Methodological Advances in Research on Social Movements, Conflict, and Change: Volume 47

Social movement scholars use protest events as a way to quantify social movements and have most often used large, national newspapers to identify those events. This has introduced known and unknown biases into our measurement of social movements. We know that national newspapers tend to cover larger and more contentious events and organizations. Protest events are furthermore a small part of what social movements actually do. Without other readily available options to quantify social movements, however, big-N studies have continued to focus on protest events via a few large newspapers. With advances in digitized data and computational methods, we now no longer have to rely on large newspapers or focus only on protests to quantify important aspects of social movements. In this paper, we use the environmental movement as a case study, analyzing data from a wide range of local, regional, and national newspapers in the United States to quantify multiple facets of social movements. We argue that the incorporation of more data and new methods to quantify information in text has the potential to transform the way we both conceive of and measure social movements in three ways: (1) the type of focal social movement organization included, (2) the type of tactics and issues covered, and (3) the ability to go beyond protest events as the primary unit of analysis. In addition to demonstrating ways that the focus on counting protest events has introduced specific biases in the type of tactics, issues, and organizations covered in social movement research, we argue that computational methods can help us extract and count meaningful aspects of social movements well beyond event counts. In short, the infusion of new data and methods into social movements, peace, and conflict studies could lead us to a substantial shift in the way we quantify social movements, from protest events to everything that occurs outside of them.

This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

Satellite images can be a powerful source of data for analyses of conflict dynamics and social movements, but sociology has been slow to develop methods and metadata standards for transforming those images into data. We ask: How can satellite images become useful data? What are the key methodological and ethical considerations for incorporating high-resolution satellite images into conflict research? Why are metadata important in this work? We begin with a review of recent developments in satellite-based social scientific work on conflict, then discuss the technical and epistemological issues raised by machine processing of satellite information into user-ready images. We argue that high-resolution images can be useful analytical tools provided they are used with full awareness of their ethical and technical parameters. To support our analysis, we draw on two novel studies of satellite data research practices during the Syrian war. We conclude with a discussion of specific methodological procedures tried and tested in our ongoing work.

While social media data are used increasingly in studies of social movements, social media evolves far more rapidly than academic research and publication. This chapter argues that researchers should adopt historical and archival approaches to social media data. Treating social media data as an “instant archive” – one that is self-curated, is co-constituted, and changes rapidly – we caution researchers to pay attention to the features of this archive and their implications for working with the data therein. Applying insights from recent discussions of archival methods for social science research to the specific features of social media data, we explore how platform features, repressive effects, and user innovations affect the content of the instant archive. We then offer strategies for researchers' methodological approaches, including how best to select units of analysis and platforms, how to collect and interpret archival materials, and how to identify silences in the data.

In this chapter, I address the issues in doing a feminist ethnography based on the archives of a lesbian feminist community structured around music. In doing so, I grapple with questions central to archival research with marginalized social movement communities. I pose these questions as moments of interrogation where I draw on personal experience as well as literature on archival research to create a framework aimed at social movement researchers who are considering or doing archival work. These interrogations cover three broad areas and apply to different moments in the research process. First, what are the origin stories of social movement archives? Second, how can researchers construct the stories of marginalized and vulnerable communities? Third, how can we access the voices of community members found in the archives? To answer these questions, I identify how conceptual tools such as identifying community boundaries and documenting types of interaction can aid a scholar in the research process.

We are long overdue for a conversation about ethical treatment of student researchers. Ethical treatment of research participants has been carefully defined through decades of public conversation, and ethical practices have been institutionalized through mechanisms like mandatory ethics trainings and Institutional Review Boards. Student researchers deserve the same level of consideration. While there are many types of ethical violations of student labor in research projects, there are two that are of particular concern to social movements researchers: use of volunteer labor without clear academic or professional benefits, and failure to ensure the safety of student researchers. The first of these ethical violations is especially common in social movements research because of the emergent nature of protests: new rounds of protests begin and researchers seek to rapidly collect data on a tight timeframe, making grant funding to pay student researchers challenging. The second situation emerges when faculty researchers do not consider the ways students' race, gender identity, or other characteristics, or the nature of the protests themselves might create potential risks for students.

In this paper, I propose using the Belmont Report principles to create guidelines for ethical treatment of student researchers. While these principles were developed for the purposes of protecting research participants, the principles of respect for persons, beneficence, and justice help us to clarify the risks and benefits for student researchers, to find ways to maximize the benefits of student research participation, and to understand and address the inequalities that plague graduate student training.

The scholarly conversation about insider and outsider positionality in observational research is long, rich, and often contentious. Debates about the benefits and challenges of studying sites where researchers share insider identities with participants, in particular, have yielded insights about power, inequality, and the uniquely relational character of observational research. In this chapter, we enter this conversation by relating our experiences with outsider-ness and insider-ness while studying social movements. We draw on two ethnographic case studies of social movement organizations within higher education settings. We identify some of the challenges faced while qualitatively studying identity-based movements embedded within institutions, specifically (1) being mindful of and negotiating the impact of researcher identity and how it relates to those of the subjects; (2) determining one's level of participation within the movement being studied; and (3) securing research approval and access to data. We offer suggestions for how researchers might think through these challenges in their own work.

Under which conditions do social movements receive extensive attention from the mainstream news media? We develop an institutional mediation model that argues that combinations of the news-heightening characteristics of movements, including their disruptive capacities, organizational resources, and political orientation, and political contexts, including partisan regimes and benefiting from national policies, bring extensive attention to movements. It also holds that investigations will draw extensive media attention to movements, and those that have achieved prominence in the news will remain prominent under specific conditions. We appraise these combinational arguments by examining 29 social movements across 100 years in four national newspapers using qualitative comparative analysis (QCA). Researchers typically use QCA to study the consequences of movements when they hypothesize outcomes to result from multiple combinations of conditions. This raises our second main question: How should scholars best address combinational hypotheses using QCA? Here we employ Venn diagrams to identify and illustrate key analytical issues and anomalies, including constrained diversity in observational data, empirical instances when combinations of conditions do not produce the expected outcome, and instances when unexpected combinations of conditions produce a consistent result. We also demonstrate the value of broad comparisons across movements and over time in these analyses.

Measuring the diffusion of protests, or more generally, the diffusion of events, is an ongoing task in social sciences. This paper proposes an inter-event approach to study what types of protests tend to diffuse or decline. We develop a standardized, five-step procedure to measure what we define as “event diffusion momentum” (EDM): (1) employ event-based data containing information on the time, location, and features of each protest; (2) define the temporal and spatial ranges of interest; (3) for each observation, count the number of events before and after it within the defined ranges; (4) predict the numbers of post-event and pre-event protests with appropriate count models; (5) calculate the ratios of predicted values for each predictor and confidence intervals using the delta method. The ratio is the EDM. Applying this method to Dynamics of Collective Action (DoCA) data, we identify several micro- and macro-level factors associated with protest diffusion in the United States, 1960–1995. We conclude with the implications and generalizability of the proposed method.

Social movements are made up of organized groups and individuals working together to accomplish shared objectives. Under what circumstances do active groups build and break their coalitions? Five conditions have been identified in the literature as influencing coalition formation: common identity, resources, organizational structure, historical connection, and institutional setting. Whereas coalition dynamics within a movement wave are best understood in terms of institutional opportunities and threats, further research is needed to determine how and to what extent these contextual elements influence coalitions. This chapter examines how threats posed by indiscriminate and selective repression affect the shape and structure of interorganizational coalitions during the 2019 Anti-Extradition Law Amendment Bill (Anti-ELAB) protests in Hong Kong. The analysis relies on an original political event dataset and an organization-event network dataset. These datasets were produced utilizing syntactic event coding techniques based on Telegram posts, which Hong Kong protesters used to distribute information, plan future actions, and crowdsource news. Furthermore, Telegram provides detailed information about state activities, event-level coalitions, and violent groups, which is difficult to access from other sources. This study investigates the coalition networks across the movement's four stages, each of which was marked by a particular type and degree of repression. The findings indicate that indiscriminate and selective repression have varied effects on coalition networks. A wide coalition disintegrates as a result of indiscriminate repression. Selective repression, however, leads to the formation of coalitions around activist groups targeted by repression.