Quantification of Diverse Personal Attributes in Tweets

Abstract

We studied personal attributes represented in tweets, such as gender, occupation, and age groups. First, we examined how much these basic attributes can be predicted from the texts of tweets, each of which was vectorized by a word2vec-based method for machine learning. The results showed that machine learning algorithms can predict all three attributes with 60-70% accuracy. We also confirmed that differences in word usage between males and females (related to semantic differences) affect the predictive accuracy of gender. Furthermore, we quantified other personal attributes, such as Big 5 and values, using IBM Personality Insights.