Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts

Abstract

Exposure to violent, sexual, or substanceabuse content in media increases the willingness of children and adolescents to imitate similar behaviors. Computational methods that identify portrayals of risk behaviors from audio-visual cues are limited in their applicability to films in post-production, where modifications might be prohibitively expensive. To address this limitation, we propose a model that estimates content ratings based on the language use in movie scripts, making our solution available at the earlier stages of creative production. Our model significantly improves the state-of-the-art by adapting novel techniques to learn better movie representations from the semantic and sentiment aspects of a character's language use, and by leveraging the co-occurrence of risk behaviors, following a multi-task approach. Additionally, we show how this approach can be useful to learn novel insights on the joint portrayal of these behaviors, and on the subtleties that filmmakers may otherwise not pick up on.

From None to Severe: Predicting Severity in Movie Scripts

“…Comparing to the previous works with restricted data access (Martinez et al, 2019(Martinez et al, , 2020, our dataset is roughly five times larger, and the data source is free to access. We made the updated dataset publicly available with the same data partitions in this work for reproducibility purposes.…”

“…(Shafaei et al, 2020) initiated the research of predicting MPAA ratings of the movies leveraging movie script and metadata. (Martinez et al, 2019) focused on violence detection using movie scripts while (Martinez et al, 2020) expanded the scope to violence, substance abuse, and sex. Both works intended to predict the severity of age-restricted content into three manually defined levels: low, mid, and high.…”

From None to Severe: Predicting Severity in Movie Scripts

“…Comparing to the previous works with restricted data access (Martinez et al, 2019(Martinez et al, , 2020, our dataset is roughly five times larger, and the data source is free to access. We made the updated dataset publicly available with the same data partitions in this work for reproducibility purposes.…”

“…(Shafaei et al, 2020) initiated the research of predicting MPAA ratings of the movies leveraging movie script and metadata. (Martinez et al, 2019) focused on violence detection using movie scripts while (Martinez et al, 2020) expanded the scope to violence, substance abuse, and sex. Both works intended to predict the severity of age-restricted content into three manually defined levels: low, mid, and high.…”

Annotation and Evaluation of Coreference Resolution in Screenplays

“…Screenplays are semi-structured text documents containing the dialogue and directions of a film. Automated screenplay analysis provides an opportunity early in the creative process to offer insights into character representations and portrayals (of who interacts with whom, about what, and how), including from a diversity, inclusion, and social impact perspective (Ramakrishna et al, 2017;Shafaei et al, 2020;Martinez et al, 2020). A typical screenplay contains indented blocks of text that can be classified into scene headers, scene descriptions, speakers, and utterances, as shown in Figure 1 (Agarwal et al, 2014).…”