Gender Inference using Statistical Name Characteristics in Twitter
Table 1: Number of names contained in each gender-labeled name dataset. The numbers show the labels as they are retrieved from the dataset files.
['Gender', 'Census', 'nam_dict']
Note that the Census data does not contain dedicated gender labels. The names are assigned to a gender using their presence in one of two files. Therefore, the number of unisex names is not explicitly given. After intersecting the names from both data files, we found that 331 names in the Census data are used by both genders. The names from the nam_dict dataset are stored and labeled in a single file. The Census data contain a much bigger share of female names than the nam_dict data. The nam_dict dataset contains far more unisex names. We choose to use the nam_dict dataset because it contains the most extensive name information at hand. The gender information was transformed by assigning the categories “male first name”, “mostly male name”, and “male name if first part of the name” to “male”, the categories “female first name”, “mostly female name”, and “female name if first part of the name” to “female”, and the category “unisex first name” was removed, because it could decrease the accuracy.