Using data science to understand the film industry’s gender gap

Data science can offer answers to a wide range of social science questions. Here we turn attention to the portrayal of women in movies, an industry that has a significant influence on society, impacting such aspects of life as self-esteem and career choice. To this end, we fused data from the online movie database IMDb with a dataset of movie dialogue subtitles to create the largest available corpus of movie social networks (15,540 networks). Analyzing this data, we investigated gender bias in on-screen female characters over the past century. We find a trend of improvement in all aspects of women's roles in movies, including a constant rise in the centrality of female characters. There has also been an increase in the number of movies that pass the well-known Bechdel test, a popular-albeit flawed-measure of women in fiction. Here we propose a new and better alternative to this test for evaluating female roles in movies. Our study introduces fresh data, an open-code framework, and novel techniques that present new opportunities in the research and analysis of movies

Social Network Extraction from Text

In the pre-digital age, when electronically stored information was non-existent, the only ways of creating representations of social networks were by hand through surveys, inter- views, and observations. In this digital age of the internet, numerous indications of social interactions and associations are available electronically in an easy to access manner as structured meta-data. This lessens our dependence on manual surveys and interviews for creating and studying social networks. However, there are sources of networks that remain untouched simply because they are not associated with any meta-data. Primary examples of such sources include the vast amounts of literary texts, news articles, content of emails, and other forms of unstructured and semi-structured texts. The main contribution of this thesis is the introduction of natural language processing and applied machine learning techniques for uncovering social networks in such sources of unstructured and semi-structured texts. Specifically, we propose three novel techniques for mining social networks from three types of texts: unstructured texts (such as literary texts), emails, and movie screenplays. For each of these types of texts, we demonstrate the utility of the extracted networks on three applications (one for each type of text)

Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation

Movie Description

Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015

A Network Text Analysis of Fight Club

Network Text Analysis (NTA) involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has used an inductive or bottom-up approach to the question of theme extraction. In this paper we take a top-down or deductive approach in that we first establish prior expectations as to the key themes to be found in the text. We then compare and contrast the results of our network analysis with the results of literary and cultural analyses of the film Fight Club as reported in over four dozen other peer-reviewed publications. While our results are remarkably consistent with and complementary to results in those studies, our analysis permits something the others do not—an analytical framework for relating those underlying and central themes to one another

Automatic movie analysis and summarisation

Automatic movie analysis is the task of employing Machine Learning methods to the field of screenplays, movie scripts, and motion pictures to facilitate or enable various tasks throughout the entirety of a movie’s life-cycle. From helping with making informed decisions about a new movie script with respect to aspects such as its originality, similarity to other movies, or even commercial viability, all the way to offering consumers new and interesting ways of viewing the final movie, many stages in the life-cycle of a movie stand to benefit from Machine Learning techniques that promise to reduce human effort, time, or both. Within this field of automatic movie analysis, this thesis addresses the task of summarising the content of screenplays, enabling users at any stage to gain a broad understanding of a movie from greatly reduced data. The contributions of this thesis are four-fold: (i)We introduce ScriptBase, a new large-scale data set of original movie scripts, annotated with additional meta-information such as genre and plot tags, cast information, and log- and tag-lines. To our knowledge, Script- Base is the largest data set of its kind, containing scripts and information for almost 1,000 Hollywood movies. (ii) We present a dynamic summarisation model for the screenplay domain, which allows for extraction of highly informative and important scenes from movie scripts. The extracted summaries allow for the content of the original script to stay largely intact and provide the user with its important parts, while greatly reducing the script-reading time. (iii) We extend our summarisation model to capture additional modalities beyond the screenplay text. The model is rendered multi-modal by introducing visual information obtained from the actual movie and by extracting scenes from the movie, allowing users to generate visual summaries of motion pictures. (iv) We devise a novel end-to-end neural network model for generating natural language screenplay overviews. This model enables the user to generate short descriptive and informative texts that capture certain aspects of a movie script, such as its genres, approximate content, or style, allowing them to gain a fast, high-level understanding of the screenplay. Multiple automatic and human evaluations were carried out to assess the performance of our models, demonstrating that they are well-suited for the tasks set out in this thesis, outperforming strong baselines. Furthermore, the ScriptBase data set has started to gain traction, and is currently used by a number of other researchers in the field to tackle various tasks relating to screenplays and their analysis

Structure-aware narrative summarization from multiple views

Narratives, such as movies and TV shows, provide a testbed for addressing a variety of challenges in the field of artificial intelligence. They are examples of complex stories where characters and events interact in many ways. Inferring what is happening in a narrative requires modeling long-range dependencies between events, understanding commonsense knowledge and accounting for non-linearities in the presentation of the story. Moreover, narratives are usually long (i.e., there are hundreds of pages in a screenplay and thousands of frames in a video) and cannot be easily processed by standard neural architectures. Movies and TV episodes also include information from multiple sources (i.e., video, audio, text) that are complementary to inferring high-level events and their interactions. Finally, creating large-scale multimodal datasets with narratives containing long videos and aligned textual data is challenging, resulting in small datasets that require data efficient approaches. Most prior work that analyzes narratives does not consider the above challenges all at once. In most cases, text-only approaches focus on full-length narratives with complex semantics and address tasks such as question-answering and summarization, or multimodal approaches are limited to short videos with simpler semantics (e.g., isolated actions and local interactions). In this thesis, we combine these two different directions in addressing narrative summarization. We use all input modalities (i.e., video, audio, text), consider full-length narratives and perform the task of narrative summarization both in a video-to-video setting (i.e., video summarization, trailer generation) and a video-to-text setting (i.e., multimodal abstractive summarization). We hypothesize that information about the narrative structure of movies and TVepisodes can facilitate summarizing them. We introduce the task of Turning Point identification and provide a corresponding dataset called TRIPOD as a means of analyzing the narrative structure of movies. According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a movie or TV episode: they define the plot structure and determine its progression and thematic units. We validate that narrative structure contributes to extractive screenplay summarization by testing our hypothesis on a dataset containing TV episodes and summary-specific labels. We further hypothesize that movies should not be viewed as a sequence of scenes from a screenplay or shots from a video and instead be modelled as sparse graphs, where nodes are scenes or shots and edges denote strong semantic relationships between them. We utilize multimodal information for creating movie graphs in the latent space, and find that both graph-related and multimodal information help contextualization and boost performance on extractive summarization. Moving one step further, we also address the task of trailer moment identification, which can be viewed as a specific instiatiation of narrative summarization. We decompose this task, which is challenging and subjective, into two simpler ones: narrativestructure identification, defined again by turning points, and sentiment prediction. We propose a graph-based unsupervised algorithm that uses interpretable criteria for retrieving trailer shots and convert it into an interactive tool with a human in the loop for trailer creation. Semi-automatic trailer shot selection exhibits comparable performance to fully manual selection according to human judges, while minimizing processing time. After identifying salient content in narratives, we next attempt to produce abstractive textual summaries (i.e., video-to-text). We hypothesize that multimodal information is directly important for generating textual summaries, apart from contributing to content selection. For that, we propose a parameter efficient way for incorporating multimodal information into a pre-trained textual summarizer, while training only 3.8% of model parameters, and demonstrate the importance of multimodal information for generating high-quality and factual summaries. The findings of this thesis underline the need to focus on realistic and multimodal settings when addressing narrative analysis and generation tasks