  In recent years, numerous studies have inferred personality and other traits
from people's online writing. While these studies are encouraging, more
information is needed in order to use these techniques with confidence. How do
linguistic features vary across different online media, and how much text is
required to have a representative sample for a person? In this paper, we
examine several large sets of online, user-generated text, drawn from Twitter,
email, blogs, and online discussion forums. We examine and compare
population-wide results for the linguistic measure LIWC, and the inferred
traits of Big5 Personality and Basic Human Values. We also empirically measure
the stability of these traits across different sized samples for each
individual. Our results highlight the importance of tuning models to each
online medium, and include guidelines for the minimum amount of text required
for a representative result.
