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Group-level personality detection based on text generated networks

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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.

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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

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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.

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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

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  1. Xiangguo Sun
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This article belongs to the Topical Collection: Computational Social Science as the Ultimate Web Intelligence

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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

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