NDLI: Beyond the Words: Predicting User Personality from Heterogeneous Information

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Beyond the Words: Predicting User Personality from Heterogeneous Information

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Beyond the Words: Predicting User Personality from Heterogeneous Information

Content Provider	ACM Digital Library
Author	Fu, Hao Ma, Wei-Ying Zhang, Fuzheng Xie, Xing Cao, Chuan Yuan, Nicholas Jing Wei, Honghao Rui, Yong
Abstract	An incisive understanding of user personality is not only essential to many scientific disciplines, but also has a profound business impact on practical applications such as digital marketing, personalized recommendation, mental diagnosis, and human resources management. Previous studies have demonstrated that language usage in social media is effective in personality prediction. However, except for single language features, a less researched direction is how to leverage the heterogeneous information on social media to have a better understanding of user personality. In this paper, we propose a Heterogeneous Information Ensemble framework, called HIE, to predict users' personality traits by integrating heterogeneous information including self-language usage, avatar, emoticon, and responsive patterns. In our framework, to improve the performance of personality prediction, we have designed different strategies extracting semantic representations to fully leverage heterogeneous information on social media. We evaluate our methods with extensive experiments based on a real-world data covering both personality survey results and social media usage from thousands of volunteers. The results reveal that our approaches significantly outperform several widely adopted state-of-the-art baseline methods. To figure out the utility of HIE in a real-world interactive setting, we also present DiPsy, a personalized chatbot to predict user personality through heterogeneous information in digital traces and conversation logs.
Starting Page	305
Ending Page	314
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018717
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User personality Big five Heterogeneous information
Content Type	Text
Resource Type	Article

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