallJ.\n            ShazeerN.\n            KulshreshthaA.\n            ChengH.-T.\n          arXiv2022"},{"key":"B116","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1108\/ITP-03-2021-0212","article-title":"Personality in information systems professions: identifying archetypal professions with suitable traits and candidates' ability to fake-good these traits","volume":"35","author":"Tomat","year":"2021","journal-title":"Inf. Technol. People"},{"key":"B117","unstructured":"LLaMA: open and efficient foundation language models\n            TouvronH.\n            LavrilT.\n            IzacardG.\n            MartinetX.\n            LachauxM.-A.\n            LacroixT.\n          38687616arXiv2023"},{"key":"B118","unstructured":"2023"},{"key":"B119","first-page":"825","article-title":"\u201cClustering based personality prediction on Turkish tweets,\u201d","volume-title":"Proc. 2019 IEEE\/ACM Int. Conf. Adv. Soc. Networks Anal. Mining, ASONAM 2019","author":"Tutaysalgir","year":"2019"},{"key":"B120","doi-asserted-by":"publisher","first-page":"5999","DOI":"10.48550\/arXiv.1706.03762","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B121","first-page":"1567","article-title":"\u201cInferring perceived demographics from user emotional tone and user-environment emotional contrast,\u201d","volume-title":"54th Annu. Meet. Assoc. Comput. Linguist. ACL 2016 - Long Pap. 3","author":"Volkova","year":"2016"},{"key":"B122","first-page":"36","article-title":"\u201cMining user interests to predict perceived psycho-demographic traits on twitter,\u201d","volume-title":"Proc. - 2016 IEEE 2nd Int. Conf. Big Data Comput. Serv. Appl. BigDataService 2016","author":"Volkova","year":"2016"},{"key":"B123","doi-asserted-by":"crossref","first-page":"357","DOI":"10.18653\/v1\/2020.coling-main.32","article-title":"\u201cTowards privacy by design in learner corpora research: a case of on-the-fly pseudonymization of Swedish learner essays,\u201d","volume-title":"Proceedings of the 28th International Conference on Computational Linguistics","author":"Volodina","year":"2020"},{"key":"B124","doi-asserted-by":"publisher","first-page":"196197","DOI":"10.1109\/ACCESS.2020.3034343","article-title":"Transfer learning with adaptive fine-tuning","volume":"8","author":"Vrban\u010di\u010d","year":"2020","journal-title":"IEEE Access"},{"key":"B125","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.17507\/tpls.1109.09","article-title":"The application of nltk library for python natural language processing in corpus research","volume":"11","author":"Wang","year":"2021","journal-title":"Theory Pract. Lang. Stud."},{"key":"B126","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1108\/09593840010377626","article-title":"Investigating traits of top performing software developers","volume":"13","author":"Wynekoop","year":"2000","journal-title":"Inf. Technol. People"},{"key":"B127","doi-asserted-by":"publisher","first-page":"4232","DOI":"10.1007\/s10489-018-1212-4","article-title":"Deep learning-based personality recognition from text posts of online social networks","volume":"48","author":"Xue","year":"2018","journal-title":"Appl. Intell."},{"key":"B128","doi-asserted-by":"publisher","first-page":"14221","DOI":"10.1609\/aaai.v35i16.17673","article-title":"Multi-document transformer for personality detection","volume":"35","author":"Yang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"B129","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.knosys.2018.11.025","article-title":"Mining personality traits from social messages for game recommender systems","volume":"165","author":"Yang","year":"2019","journal-title":"Knowledge-Based Syst."},{"key":"B130","unstructured":"FinBERT: a pretrained language model for financial communications\n            YangY.\n            ChristopherM.\n            UyS.\n            HuangA.\n          arXiv2020"},{"key":"B131","first-page":"32","article-title":"XLNet: generalized autoregressive pretraining for language understanding","volume":"165","author":"Yang","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"B132","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.jrp.2010.04.001","article-title":"Personality in 100,000 words: a large-scale analysis of personality and word use among bloggers","volume":"44","author":"Yarkoni","year":"2010","journal-title":"J. Res. Pers."},{"key":"B133","doi-asserted-by":"publisher","first-page":"134540","DOI":"10.1016\/j.scitotenv.2019.134540","article-title":"Mapping human's digital footprints on the Tibetan Plateau from multi-source geospatial big data","volume":"711","author":"Yi","year":"2020","journal-title":"Sci. Total Environ."},{"key":"B134","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1073\/pnas.1418680112","article-title":"Computer-based personality judgments are more accurate than those made by humans","volume":"112","author":"Youyou","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"B135","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1109\/ICAwST.2017.8256484","article-title":"Deep learning based personality recognition from Facebook status updates","volume-title":"2017 IEEE 8th international conference on awareness science and technology (iCAST)","author":"Yu","year":"2017"},{"key":"B136","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1007\/s42001-023-00224-9","article-title":"Transfer learning for hate speech detection in social media","volume":"6","author":"Yuan","year":"2023","journal-title":"J. Comput. Soc. Sci."},{"key":"B137","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1080\/02650487.2019.1575106","article-title":"Are we who we follow? Computationally analyzing human personality and brand following on Twitter","volume":"38","author":"Yun","year":"2019","journal-title":"Int. J. Advert."},{"key":"B138","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s11704-018-8052-6","article-title":"A survey of autoencoder-based recommender systems","volume":"14","author":"Zhang","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"B139","unstructured":"FEEL: a framework for evaluating emotional support capability with large language models\n            ZhangH.\n            ChenY.\n            WangM.\n            FengS.\n          arXiv2024"},{"key":"B140","doi-asserted-by":"publisher","first-page":"1","DOI":"10.36227\/techrxiv.21781109.v2","article-title":"Automated measures of sentiment via transformer- and lexicon-based sentiment analysis (TLSA)","volume":"7","author":"Zhao","year":"2023","journal-title":"J. Comput. Soc. Sci."},{"key":"B141","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1145\/3195106.3195124","article-title":"\u201cA general personality prediction framework based on Facebook profiles,\u201d","volume-title":"Proceedings of the 2018 10th International Conference on Machine Learning and Computing","author":"Zhong","year":"2018"},{"key":"B142","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1037\/pas0000625","article-title":"Integrating structure and dynamics in personality assessment: first steps toward the development and validation of a personality dynamics diary","volume":"31","author":"Zimmermann","year":"2019","journal-title":"Psychol. Assess."}],"container-title":["Frontiers in Big Data"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1387325\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:25:17Z","timestamp":1726201517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1387325\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":142,"alternative-id":["10.3389\/fdata.2024.1387325"],"URL":"https:\/\/doi.org\/10.3389\/fdata.2024.1387325","relation":{},"ISSN":["2624-909X"],"issn-type":[{"value":"2624-909X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]},"article-number":"1387325"}}