Using Noisy Self-Reports to Predict Twitter User Demographics
Table 6: Profile Behavioral Features. The first four columns are from users collected in our HF dataset, the fifth column gives a comparison to a random sample of 1M users reported in Wood-Doughty et al. (2017), when available. (m) indicates that a percent or average was computed via micro-averaging across users’ tweets; all others are macro-averaged across users. Almost all differences in a row are statistically significant from one another, according to a Mann-Whitney U Test. However, if two entries in the same row share a superscript symbol, they are not significantly different at a 0.05 confidence level. We are unable to conduct significance tests on the data from the random sample.
['[EMPTY]', 'Asian', 'Black', 'Hispanic/Latinx', 'White', 'Random']
Finally, we consider a few basic measures of Twitter usage, computed from the profile information of each user, following Wood-Doughty et al. Almost all differences in these behavioral features are significant across groups. The biggest difference appears in device usage, where we see that White users are much more likely to have used an iPhone to tweet and much less likely to have used an Android to tweet, when compared against users of the other three demographic groups. This table also provides interesting comparisons to prior work. Comparing against those numbers, we see that across all our demographic groups, users in our datasets are much more likely to include a custom profile image or profile URL, or to enable geotagging on their profile. While the differences from the random sample are substantial for most features, the random sample may contain many more bots or spam accounts than our collected datasets Morstatter et al.