No abstract

We present a methodology for the extraction of narrative information from a large corpus. The key idea is to transform the corpus into a network, formed by linking the key actors and objects of the narration, and then to analyse this network to extract information about their relations. By representing information into a single network it is possible to infer relations between these entities, including when they have never been mentioned together. We discuss various types of information that can be extracted by our method, various ways to validate the information extracted and two different application scenarios. Our methodology is very scalable, and addresses specific research needs in social sciences.

The Civil Rights Movements in the southern United States and Northern Ireland were able to mobilize African Americans and Irish Catholics respectively against minority discrimination. These movements initially displayed very similar goals and tactics, looking at courts to counter institutional discrimination, but in successive stages of contention their trajectories fundamentally diverged. While legal mobilization in the United States constituted one of the pillars of the civil rights strategy of contention, in Northern Ireland legal tactics were supplanted by a transgressive (and at times violent) repertoire of contention. To explain this discrepancy, this article relies on the concept of legal opportunity structure (LOS) as an analytical tool to investigate how a state's legal structure affects social movement legal mobilization. Accessibility to courts, availability of justiciable rights and judiciary receptivity are identified as the three core dimensions of the LOS shaping its degree of openness/closure. The paired comparison of these movements reveal that a closed LOS may narrow down the array of tactical options available to social movements, redirecting activists' efforts towards protest. Conversely, an open LOS may encourage legal mobilization as a viable tactical option and, under certain circumstances, even promote contentious activities.

This paper advocates an actor-centered, relational view of agency and proposes Quantitative Narrative Analysis (QNA) as a promising method for operationalizing and measuring agency. QNA organizes the information contained in narrative texts by exploiting the invariant linguistic structural properties of narrative—namely, sets of SVOs (Subject, Verb, Object) organized in predictable sequences and where in narrative S are actors and V are actions. The relational data made available by QNA are ideally suited for analysis with geographic information systems (GIS) tools, sequence analysis, or network analysis. These tools preserve the centrality of agency (actors and their actions) in social scientific explanation of social reality. An application of QNA to newspaper stories of lynchings in Georgia (1875–1930) will illustrate the power of this approach. The paper complements the illustration of this quantitative way of measuring agency with discourse analysis—another popular social science approach to texts. We will rely on this approach to illustrate how linguistic and rhetorical strategies can be used to hide agency in texts and the challenges (and solutions) this poses for measurement: How can we measure something that is not there?

No abstract