Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Review Help
Search
Beyond the words: predicting user personality from heterogeneous information
Wei H., Zhang F., Yuan N., Cao C., Fu H., Xie X., Rui Y., Ma W.  WSDM 2017 (Proceedings of the 10th ACM International Conference on Web Search and Data Mining, Cambridge, UK, Feb 6-10, 2017)305-314.2017.Type:Proceedings
Date Reviewed: Apr 19 2018

In contemporary digital culture, everyday life is permeated by computer-mediated activities. As a byproduct, a reservoir of digital trace data is generated. Can this data be leveraged to infer one’s personality?

Given the importance of personality prediction in several compelling applications ranging from mental health monitoring to occupational proficiency, in recent years there has been an increasing interest in developing new methods to predict personality traits automatically, by relying on digital traces. However, previous studies have focused on a single specific modality such as language patterns or profile pictures; never before has heterogeneous digital trace data been analyzed holistically.

By relying on the use of avatars, emoticons, language, and responsive patterns in social media as documenting slices of users’ everyday interactions, the authors demonstrate that it is possible to infer user personality traits in an effective and efficient way. Additionally, they provide useful insights about the correlation between different aspects of heterogeneous digital footprints and user personality. The proposed approach consists of first extracting semantic representations from each personality cue through feature engineering, then in extracting classification scores for each domain separately, and finally in using the set of resulting scores as input of a stacked generalization-based ensemble method.

Performances obtained with large-scale experiments compare favorably with respect to state-of-the-art methodologies such as MyPersonality and Personality Insights, also demonstrating the ability of the system to accurately distinguish users with extremely high scores on a given single personality trait from those with medium scores spread out over several traits. Furthermore, the authors tested the reliability of the proposed framework in a real-world scenario where a chatbot, called DiPsy, acts as a personalized digital psychologist able to predict the user’s mental status based on digital footprints, to revise the prediction results based on its understanding during natural language conversations, and eventually to conduct cognitive behavioral therapy.

Overall, this ambitious study shows that cyber-psychology is becoming a reality. Therefore, it can be a good read for researchers in the fields of social media analysis, multimedia applications, multimodal fusion, and social signal processing.

Reviewer:  Mariella Dimiccoli Review #: CR145986 (1807-0391)
Bookmark and Share
  Reviewer Selected
 
 
General (H.2.0 )
 
 
Psychology (J.4 ... )
 
Would you recommend this review?
yes
no
Other reviews under "General": Date
Design of the Mneme persistent object store
Moss J. ACM Transactions on Information Systems 8(2): 103-139, 2001. Type: Article
Jul 1 1991
Database management systems
Gorman M., QED Information Sciences, Inc., Wellesley, MA, 1991. Type: Book (9780894353239)
Dec 1 1991
Database management (3rd ed.)
McFadden F., Hoffer J., Benjamin-Cummings Publ. Co., Inc., Redwood City, CA, 1991. Type: Book (9780805360400)
Jun 1 1992
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy