25 Tweets to Know You: A New Model to Predict Personality with Social Media

TL;DR

This paper introduces a new Twitter-based personality prediction model that uses Word Embedding features and Gaussian Processes, requiring significantly less data while maintaining high accuracy.

Contribution

The study presents a novel, data-efficient personality prediction model for social media that outperforms existing methods with much less user input data.

Findings

Achieves comparable or better accuracy than state-of-the-art methods.

Requires 8 times less data for effective prediction.

Validated on over 1,300 Twitter users.

Abstract

Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we aim to drastically reduce the data requirement for personality modeling and develop a model that is applicable to most users on Twitter. Our model integrates Word Embedding features with Gaussian Processes regression. Based on the evaluation of over 1.3K users on Twitter, we find that our model achieves comparable or better accuracy than state of the art techniques with 8 times fewer data.