What rhetorics run throughout radical discourse, and why do some gain prominence over others? The scholarship on radicalism largely portrays radical discourse as opposition to powerful ideas and enemies, but radicals often evince great interest in personal and local concerns. To shed light on how radicals use and adopt rhetoric, we analyze an original corpus of more than 23,000 pages produced by Afghan radical groups between 1979 and 2001 using a novel computational abductive approach. We first identify how radicalism not only attacks dominant ideas, actors, and institutions using a rhetoric of subversion, but also how it can use a rhetoric of reversion to urge intimate transformations in morals and behavior. Next, we find evidence that radicals’ networks of support affect the rhetorical mixture they espouse, due to social ties drawing radicals into encounters with backers’ social domains. Our study advances a relational understanding of radical discourse, while also showing how a combination of computational and abductive methods can help theorize and analyze discourses of contention.

Can developmental aid bring peace to war-torn communities? The current literature is divided on this issue. One line of reasoning suggests that aid is likely to decrease violence by improving employment and prosperity, thereby making participation in conflict more costly. Another view cites evidence showing an association between aid projects and increased insurgent activity. Addressing this contradiction, we argue that different types of aid projects lead to different outcomes, as some projects foster an unequal distribution of benefits within communities. Our reasoning draws on qualitative accounts from conflict zones, recent research on how grievances associated with exclusion can foster civil war onset, and experimental findings regarding perceived inequity and punishment. Building on this scholarship, we use a recently developed event-matching methodology to offer insight from contemporary Afghanistan. Aid projects that tend to exclude portions of the community yield more insurgent activity in their wake than more inclusive projects. These results shed light on why some aid projects reduce violence while others do not, emphasizing that efforts to ‘win hearts and minds’ can be a source of both contentment and contestation.

Computational methods have become widespread in the social sciences, but probabilistic language models remain relatively underused. We introduce language models to a general social science readership. First, we offer an accessible explanation of language models, detailing how they estimate the probability of a piece of language, such as a word or sentence, on the basis of the linguistic context. Second, we apply language models in an illustrative analysis to demonstrate the mechanics of using these models in social science research. The example application uses language models to classify names in a large administrative database; the classifications are then used to measure a sociologically important phenomenon: the spatial variation of religiosity. This application highlights several advantages of language models, including their effectiveness in classifying text that contains variation around the base structures, as is often the case with localized naming conventions and dialects. We conclude by discussing language models’ potential to contribute to sociological research beyond classification through their ability to generate language.

Does the use of right-wing social media translate into right-wing protest and violence? Recent research linking social media activity to political attitudes, participation in large, isolated demonstrations, and the occurrence of hate crimes suggests a relationship between right-wing social media activity and subsequent contentious events and violence. However, analyzing this relationship at countrywide scales over many months has not been possible in the absence of reliable georeferenced social media and corresponding contentious politics data. We create a spatial panel dataset measuring 57,505 right-wing social media posts and 1,765 incidents of right-wing unrest in all United States counties from January 2020 through January 2021. Using spatial regression analysis, we find that right-wing social media activity in a given month increased the frequency of right-wing unrest events in the following month. A content analysis of 1.5 million Parler comments reveals that discussions of political events were relatively rare, suggesting that while right-wing social media can be used to coordinate political unrest, this may not fully explain the positive relationship between online and offline activity. Instead, a subsequent analysis of politically moderate and conservative counties indicates that the main effect we observe is driven by shifting perceptions of norms and not changes in individuals’ beliefs or preferences. Our results help explain how social media affect political unrest beyond the conventional coordination mechanism and inform an emerging literature on social media, political attitudes and action, and violence.

After decades of study, much remains unknown about the foraging practices of the earliest inhabitants of Puerto Rico. Here, we present an analysis of the malacological assemblages of two neighboring and (partially) contemporary early sites from the island's southwest, finding intriguing intersite differences in shellfish collection practices. We attribute this diversity to differences in site type, chronology, or changes in local coastal configuration. This work not only provides insights into prehistoric Puerto Rican foraging dynamics but also demonstrates the importance of considering factors including intra-age chronology, site type, and changes in paleoenvironmental conditions when considering ancient foraging practices.