Abstract:
The present study used a computational linguistic approach to examine moral
foundations, emotionality, and personal concerns in the written communications of mass
shooters. Writings by mass shooters (N = 36) were harvested from an online database and coded
for writing type using five writing categories: manifestos, blog posts, social media, private
letters, and journal entries. They were then submitted to linguistic analyses using Linguistic
Inquiry and Word Count (LIWC). Shooters’ writings were then compared to a sample of
prisoners (N = 35) who were convicted of violent crime along with the normative data available
in LIWC. The Moral Foundations Dictionary (MFD) was also used to predict differences
between the samples, although no significant differences were found between shooters and
prisoners on the five moral foundations. Overall, results of these analyses indicated that mass
shooters primarily use high rates of negative emotion and swear words. Unlike previous studies,
however, mass shooters were comparatively low on cognitive processes and complexity relative
to the prisoner sample. Exploratory analyses were then conducted using the entire LIWC2015
and MFD dictionaries to identify the set of word categories that maximally predicts differences
between groups across writing types.
