面向社交媒体数据的人格识别研究进展

doi:10.3778/j.issn.1673-9418.2212012

摘要/Abstract

摘要： 人格是一种与人类的想法、情绪、行为相关的稳定模式，任何涉及对人类行为进行理解、分析、预测的技术都有可能受益于人格识别。准确识别人格将有助于人机交互、推荐系统、网络空间安全等研究。社交媒体为人格识别研究提供了高质量的数据源，而自陈量表、投射测验等经典人格测量方法已无法匹配大数据时代的社交媒体数据，且主流的基于机器学习训练的人格识别模型仍有很大性能提升空间。故此，梳理了当前面向社交媒体数据的人格识别的研究，介绍了人格识别的背景知识，按照人格识别模型输入数据的类型分别综述研究现状并系统地总结了经典文献，具体类型为基于社交文本数据、基于社交图像数据、基于社交应用统计数据以及基于多模态数据。最后，提出面向社交媒体数据的人格识别研究的七个未来研究方向。

关键词: 人格识别, 社交媒体, 大五人格, 迈尔斯布里格斯类型指标

Abstract: Personality is a stable construction, which is associated with thoughts, emotions and behaviors of human. Any technology involved in understanding, analyzing and predicting human behaviors may benefit from personality recognition. Accurately recognizing the personality will contribute to the research of human-computer interaction, recommendation system, cyberspace security, etc. Social media provides high-quality data for personality recog-nition, while classic personality measurement methods such as self-report scales and projective tests can no longer match the social media data in the age of big data. Moreover, the current mainstream personality recognition methods based on machine learning still have lots of room for performance improvement. Therefore, this paper investigates the literature on personality recognition based on social media data, introduces the background knowledge of personality recognition and summarizes the research status according to the data types of recognition model input used, specifically based on social text data, social image data, social application data and multimodal data. Finally, this paper proposes seven future research directions of personality recognition based on social media data.

Key words: personality recognition, social media, Big Five model, Myers-Briggs type indicator

林浩, 王春东, 孙永杰. 面向社交媒体数据的人格识别研究进展[J]. 计算机科学与探索, 2023, 17(5): 1002-1016.

LIN Hao, WANG Chundong, SUN Yongjie. Survey on Personality Recognition Based on Social Media Data[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(5): 1002-1016.

参考文献

[1] SHUMANOV M, JOHNSON L. Making conversations with chatbots more personalized[J]. Computers in Human Behavior, 2021, 117: 106627.
[2] AGUIAR J J B, FECHINE J M, COSTA E B. Collaborative filtering strategy for product recommendation using per-sonality characteristics of customers[C]//Proceedings of the 2020 Brazilian Symposium on Multimedia and the Web, S?o Luís, Nov 30-Dec 4, 2020. Stroudsburg: ACL, 2020: 157-164.
[3] PENG X Z, CUI F, CUI C, et al. Factors influencing rumor transmission: characteristics of circumstances, contents, trans-mitters and recipients[J]. Journal of Psychological Science, 2018, 41(4): 916-921.
[4] AMITABHA A, AMAN A, SUJAY S, et al. Impact of COVID-19 on the human personality: an analysis based on document modeling using machine learning tools[J]. The Computer Journal, 2023, 66(4): 963-969.
[5] MAJALUOMA S, SEPPALA T, KAUTIAINEN H, et al. Type D personality and metabolic syndrome among Finnish female municipal workers[J]. BMC Womens Health, 2020, 20(1): 202-209.
[6] HOLLENBAUGH E E, FERRIS A L. Facebook self-disclosure: examining the role of traits, social cohesion, and motives[J]. Computers in Human Behavior, 2014, 30: 50-58.
[7] 杨义先, 钮心忻. 黑客心理学-社会工程学原理[M]. 北京: 电子工业出版社, 2019.
YANG Y X, NIU X X. Hacker psychology-principles of social engineering[M]. Beijing: Publishing House of Electronics Industry, 2019.
[8] 郑日昌, 蔡永红, 周益群. 心理测量学[M]. 北京: 人民教育出版社, 1999: 155-170.
ZHENG R C, CAI Y H, ZHOU Y Q. Psychometrics[M]. Beijing: People??s Education Press, 1999: 155-170.
[9] MORGAN C D, MURRAY H A. A method for investigating fantasies: the thematic apperception test[J]. Archives of Neurology & Psychiatry, 1935, 34(2): 289-306.
[10] 蔡莉, 王淑婷, 刘俊晖, 等. 数据标注研究综述[J]. 软件学报, 2020, 31(2): 302-320.
CAI L, WANG S T, LIU J H, et al. Survey of data annotation[J]. Journal of Software, 2020, 31(2): 302-320.
[11] 朱廷劭, 汪静莹, 赵楠, 等. 论大数据时代的心理学研究变革[J]. 新疆师范大学学报(哲学社会科学版), 2015, 36(4): 100-107.
ZHU T S, WANG J Y, ZHAO N, et al. Reform on psychological research in big data age[J]. Journal of Xinjiang Normal University (Philosophy and Social Sciences), 2015, 36(4): 100-107.
[12] 张磊, 陈贞翔, 杨波. 社交网络用户的人格分析与预测[J]. 计算机学报, 2014, 37(8): 1877-1894.
ZHANG L, CHEN Z X, YANG B. Personality analysis and prediction of social network users[J]. Chinese Journal of Computers, 2014, 37(8): 1877-1894.
[13] 费定舟, 赵雅婷. 社交媒体中的人格计算研究综述[J]. 计算机工程与应用, 2019, 55(20): 34-42.
FEI D Z, ZHAO Y T. Survey of personality computing on social media[J]. Computer Engineering and Applications, 2019, 55(20): 34-42.
[14] 吴桐, 郑康锋, 伍淳华, 等. 网络空间安全中的人格研究综述[J]. 电子与信息学报, 2020, 42(12): 2827-2840.
WU T, ZHENG K F, WU C H, et al. A survey on personality in cyberspace security[J]. Journal of Electronics & Information Technology, 2020, 42(12): 2827-2840.
[15] GOLDBERG L R, JOHNSON J A, EBER H W, et al. The international personality item pool and the future of public-domain personality Measures[J]. Journal of Research in Personality, 2006, 40(1): 84-96.
[16] 顾雪英, 胡湜. MBTI人格类型量表:新近发展及应用[J]. 心理科学进展, 2012, 20(10): 1700-1708.
GU X Y, HU S. MBTI: new development and application[J]. Advances in Psychological Science, 2012, 20(10): 1700-1708.
[17] VINCIARELLI A, MOHAMMADI G. A survey of personality computing[J]. IEEE Transactions on Affective Computing, 2014, 5(3): 273-291.
[18] ARGAMON S, KOPPEL D S M, PENNEBAKER J. Lexical predictors of personality type[C]//Proceedings of the 2005 Conference of the Classification Society of North America, City of Saint Louis, Jan 1, 2005: 1-16.
[19] MAIRESSE F, WALKER M, MEHL M, et al. Using linguistic cues for the automatic recognition of personality in conversation and text[J]. Journal of Artificial Intelligence Research, 2007, 30: 457-500.
[20] NGUYEN T, PHUNG D, ADAMS B, et al. Towards discovery of influence and personality traits through social link prediction[C]//Proceedings of the 5th International Conference on Weblogs and Social Media, Barcelona, Jul 17-21, 2011. Menlo Park: AAAI, 2011: 566-569.
[21] PORIA S, GELBUKH A, AGARWAL B, et al. Common sense knowledge based personality recognition from text[C]//LNCS 8266: Proceedings of the 12th Mexican International Conference on Artificial Intelligence, Mexico City,Nov 24-30, 2013. Berlin, Heidelberg: Springer, 2013: 484-496.
[22] CELLI F. Unsupervised personality recognition for social network sites[C]//Proceedings of the 6th International Conference on Digital Society, Valencia, Jan 30-Feb 4, 2012. Amsterdam: International Academy, Research, and Industry Association, 2012: 59-62.
[23] AMIRHOSSEINI M H, KAZEMIAN H. Machine learning approach to personality type prediction based on the Myers-Briggs type indicator[J]. Multimodal Technologies and Interaction, 2020, 4(1): 9.
[24] CHOONG E J, VARATHAN K D. Predicting judging perceiving of Myers-Briggs type indicator (MBTI) in online social forum[J]. PeerJ, 2021, 9: e11382.
[25] TAO Y, YANG F F, OUYANG H L, et al. Psycholinguistic tripartite graph network for personality detection[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Aug 2-4, 2021. Stroudsburg: ACL, 2021: 4229-4239.
[26] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv:1301.3781, 2013.
[27] PENNINGTON J, SOCHER R, MANNING C D. GloVe: global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing , Doha, Oct 25-29, 2014. Stroudsburg: ACL, 2014: 1532-1543.
[28] JOULIN A, GRAVE E, BOJANOWSKI P, et al. Bag of tricks for efficient text classification[J]. arXiv:1607.01759, 2016.
[29] FLORIDI L, CHIRIATTI M. GPT-3: its nature, scope, limits, and consequences[J]. Minds and Machines, 2020, 30(4): 681-694.
[30] DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Jun 2-7, 2019. Stroudsburg: ACL, 2019: 4171-4186.
[31] LIU Y, OTT M, GOYAL N, et al. RoBERTa: a robustly optimized BERT pretraining approach[J]. arXiv:1907.11692, 2019.
[32] YANG Z L, DAI Z H, YANG Y M, et al. XLNet: generalized autoregressive pretraining for language understanding[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, Dec 8-14, 2019. Cambridge: MIT Press, 2019: 5753-5763.
[33] CERKEZ N, VRDOLJAK B, SKANSI S. A method for MBTI classification based on impact of class components[J]. IEEE Access, 2021, 9: 146550-146567.
[34] DARLIANSYAH A. SENTIPEDE: a smart system for sentiment-based personality detection from short texts[J]. Journal of Universal Computer Science, 2019, 25: 1323-1352.
[35] INDIRA R, MAHARANI W. Personality detection on social media Twitter using long short-term memory with Word-2Vec[C]//Proceedings of the 2021 IEEE International Conference on Communication, Networks and Satellite, Purwokerto, Jul 17-18, 2021. Piscataway: IEEE, 2021: 64-69.
[36] MEHTA Y, FATEHI S, KAZAMEINI A, et al. Bottom-up and top-down: predicting personality with psycholinguistic and language model features[C]//Proceedings of the 2020 IEEE International Conference on Data Mining, Sorrento, Nov 17-20, 2020. Piscataway: IEEE, 2020: 1184-1189.
[37] WANG Y, ZHENG J, LI Q, et al. XLNet-Caps: personality classification from textual posts[J]. Electronics, 2021, 10(11): 1360.
[38] JIANG H, ZHANG X Z, CHOI D J. Automatic text-based personality recognition on monologues and multiparty dialogues using attentive networks and contextual embeddings[C]//Proceedings of the 2020 AAAI Conference on Artificial Intelligence, New York, Feb 7-12, 2020. Menlo Park:AAAI, 2020: 13821-13822.
[39] EL-DEMERDASH K, EL-KHORIBI R A, ISMAIL S M, et al. Deep learning based fusion strategies for personality prediction[J]. Egyptian Informatics Journal, 2021, 23(1): 47-53.
[40] LOPEZ F O, OROZCO J R. Automatic personality evaluation from transliterations of YouTube Vlogs using classical and state-of-the-art word embedding[J]. Ingenieria e Investigación, 2022, 42(2): e93803.
[41] VáSQUEZ R L, LUNA J O. Transformer-based approaches for personality detection using the MBTI model[C]//Proceedings of the 2021 XLVII Latin American Computing Conference, Cartago, Oct 25-29, 2021. Piscataway: IEEE, 2021: 1-7.
[42] 张晗, 贾甜远, 骆方, 等. 面向网络文本的BERT心理特质预测研究[J]. 计算机科学与探索, 2021, 15(8): 1459-1468.
ZHANG H, JIA T Y, LUO F, et al. Study on predicting psychological traits of online text by BERT[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1459-1468.
[43] YUAN C, WU J, LI H, et al. Personality recognition based on user generated content[C]//Proceedings of the 15th International Conference on Service Systems and Service Management, Hangzhou, Jul 21-22, 2018. Piscataway: IEEE, 2018: 1-6.
[44] MAJUMDER N. Deep learning-based document modeling for personality detection from text[J]. IEEE Intelligent Systems, 2017, 32: 74-79.
[45] KAZAMEINI A, FATEHI S, MEHTA Y, et al. Personality trait detection using bagged SVM over BERT word embedding ensembles[C]//Proceedings of the ACL 2020 Workshop on Widening NLP, Washington, Jul 5, 2020. Stroudsburg: ACL, 2020: 1-4.
[46] REN Z, SHEN Q, DIAO X, et al. A sentiment-aware deep learning approach for personality detection from text[J]. Information Processing and Management, 2021, 58(3): 102532.
[47] PAVAN K K N, GAVRILOVA M L. Latent personality traits assessment from social network activity using contextual language embedding[J]. IEEE Transactions on Computational Social Systems, 2022, 9(2): 638-649.
[48] MOHAMMAD S M, TURNEY P D. Crowdsourcing a word-emotion association lexicon[J]. Computational Intelligence, 2013, 29(3): 436-465.
[49] MOHAMMAD S. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Jul 15-20, 2018. Stroudsburg: ACL, 2018: 174-184.
[50] CHATURVEDI I, SATAPATHY R, CAVALLARI S, et al. Fuzzy commonsense reasoning for multimodal sentiment analysis[J]. Pattern Recognition Letters, 2019, 125: 264-270.
[51] LIN H, WANG C D, HAO Q B. A novel personality detection method based on high-dimensional psycholinguistic feat-ures and improved distributed gray wolf optimizer for feature selection[J]. Information Processing & Management, 2023, 60(2): 103217.
[52] FITZGERALD S, EVANS D, GREEN R. Is your profile picture worth 1000 words? Photo characteristics associated with personality impression agreement[C]//Proceedings of the 2009 AAAI International Conference on Weblogs and Social Media, San Jose, May 17-20, 2009. Menlo Park: AAAI, 2009: 327-330.
[53] CELLI F，BRUNI E，LEPRI B. Automatic personality and interaction style recognition from facebook profile pictures[C]//Proceedings of the 2014 ACM International Conference on Multimedia, Orlando, Nov 3-7, 2014. New York: ACM, 2014: 1101-1104.
[54] TAREAF R B, ALHOSSEINI S A, MEINEL C. Facial-based personality prediction models for estimating individuals private traits[C]//Proceedings of the 2019 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking, Xiamen, Dec 16-18, 2019. Piscataway: IEEE, 2019: 1586-1594.
[55] SEGALIN C, CELLI F, POLONIO L, et al. What your Facebook profile picture reveals about your personality[C]//Proceedings of the 25th ACM Conference on Multimedia, Mountain View, Oct 23-27, 2017. New York: ACM, 2017: 460-468.
[56] JEREMY N H, CHRISTIAN G, KAMAL M F, et al. Automatic personality prediction using deep learning based on social media profile picture and posts[C]//Proceedings of the 4th International Seminar on Research of Information Technology and Intelligent Systems, Yogyakarta, Dec 16-17, 2021. Piscataway: IEEE, 2021: 166-172.
[57] FERWERDA B, TKALCIC M. Predicting users?? personality from instagram pictures: using visual and/or content features?[C]//Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, Singapore, Jul 8-11, 2018. New York: ACM, 2018: 157-161.
[58] CRISTANI M, VINCIARELLI A, SEGALIN C, et al. Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis[C]//Proceedings of the 2013 ACM International Conference on Multimedia, Barcelona, Oct 21-25, 2013. New York: ACM, 2013: 213-222.
[59]SEGALIN C, PERINA A, CRISTANI M, et al. The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits[J]. IEEE Transactions on Affective Computing, 2017, 8: 268-285.
[60] TORFASON R, AGUSTSSON E, ROTHE R, et al. From face images and attributes to attributes[C]//LNCS 10113:Proceedings of the 13th Asian Conference on Computer Vision, Taipei, China, Nov 20-24, 2016. Cham: Springer, 2016: 313-329.
[61] BISWAS K, SHIVAKUMARA P, PAL U, et al. Fuzzy and genetic algorithm based approach for classification of personality traits oriented social media images[J]. Knowledge-Based Systems, 2022, 241: 108024.
[62] GOLBECK J, ROBLES C, TURNER K. Predicting personality with social media[C]//Proceedings of the CHI’11 Extended Abstracts on Human Factors in Computing Systems, Vancouver, May 7-12, 2011. New York: ACM, 2011: 253-262.
[63] GOLBECK J, ROBLES C, EDMONDSON M, et al. Predicting personality from Twitter[C]//Proceedings of the 2011 IEEE 3rd International Conference on Social Computing, Boston, Oct 9-11, 2011. Washington: IEEE Computer Society, 2011: 149-156.
[64] CELLI F, BRUNO L. Is Big Five better than MBTI? A personality computing challenge using Twitter data[C]//Proceedings of the 5th Italian Conference on Computational Linguistics, Torino, Dec 10-12, 2018: 93-98.
[65] 郑敬华, 郭世泽, 高梁, 等. 基于多任务学习的大五人格预测[J]. 中国科学院大学学报, 2018, 35(4): 550-560.
ZHENG J H, GUO S Z, GAO L, et al. Microblog users?? Big-Five personality prediction based on multi-task learning[J]. Journal of University of Chinese Academy of Sciences, 2018, 35(4): 550-560.
[66] GJURKOVI? M, ?NAJDER J. Reddit: a gold mine for personality prediction[C]//Proceedings of the 2nd Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, New Orleans, Jun 12-13, 2018. Stroudsburg: ACL, 2018: 87-97.
[67] 魏华, 范翠英, 平凡, 等. 网络游戏动机的种类、影响及其作用机制[J]. 心理科学进展, 2011, 19(10): 1527-1533.
WEI H, FAN C Y, PING F, et al. Online games motivations: types, effects and mechanism[J]. Advances in Psychological Science, 2011, 19(10): 1527-1533.
[68] MEHTA Y, MAJUMDER N, GELBUKH A, et al. Recent trends in deep learning based personality detection[J]. Artificial Intelligence Review, 2020, 53: 2313-2339.
[69] SUN Z, SONG Q, ZHU X, et al. A novel ensemble method for classifying imbalanced data[J]. Pattern Recognition, 2015, 48(5): 1623-1637.
[70] VOLKOVA S, BACHRACH Y, ARMSTRONG M, et al. Inferring latent user properties from texts published in social media[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin Texas, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 4296-4297.
[71] WEI H H, ZHANG F Z, NICHOLAS J Y, et al. Beyond the words: predicting user personality from heterogeneous information[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, Feb 6-10, 2017. New York: ACM, 2017: 305-314.
[72] ONNO K, ELHAM J, BAREZI D B, et al. Investigating audio, video, and text fusion methods for end-to-end automatic personality prediction[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Jul 15-20, 2018. Stroudsburg: ACL, 2018: 606-611.
[73] HUANG Y, DU C Z, XUE Z H, et al. What makes multi-modal learning better than single (Provably)[C]//Proceedings of the 35th Conference on Neural Information Processing Systems, Dec 6-14, 2021. Cambridge: MIT Press, 2021: 10944-10956.
[74] YANG Y, YE H J, ZHAN D C, et al. Auxiliary information regularized machine for multiple modality feature learning[C]//Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Jul 25-31, 2015. Menlo Park: AAAI, 2015: 1033-1039.
[75] YANG Y, WU Y F, ZHAN D C, et al. Deep robust unsupervised multi-modal network[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 5652-5659.
[76] YANG Y, WANG K T, ZHAN D C, et al. Comprehensive semi-supervised multi-modal learning[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, Aug 10-16, 2019. Menlo Park: AAAI, 2019: 4092-4098.
[77] 潘嘉诚, 董一鸿, 陈华辉. 基于图神经网络的自闭症辅助诊断研究综述[J]. 计算机工程, 2022, 48(9): 1-11.
PAN J C, DONG Y H, CHEN H H. Review of research on auxiliary diagnosis of autism based on graph neural networks[J]. Computer Engineering, 2022, 48(9): 1-11.
[78] WEI J W, ZOU K. EDA: easy data augmentation techniques for boosting performance on text classification tasks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, China, Nov 3-7, 2019. Stroudsburg: ACL, 2019: 6381-6387.
[79] 王鑫鹏, 王晓强, 林浩, 等. 深度学习典型目标检测算法的改进综述[J]. 计算机工程与应用, 2022, 58(6): 42-57.
WANG X P, WANG X Q, LIN H, et al. Review on improvement of typical object detection algorithms in deep learning[J]. Computer Engineering and Applications, 2022, 58(6): 42-57.
[80] SANJA S, SEREN Y. Why is MBTI personality detection from texts a difficult task?[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, Apr 21-23, 2021. Stroudsburg: ACL, 2021: 3580-3589.
[81] 钱锡红. 员工性格差异分析及其对管理的启示[J]. 领导科学, 2020(20): 75-78.
QIAN X H. Analysis of employee personality differences and its implications for management[J]. Leadership Science, 2020(20): 75-78.
[82] 朱昭红, 孙令令. 阈上阈下启动刺激在不同锻炼人群中引发自动评价的特点[J]. 心理与行为研究, 2022, 20(2): 182-189.
ZHU Z H, SUN L L. Automatic evaluation of supraliminal and subliminally presentation of exercis-related stimuli in different exercise groups[J]. Studies of Psychology and Behavior, 2022, 20(2): 182-189.
[83] 王凌云, 王爱君, 齐宇欣, 等. 内隐联想测验中他人重要性对自我心理表征的影响[J]. 心理科学, 2019, 42(3): 633-638.
WANG L Y, WANG A J, QI Y X, et al. The mental representation of the self in the implicit association test: the role of the other[J]. Journal of Psychological Science, 2019, 42(3): 633-638.
[84] 温芳芳, 柯文琳, 佐斌, 等. 内隐关系评估程序(IRAP)：测量原理及应用[J]. 心理科学进展, 2021, 29(11): 1936-1947.
WEN F F, KE W L, ZUO B, et al. Implicit relational assessment procedure (IRAP): measuring principle and applications[J]. Advances in Psychological Science, 2021, 29(11): 1936-1947.
[85] 蔺泽浩, 李国趸, 曾祥极, 等. 基于跨媒体解纠缠表示学习的风格化图像描述生成[J]. 计算机学报, 2022, 45(12): 2510-2527.
LIN Z H, LI G D, ZENG X J, et al. A stylized image caption approach based on cross-media disentangled representation learning[J]. Chinese Journal of Computers, 2022, 45(12): 2510-2527.
[86] PINTAS J T, FERNANDES L A F, GARCIA A C B. Feature selection methods for text classification: a systematic literature review[J]. Artificial Intelligence Review, 2021, 54: 6149-6200.
[87] MCCRAE R R. Integrating trait and process approaches to personality: a sketch of an agenda[M]. Hoboken: John Wiley & Sons, 2016.
[88] SHAPPIE A T, DAWSON C A, DEBB S M. Personality as a predictor of cybersecurity behavior[J]. Psychology of Popular Media, 2020, 9(4): 475-480.
[89] SU K W, CHEN C J, SHUE L Y. Implication of cognitive style in designing computer-based procedure interface[J]. Human Factors and Ergonomics in Manufacturing & Service Industries, 2013, 23(3): 230-242.