25 Tweets to Know You: A New Model to Predict Personality with Social Media
This work addresses the need for practical personality prediction in social applications by enabling accurate modeling with less input data, making it more applicable to most Twitter users.
The paper tackles the problem of predicting personality from social media text by reducing data requirements, achieving comparable or better accuracy than state-of-the-art techniques with 8 times fewer data based on evaluation of over 1.3K Twitter users.
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.