We examine differences in portrayal of characters in movies using psycholinguistic and graph theoretic measures computed directly from screenplays. Differences are examined with respect to characters' gender, race, age and other metadata. Psycholinguistic metrics are extrapolated to dialogues in movies using a linear regression model built on a set of manually annotated seed words. Interesting patterns are revealed about relationships between genders of production team and the gender ratio of characters. Several correlations are noted between gender, race, age of characters and the linguistic metrics.

Children with Autism Spectrum Disorder (ASD) are known to have difficulty in producing and perceiving emotional facial expressions. Their expressions are often perceived as atypical by adult observers. This paper focuses on data driven ways to analyze and quantify atypicality in facial expressions of children with ASD. Our objective is to uncover those characteristics of facial gestures that induce the sense of perceived atypicality in observers. Using a carefully collected motion capture database, facial expressions of children with and without ASD are compared within six basic emotion categories employing methods from information theory, time-series modeling and statistical analysis. Our experiments show that children with ASD usually have less complex expression producing mechanisms; the differences in facial dynamics between children with and without ASD primarily come from the eye region. Our study also notes that children with ASD exhibit lower symmetry between left and right regions, and lower variation in motion intensity across facial regions.

Violent content in movies can influence viewers’ perception of the society. For example, frequent depictions of certain demographics as perpetrators or victims of abuse can shape stereotyped attitudes. In this work, we propose to characterize aspects of violent content in movies solely from the language used in the scripts. This makes our method applicable to a movie in the earlier stages of content creation even before it is produced. This is complementary to previous works which rely on audio or video post production. Our approach is based on a broad range of features designed to capture lexical, semantic, sentiment and abusive language characteristics. We use these features to learn a vector representation for (1) complete movie, and (2) for an act in the movie. The former representation is used to train a movie-level classification model, and the latter, to train deep-learning sequence classifiers that make use of context. We tested our models on a dataset of 732 Hollywood scripts annotated by experts for violent content. Our performance evaluation suggests that linguistic features are a good indicator for violent content. Furthermore, our ablation studies show that semantic and sentiment features are the most important predictors of violence in this data. To date, we are the first to show the language used in movie scripts is a strong indicator of violent content. This offers novel computational tools to assist in creating awareness of storytelling.

Direct content analysis reveals important details about movies including those of gender representations and potential biases. We investigate the differences between male and female character depictions in movies, based on patterns of language used. Specifically, we use an automatically generated lexicon of linguistic norms characterizing gender ladenness. We use multivariate analysis to investigate gender depictions and correlate them with elements of movie production. The proposed metric differentiates between male and female utterances and exhibits some interesting interactions with movie genres and the screenplay writer gender.