Browse > Article
http://dx.doi.org/10.22937/IJCSNS.2022.22.11.39

A Deep Learning Model for Predicting User Personality Using Social Media Profile Images  

Kanchana, T.S. (Department of Computer Science, Bharathidasan University, Khaja Mali Campus)
Zoraida, B.S.E. (Department of Computer Science, Bharathidasan University, Khaja Mali Campus)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.11, 2022 , pp. 265-271 More about this Journal
Abstract
Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.
Keywords
Personality Traits; Classification; Deep Learning; Convolution Neural Networks; Five Factor Model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 J. Schmidhuber, ''Deep learning in neural networks: An overview,'' Neural Network., vol. 61, pp. 85-117, Jan. 2015.   DOI
2 P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. (2013). ''OverFeat: Integrated recognition, localization and detection using convolutional networks.'' [Online]. Available: https://arxiv.org/abs/1312.6229
3 G. Hinton et al., ''Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,'' IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82-97, Nov. 2012   DOI
4 T. Mikolov, A. Deoras, D. Povey, L. Burget, and J. Cernocky, ''Strategies for training large scale neural network language models,'' in Proc. IEEE Workshop Autom. Speech Recognit. Understand., Dec. 2011, pp. 196-201.
5 M. Cao and Z. Wan, ''Psychological counseling and character analysis algorithm based on image emotion,'' IEEE Access, early access, Aug. 28, 2020, doi: 10.1109/ACCESS.2020.3020236.   DOI
6 K. Kircaburun, S. Alhabash, S. B. Tosuntas, and M. D. Griffiths, ''Uses and gratifications of problematic social media use among university students: A simultaneous examination of the big five of personality traits, social media platforms, and social media use motives,'' Int. J. Mental Health Addiction, vol. 18, no. 3, pp. 525-547, Jun. 2020.   DOI
7 R. M. Warner and D. B. Sugarman, ''Attributions of personality based on physical appearance, speech, and handwriting.,'' J. Personality Social Psychol., vol. 50, no. 4, pp. 792-799, 1986.   DOI
8 G. Farnadi, G. Sitaraman, S. Sushmita, F. Celli, M. Kosinski, D. Stillwell, S. Davalos, M.-F. Moens, and M. De Cock, ''Computational personality recognition in social media,'' User Model. User-Adapted Interact., vol. 26, nos. 2-3, pp. 109-142, 2016.   DOI
9 Yuheng Hu, Lydia Manikonda and Subbarao Kambhampati, "What we instagram: A first analysis of instagram photo content and user types", ICWSM, 2014.
10 A. Krizhevsky, I. Sutskever, and G. E. Hinton ''ImageNet classification with deep convolutional neural networks,'' in Proc. Int. Conf. Neural Inf.Process. Syst., 2012, pp. 1097-1105.
11 R. R. McCrae and O. P. John, ''An introduction to the fivefactor model and its applications,'' J. Personality, vol. 60, no. 2, pp. 175-215, 1992.   DOI
12 A. Souri, S. Hosseinpour, and A. M. Rahmani, ''Personality classification based on profiles of social etworks' users and the five-factor model of per- sonality,'' Hum. -Centric Comput. Inf. Sci., vol. 8, no. 1, p. 24, Dec. 2018.   DOI
13 C. Xu, S. Cetintas, K.-C. Lee, and L.-J. Li, ''Visual sentiment prediction with deep convolutional neural networks,'' 2014, arXiv:1411.5731. [Online]. Available: http://arxiv.org/abs/1411.5731
14 S. Nestler, B. Egloff, A. C. P. Kufner, and M. D. Back, ''An integrative lens model approach to bias and accuracy in human inferences: Hindsight effects and knowledge updating in personality judgments.,'' J. Personality Social Psychol., vol. 103, no. 4, p. 689, 2012.   DOI
15 M. Shevlin, S. Walker, M. N. O. Davies, P. Banyard, and C. A. Lewis, ''Can you judge a book by its cover? Evidence of self-stranger agreement on personality at zero acquaintance,'' Personality Individual Differences, vol. 35, no. 6, pp. 1373-1383, Oct. 2003.   DOI
16 Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, ''Gradientbased learning applied to document recognition,'' Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
17 Y. LeCun, Y. Bengio, and G. Hinton, ''Deep learning,'' Nature, vol. 521, pp. 436-444, May 2015.   DOI
18 P. Sermanet, S. Chintala, and Y. LeCun, ''Convolutional neural networks applied to house numbers digit classification,'' 2012, arXiv:1204.3968. [Online]. Available: http://arxiv.org/abs/1204.3968
19 L. A. Zebrowitz, J. A. Hall, N. A. Murphy, and G. Rhodes, ''Looking smart and looking good: Facial cues to intelligence and their origins,'' Personality Social Psychol. Bull., vol. 28, no. 2, pp. 238-249, Feb. 2002.   DOI
20 I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016. [Online]. Available: http://goodfeli.github.io/dlbook/%0Ahttp://dx.doi.org/10.1038/nature14539