Character Networks and Centrality

A character network represents relations between characters from a text; the relations are based on text proximity, shared scenes/events, quoted speech, etc. Our project sketches a theoretical framework for character network analysis, bringing together narratology, both close and distant reading approaches, and social network analysis. It is in line with recent attempts to automatise the extraction of literary social networks (Elson, 2012; Sack, 2013) and other studies stressing the importance of character- systems (Woloch, 2003; Moretti, 2011). The method we use to build the network is direct and simple. First, we extract co-occurrences from a book index, without the need for text analysis. We then describe the narrative roles of the characters, which we deduce from their respective positions in the network, i.e. the discourse. As a case study, we use the autobiographical novel Les Confessions by Jean-Jacques Rousseau. We start by identifying co-occurrences of characters in the book index of our edition (Slatkine, 2012). Subsequently, we compute four types of centrality: degree, closeness, betweenness, eigenvector. We then use these measures to propose a typology of narrative roles for the characters. We show that the two parts of Les Confessions, written years apart, are structured around mirroring central figures that bear similar centrality scores. The first part revolves around the mentor of Rousseau; a figure of openness. The second part centres on a group of schemers, depicting a period of deep paranoia. We also highlight characters with intermediary roles: they provide narrative links between the societies in the life of the author. The method we detail in this complete case study of character network analysis can be applied to any work documented by an index.

Analyse des réseaux de personnages dans Les Confessions de Jean-Jacques Rousseau

Frédéric Kaplan, Yannick Rochat

Cet article étudie le concept de centralité dans les réseaux de personnages apparaissant dans Les Confessions de Jean-Jacques Rousseau. Notre objectif est ainsi de caractériser certains aspects des rôles des personnages du récit sur la base de leurs cooccurrences dans le texte. We sketch a theoretical framework for literary network analysis, bringing together narratology, distant reading and social network analysis. We extract co-occurrences from a book index without the need for text analysis and describe the narrative roles of the characters. As a case study, we use the autobiographical novel Les Confessions from Jean-Jacques Rousseau. Eventually, we compute four types of centrality — degree, closeness, betweenness, eigenvector — and use these measures to propose a typology of narrative roles for the characters.

2014

A social network analysis of Rousseau’s autobiography “Les Confessions”

Cyril Antoine Michel Bornet, Frédéric Kaplan, Yannick Rochat

We propose an analysis of the social network composed of the characters appearing in Jean-Jacques Rousseau's autobiographic Les Confessions, with existence of edges based on co-occurrences. This work consists of twelve volumes, that span over fifty years of his life. Having a unique author allows us to consider the book as a coherent work, unlike some of the historical texts from which networks often get extracted, and to compare the evolution of patterns of characters through the books on a common basis. Les Confessions, considered as one of the first modern autobiographies, has the originality to let us compose a social network close to the reality, only with a bias introduced by the author, that has to be taken into account during the analysis. Hence, with this paper, we discuss the interpretation of networks based on the content of a book as social networks. We also, in a digital humanities approach, discuss the relevance of this object as an historical source and a narrative tool.

2013

Social Network Analysis for Automatic Role Recognition

Sarah Favre

The computing community has shown a significant interest for the analysis of social interactions in the last decade. Different aspects of social interactions have been studied such as dominance, emotions, conflicts, etc. However, the recognition of roles has been neglected whereas these are a key aspect of social interactions. In fact, sociologists have shown not only that people play roles each time they interact, but also that roles shape behavior and expectations of interacting participants. The aim of this thesis is to fill this gap by investigating the problem of automatic role recognition in a wide range of interaction settings, including production environments, e.g. news and talk-shows, and spontaneous exchanges, e.g. meetings. The proposed role recognition approach includes two main steps. The first step aims at representing the individuals involved in an interaction with feature vectors accounting for their relationships with others. This step includes three main stages, namely segmentation of audio into turns (i.e. time intervals during which only one person talks), conversion of the sequence of turns into a social network, and use of the social network as a tool to extract features for each person. The second step uses machine learning methods to map the feature vectors into roles. The experiments have been carried out over roughly 90 hours of material. This is not only one of the largest databases ever used in literature on role recognition, but also the only one, to the best of our knowledge, including different interaction settings. In the experiments, the accuracy of the percentage of data correctly labeled in terms of roles is roughly 80% in production environments and 70% in spontaneous exchanges (lexical features have been added in the latter case). The importance of roles has been assessed in an application scenario as well. In particular, the thesis shows that roles help to segment talk-shows into stories, i.e. time intervals during which a single topic is discussed, with satisfactory performance. The main contributions of this thesis are as follows: To the best of our knowledge, this is the first work where social network analysis is applied to automatic analysis of conversation recordings. This thesis provides the first quantitative measure of how much roles constrain conversations, and a large corpus of recordings annotated in terms of roles. The results of this work have been published in one journal paper, and in five conference articles.

EPFL2011

Social Network Analysis for Automatic Role Recognition

Sarah Favre

EPFL2011