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A Neural Network Approach for Predicting Personality From Facebook Data

Author

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  • Seren BaÅŸaran
  • Obinna H. Ejimogu

Abstract

Everyday, social media usage particularly Facebook usage are growing exponentially. Simply, inspecting Facebook usage provides meaningful information concerning users’ daily interactions and hence about their personality traits. Numerous studies have been done to harness such streams of Facebook data to obtain accurate prediction of human behavior, social interactions, and personality. The aim of this study is to build a neural network–based predictive model that uses Facebook user’s data and activity to predict the Big 5 personalities. This study combines the inference features highlighted in three different relevant studies which are; number of likes, events, groups, tags, updates, network size, relationship status, age, and gender. The study was conducted on 7,438 unique Facebook participants obtained from the myPersonality database. The findings of this study showed how much a person’s personality can be predicted only by analyzing their Facebook activity. The proposed artificial neural network model was able to correctly classify an individual’s personality at an 85% prediction accuracy.

Suggested Citation

  • Seren BaÅŸaran & Obinna H. Ejimogu, 2021. "A Neural Network Approach for Predicting Personality From Facebook Data," SAGE Open, , vol. 11(3), pages 21582440211, July.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:3:p:21582440211032156
    DOI: 10.1177/21582440211032156
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    Cited by:

    1. Honglin Xiong & Chongjun Fan & Hongmin Chen & Yun Yang & Collins Opoku ANTWI & Xiaomao Fan, 2022. "A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model," SAGE Open, , vol. 12(1), pages 21582440211, January.

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