Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Group-level personality detection based on text generated networks

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Personality analysis has been widely used in various social services such as mental healthcare, recommendation systems and so on because its natural explainability for AI applications in Web intelligence. With the penetration of Web2.0, traditional social researches have gradually turned to online social networks. However, for a long time, personality detection from online social texts has sunk into an embarrassing situation for the lack of large labeled datasets. Limited by supervised learning frameworks and small labeled datasets, prior works mainly detect one’s personality in the individual perspective, which may not well meet the challenges of massive un-labeled data in the near future. In this paper, we present a first look into group-level personality detection and we use an unsupervised feature learning method instead of supervised methods used in most related works. We propose AdaWalk, a new and novel model of group-level personality detection by learning the influence from text generated networks. The model uses different kernels to evaluate how much a given node should decide its walk path locally or globally. The advantage of AdaWalk is three-folded: a) the model is an unsupervised feature learning method, which means it relies less on annotations. b) by traversing the network, we can capture the influence in the group level, thus the analysis of one’s personality is not only based on individual records but also the information in groups. Therefore, AdaWalk can leverage small datasets more comprehensively. c) AdaWalk is scalable and can be easily transformed as distributed algorithms, which means it has more potential, compared with existing personality detection methods, to meet the massive data without annotations. We use AdaWalk to predict users’ Big Five personality scores in FIVE heterogeneous personality datasets. Compared with more than TEN famous related methods, AdaWalk outperforms the others, meanwhile verifying the significance of the group perspective and unsupervised feature learning methods in the application of personality analysis. To make our experiment repeatable, AdaWalk and related datasets are available at https://xiangguosun.strikingly.com.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

Notes

  1. https://www.ocf.berkeley.edu/~johnlab/bfi.htm

  2. A taxonomy for personality traits. It describe one’s personality from five factors: Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism (or Stability).

  3. The source code can be accessed only for the academic purpose. https://xiangguosun.strikingly.com

  4. An open-source toolkit for Network Embedding. https://github.com/thunlp/OpenNE

  5. https://openpsychometrics.org

References

  1. Adelstein, J.S., Shehzad, Z., Mennes, M., DeYoung, C.G, Zuo, X.-N., Kelly, C., Margulies, D.S., Bloomfield, A., Gray, J.R., Castellanos, F.X., et al.: Personality is reflected in the brain’s intrinsic functional architecture. PloS one 6 (11), e27633 (2011)

    Article  Google Scholar 

  2. Ahmed, A., Shervashidze, N., Narayanamurthy, S., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 37–48. ACM (2013)

  3. Amichai-Hamburger, Y., Vinitzky, G.: Social network use and personality. Comput. Hum. Behav. 26(6), 1289–1295 (2010)

    Article  Google Scholar 

  4. Andrew Schwartz, H., Eichstaedt, J.C., Kern, M.L, Dziurzynski, L., Ramones, S.M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M.E.P., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PloS one 8(9), e73791 (2013)

    Article  Google Scholar 

  5. Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., Stillwell, D.: Personality and patterns of facebook usage. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 24–32. ACM (2012)

  6. Bai, S., Gao, R., Zhu, T.: Determining personality traits from renren status usage behavior. In: Computational Visual Media, pp. 226–233. Springer (2012)

  7. Barrick, M.R., Mount, M.K.: The big five personality dimensions and job performance: a meta-analysis. Person. Psychol. 44(1), 1–26 (1991)

    Article  Google Scholar 

  8. Benet-Martinez, V., John, O.P: Los cinco grandes across cultures and ethnic groups: multitrait-multimethod analyses of the big five in Spanish and English. J. Person. Soc. Psychol. 75(3), 729 (1998)

    Article  Google Scholar 

  9. Biel, J.-I., Tsiminaki, V., Dines, J., Gatica-Perez, D.: Hi youtube!: personality impressions and verbal content in social video. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 119–126. ACM (2013)

  10. Cavallari, S., Zheng, V.W., Cai, H., Chang, K.C.-C., Cambria, E.: Learning community embedding with community detection and node embedding on graphs. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 377–386. ACM (2017)

  11. Celli, F., Pianesi, F., Stillwell, D., Kosinski, M.: Workshop on computational personality recognition (shared task). In: Proceedings of the Workshop on Computational Personality Recognition (2013)

  12. Chen, C-M, Chien, P-C, Lin, Y-C, Tsai, M-F, Yang, Y-H: Exploiting latent social listening representations for music recommendations. In: Proceedings of Ninth ACM International Conf. Recommender Syst. Poster (2015)

  13. Costa, P.T., McCrae, R.R.: The revised neo personality inventory (neo-pi-r). The SAGE Handb. Person. Theory Assess. 2, 179–198 (2008)

    Google Scholar 

  14. Deyoung, C.G.: Toward a theory of the big five. Psychol. Inq. 21(1), 26–33 (2010)

    Article  Google Scholar 

  15. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)

  16. Guntuku, S.C., Zhou, J.T., Roy, S., Lin, W., Tsang, I.W.: ‘Who likes what and, why?’ insights into modeling users’ personality based on image ‘likes’. IEEE Trans. Affect. Comput. 9, 130–143 (2018)

    Article  Google Scholar 

  17. John, O.P., Donahue, E.M., Kentle, R.L.: The big five inventory: versions 4a and 54, Institute of personality and social research. University of California, Berkeley (1991)

  18. John, O.P., Naumann, L.P., Soto, C.J.: Paradigm shift to the integrative big five trait taxonomy. Handb. Person Theory Res. 3, 114–158 (2008)

    Google Scholar 

  19. Kampman, O., Barezi, E.J., Bertero, D., Fung, P.: Investigating audio, video, and text fusion methods for end-to-end automatic personality prediction. In: ACL (2), pp. 606–611. Association for Computational Linguistics (2018)

  20. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110(15), 5802–5805 (2013)

    Article  Google Scholar 

  21. Lambiotte, R., Kosinski, M.: Tracking the digital footprints of personality. Proc. IEEE 102(12), 1934–1939 (2014)

    Article  Google Scholar 

  22. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

  23. Li, A., Zhang, F., Zhu, T.: Web use behaviors for identifying mental health status. In: International Conference on Brain and Health Informatics, pp. 348–358. Springer (2013)

  24. Liu, X., Zhu, T.: Deep learning for constructing microblog behavior representation to identify social media user’s personality. PeerJ Comput. Sci. e81, 2 (2016)

    Google Scholar 

  25. Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. 30, 457–500 (2007)

    Article  Google Scholar 

  26. Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)

    Article  Google Scholar 

  27. McCallum, A.K, Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retr. 3(2), 127–163 (2000)

    Article  Google Scholar 

  28. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

  29. Nov, O., Ye, C.: Personality and technology acceptance: personal innovativeness in it, openness and resistance to change. In: Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, pp. 448–448. IEEE (2008)

  30. Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114. ACM (2016)

  31. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the Web. Technical report, Stanford InfoLab (1999)

  32. Park, G., Andrew Schwartz, H., Eichstaedt, J.C, Kern, M.L., Kosinski, M., Stillwell, D., Ungar, L.H., Seligman, M.E.P.: Automatic personality assessment through social media language. J. Pers. Soc. Psychol. 108(6), 934 (2015)

    Article  Google Scholar 

  33. Pennebaker, J.W., King, L.A.: Linguistic styles: language use as an individual difference. J. Person. Soc. Psychol. 77(6), 1296 (1999)

    Article  Google Scholar 

  34. Pennebaker, J.W, Francis, M.E, Booth, R.J: Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Assoc. 71(2001), 2001 (2001)

    Google Scholar 

  35. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)

  36. Qian, Q., Huang, M., Zhao, H., Xu, J., Zhu, X.: Assigning personality/profile to a chatting machine for coherent conversation generation. In: IJCAI, pp. 4279–4285 (2018)

  37. Qiu, L., Lin, H., Ramsay, J., Yang, F.: You are what you tweet: personality expression and perception on twitter. J. Res. Pers. 46(6), 710–718 (2012)

    Article  Google Scholar 

  38. Qiu, L., Lu, J., Ramsay, J., Yang, S., Qu, W., Zhu, T.: Personality expression in Chinese language use. International Journal of Psychology (2016)

  39. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J.W., Christakis, N.A., Couzin, I.D., Jackson, M.O., et al.: Machine behaviour. Nature 568(7753), 477 (2019)

    Article  Google Scholar 

  40. Rai, T.S.: High replicability in personality psychology. Science 364(6438), 348–348 (2019)

    Google Scholar 

  41. Rajaraman, A., Ullman, J.D.: Mining of massive datasets. Cambridge University Press, Cambridge (2011)

  42. Rangel, F., Celli, F., Rosso, P., Potthast, M., Stein, B., Walter, D.: Overview of the 3rd author profiling task at PAN 2015. In: Cappellato, L., Ferro, N., Jones, G., San Juan, E. (eds.) CLEF Evaluation Labs and Workshop – Working Notes Papers, 8-11 September, Toulouse, France. CEUR-WS.org (2015)

  43. Selfhout, M., Burk, W., Branje, S., Denissen, J., Van Aken, M., Meeus, W.: Emerging late adolescent friendship networks and big five personality traits: a social network approach. J. Person. 78(2), 509–538 (2010)

    Article  Google Scholar 

  44. Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)

    Article  Google Scholar 

  45. Sun, G., Shen, J.: Facilitating social collaboration in mobile cloud-based learning: a teamwork as a service (taas) approach. IEEE Trans. Learn. Technol. 7(3), 207–220 (2014)

    Article  Google Scholar 

  46. Sun, X., Liu, B., Cao, J., Luo, J., Shen, X.: Who am I? personality detection based on deep learning for texts. In: ICC, pp. 1–6. IEEE (2018)

  47. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)

  48. Wang, H., Zhang, F., Hou, M., Xie, X., Guo, M., Qi, L.: Shine: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 592–600. ACM (2018)

  49. Wei, H., Zhang, F., Yuan, N.J., Cao, C., Fu, H., Xie, X., Rui, Y., Ma, W.-Y.: Beyond the words predicting user personality from heterogeneous information. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 305–314. ACM (2017)

  50. Wu, Y., Kosinski, M., Stillwell, D.: Computer-based personality judgments are more accurate than those made by humans. Proc. Natl. Acad. Sci. 112(4), 1036–1040 (2015)

    Article  Google Scholar 

  51. Zafarani, R., Liu, H.: Social computing data repository at asu (2009)

  52. Zhao, S., Ding, G., Han, J., Gao, Y.: Personality-aware personalized emotion recognition from physiological signals. In: IJCAI, pp. 1660–1667 (2018)

  53. Zheng, S., Qi, M., Wang, T., Chen, W., Yu, N., Ma, Z-M, Liu, T-Y: Asynchronous stochastic gradient descent with delay compensation. In: International Conference on Machine Learning, pp. 4120–4129 (2017)

  54. Zibrek, K., Kokkinara, E., McDonnell, R.: The effect of realistic appearance of virtual characters in immersive environments - does the character’s personality play a role?. IEEE Trans. Vis. Comput. Graph. 24(4), 1681–1690 (2018)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by National Key R&D Program of China 2017YFB1003000, National Natural Science Foundation of China under Grants No. 61972087, No. 61772133, No. 61472081, No. 61402104. Jiangsu Provincial Key Project BE2018706. Key Laboratory of Computer Network Technology of Jiangsu Province. Jiangsu Provincial Key Laboratory of Network and Information Security under Grants No. BM2003201, and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No. 93K-9.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast Univerisity, NanJing, 211189, China

    Xiangguo Sun, Bo Liu, Qing Meng & Junzhou Luo

  2. School of Cyber Science and Engineering, Southeast Univerisity, NanJing, 211189, China

    Jiuxin Cao

  3. School of Information Technology and Electrical Engineering, The University of Queensland, Queensland, Australia

    Hongzhi Yin

Authors
  1. Xiangguo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qing Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiuxin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Junzhou Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongzhi Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangguo Sun.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Computational Social Science as the Ultimate Web Intelligence

Guest Editors: Xiaohui Tao, Juan D. Velasquez, Jiming Liu, and Ning Zhong

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, X., Liu, B., Meng, Q. et al. Group-level personality detection based on text generated networks. World Wide Web 23, 1887–1906 (2020). https://doi.org/10.1007/s11280-019-00729-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-019-00729-2

Keywords

Search

Navigation