Jean-Philippe Cointet

Three maps and three misunderstandings: A digital mapping of climate diplomacy

This article proposes an original analysis of the international debate on climate change through the use of digital methods. Its originality is twofold. First, it examines a corpus of reports covering 18 years of international climate negotiations, a dataset never explored before through digital techniques. This corpus is particularly interesting because it provides the most consistent and detailed reporting of the negotiations of the United Nations Framework Convention on Climate Change. Second, in this paper we test an original approach to text analysis that combines automatic extractions and manual selection of the key issue-terms. Through this mixed approach, we tried to obtain relevant findings without imposing them on our corpus. The originality of our corpus and of our approach encouraged us to question some of the habits of digital research and confront three common misunderstandings about digital methods that we discuss in the first part of the article (section ‘Three misunderstandings on digital methods in social sciences’). In addition to reflecting on methodology, however, we also wanted to offer some substantial contribution to the understanding of UN-framed climate diplomacy. In the second part of the article (section ‘Three maps on climate negotiations’) we will therefore introduce some of the preliminary results of our analysis. By discussing three visualizations, we will analyze the thematic articulation of the climatic negotiations, the rise and fall of these themes over time and the visibility of different countries in the debate.

Social and semantic coevolution in knowledge networks

Abstract Socio-semantic networks involve agents creating and processing information: communities of scientists, software developers, wiki contributors and webloggers are, among others, examples of such knowledge networks. We aim at demonstrating that the dynamics of these communities can be adequately described as the coevolution of a social and a socio-semantic network. More precisely, we will first introduce a theoretical framework based on a social network and a socio-semantic network, i.e. an epistemic network featuring agents, concepts and links between agents and between agents and concepts. Adopting a relevant empirical protocol, we will then describe the joint dynamics of social and socio-semantic structures, at both macroscopic and microscopic scales, emphasizing the remarkable stability of these macroscopic properties in spite of a vivid local, agent-based network dynamics.

Phylomemetic patterns in science evolution--the rise and fall of scientific fields.

We introduce an automated method for the bottom-up reconstruction of the cognitive evolution of science, based on big-data issued from digital libraries, and modeled as lineage relationships between scientific fields. We refer to these dynamic structures as phylomemetic networks or phylomemies, by analogy with biological evolution; and we show that they exhibit strong regularities, with clearly identifiable phylomemetic patterns. Some structural properties of the scientific fields - in particular their density -, which are defined independently of the phylomemy reconstruction, are clearly correlated with their status and their fate in the phylomemy (like their age or their short term survival). Within the framework of a quantitative epistemology, this approach raises the question of predictibility for science evolution, and sketches a prototypical life cycle of the scientific fields: an increase of their cohesion after their emergence, the renewal of their conceptual background through branching or merging events, before decaying when their density is getting too low.

Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions.

Socio-semantic Dynamics in a Blog Network

The blogosphere can be construed as a knowledge network made of bloggers who are interacting through a social network to share, exchange or produce information. We claim that the social and semantic dimensions are essentially co-determined and propose to investigate the co-evolutionary dynamics of the blogosphere by examining two intertwined issues: first, how does knowledge distribution drive new interactions and thus influence the social network topology? Second, which role structural network properties play in the information circulation in the system?We adopt an empirical standpoint by analyzing the semantic and social activity of a portion of the US political blogosphere, monitored on a period of four months.

Lexical shifts, substantive changes, and continuity in State of the Union discourse, 1790–2014

Significance A synoptic picture of the evolution of American politics is presented, based on analysis of the corpus of presidents’ State of the Union addresses, 1790–2014. The paper presents a strategy for automated text analysis that can identify meaningful categories in textual corpora that span long durées, where terms, concepts and language use changes, and evolution of topical structure is a priori unknown. Discourse streams identified as river networks reveal how change in contents masks continuity in the articulation of the major tasks of governance over US history. This study reveals that the entry into World War I in 1917 indexed the decisive transition to the modern period in American political consciousness, ushering in new objects of political discourse, a more rapid pace of change of those objects, and a fundamental reframing of the main tasks of governance. We develop a strategy for identifying meaningful categories in textual corpora that span long historic durées, where terms, concepts, and language use changes. Our approach is able to account for the fluidity of discursive categories over time, and to analyze their continuity by identifying the discursive stream as the object of interest.

Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study

We propose new methods to detect paradigmatic fields through simple statistics over a scientific content database. We propose an asymmetric paradigmatic proximity metric between terms which provide insight into hierarchical structure of scientific activity and test our methods on a case study with a database made of several millions of resources. We also propose overlapping categorization to describe paradigmatic fields as sets of terms that may have several different usages. Terms can also be dynamically clustered providing a high-level description of the evolution of the paradigmatic fields.

Deep tags: toward a quantitative analysis of online pornography

The development of the web has increased the diversity of pornographic content, and at the same time the rise of online platforms has initiated a new trend of quantitative research that makes possible the analysis of data on an unprecedented scale. This paper explores the application of a quantitative approach to publicly available data collected from pornographic websites. Several analyses are applied to these digital traces with a focus on keywords describing videos and their underlying categorization systems. The analysis of a large network of tags shows that the accumulation of categories does not separate scripts from each other, but instead draws a multitude of significant paths between fuzzy categories. The datasets and tools we describe have been made publicly available for further study.

Generating constrained random graphs using multiple edge switches

The generation of random graphs using edge swaps provides a reliable method to draw uniformly random samples of sets of graphs respecting some simple constraints (e.g., degree distributions). However, in general, it is not necessarily possible to access all graphs obeying some given constraints through a classical switching procedure calling on pairs of edges. Therefore, we propose to get around this issue by generalizing this classical approach through the use of higher-order edge switches. This method, which we denote by “k-edge switching,” makes it possible to progressively improve the covered portion of a set of constrained graphs, thereby providing an increasing, asymptotically certain confidence on the statistical representativeness of the obtained sample.

Precursors and Laggards: An Analysis of Semantic Temporal Relationships on a Blog Network

Most current methods of quantifying the contribution of nodes in blog networks do not account for temporal relationships. We provide a method for measuring how early or late bloggers typically are, in the topic flow of a network of related blogs. Furthermore, we show that this type of analysis adds to the knowledge that can be extracted by studying the network only at the structural level of URL links. We present an algorithm to automatically detect fine-grained discussion topics, characterized by n-grams and time intervals. We then propose a probabilistic model to estimate the temporal relationships that blogs have with one another. We define the precursor score of blog A in relation to blog B as the probability that A enters a new topic before B, discounting the effect created by asymmetric posting rates. Network-level metrics of precursor and laggard behavior are derived from these dyadic precursor score estimations. This model is used to analyze a network of French political blogs. The scores are compared to traditional link degree metrics. We obtain insights into the dynamics of topic participation on this network, as well as the relationship between precursor/laggard and linking behaviors. We validate and analyze results with the help of an expert on the French blogosphere. Finally, we propose possible applications to the improvement of search engine ranking algorithms.