Drew Margolin

Rising Tides or Rising Stars?: Dynamics of Shared Attention on Twitter during Media Events

“Media events” generate conditions of shared attention as many users simultaneously tune in with the dual screens of broadcast and social media to view and participate. We examine how collective patterns of user behavior under conditions of shared attention are distinct from other “bursts” of activity like breaking news events. Using 290 million tweets from a panel of 193,532 politically active Twitter users, we compare features of their behavior during eight major events during the 2012 U.S. presidential election to examine how patterns of social media use change during these media events compared to “typical” time and whether these changes are attributable to shifts in the behavior of the population as a whole or shifts from particular segments such as elites. Compared to baseline time periods, our findings reveal that media events not only generate large volumes of tweets, but they are also associated with (1) substantial declines in interpersonal communication, (2) more highly concentrated attention by replying to and retweeting particular users, and (3) elite users predominantly benefiting from this attention. These findings empirically demonstrate how bursts of activity on Twitter during media events significantly alter underlying social processes of interpersonal communication and social interaction. Because the behavior of large populations within socio-technical systems can change so dramatically, our findings suggest the need for further research about how social media responses to media events can be used to support collective sensemaking, to promote informed deliberation, and to remain resilient in the face of misinformation.

Research Methods for Studying Evolutionary and Ecological Processes in Organizational Communication

In a previous MCQ article, Monge et al. overviewed the fundamental concepts and processes of evolutionary theory and their applications to key issues in organizational communication. This article extends that work by providing an overview of research tools for studying organizational ecology and evolution, including (a) the variation-selection-retention sequence, (b) the likelihood of events occurring over a period of time (event history analysis), (c) transition sequence of populations from one state to another (sequence analysis), (d) relationships among nodes in networks over time (network analysis), (e) simulation of complex relationships and interactions (computational modeling), (f) changes in populations’ fitness for survival (NKC models), and (g) competitive interdependence among populations over time (predator—prey models). We conclude with a brief review of graphical and qualitative methods.

Political Fact-Checking on Twitter: When Do Corrections Have an Effect?

Research suggests that fact checking corrections have only a limited impact on the spread of false rumors. However, research has not considered that fact-checking may be socially contingent, meaning there are social contexts in which truth may be more or less preferred. In particular, we argue that strong social connections between fact-checkers and rumor spreaders encourage the latter to prefer sharing accurate information, making them more likely to accept corrections. We test this argument on real corrections made on Twitter between Janurary 2012 and April, 2014. As hypothesized, we find that individuals who follow and are followed by the people who correct them are significantly more likely to accept the correction than individuals confronted by strangers. We then replicate our findings on new data drawn from November 2015 to February, 2016. These findings suggest that the underlying social structure is an important factor in the correction of misinformation.

Wiki-worthy: collective judgment of candidate notability

The use of socio-technical data to predict elections is a growing research area. We argue that election prediction research suffers from under-specified theoretical models that do not properly distinguish between ‘poll-like’ and ‘prediction market-like’ mechanisms understand findings. More specifically, we argue that, in systems with strong norms and reputational feedback mechanisms, individuals have market-like incentives to bias content creation toward candidates they expect will win. We provide evidence for the merits of this approach using the creation of Wikipedia pages for candidates in the 2010 US and UK national legislative elections. We find that Wikipedia editors are more likely to create Wikipedia pages for challengers who have a better chance of defeating their incumbent opponent and that the timing of these page creations coincides with periods when collective expectations for the candidates success are relatively high.

Normative Influences on Network Structure in the Evolution of the Children’s Rights NGO Network, 1977-2004

This study examines the impact of legitimacy on the dynamics of interorganizational networks within the nongovernmental organizations’ children’s rights community. The 27-year period of analysis included a critical community event: the ratification of the United Nations Convention on the Rights of the Child (UNCRC). Building on theories of organizational evolution, hypotheses proposed that (1) ratification of the UNCRC served to codify and more broadly communicate the legitimate norms of the community, and (2) dissemination of normative information made it easier (a) for less experienced organizations to form and maintain partnerships, and (b) for organizations to form partnerships without reference to shared third-party contacts or dominant organizations. Data analysis via a longitudinal network model supported the hypotheses. Further investigation via an event history analysis suggested that these effects were largely confined to links among organizations in the children’s rights community and not to links made by these organizations to more general others.

Uncovering social semantics from textual traces: A theory-driven approach and evidence from public statements of U.S. Members of Congress

The increasing abundance of digital textual archives provides an opportunity for understanding human social systems. Yet the literature has not adequately considered the disparate social processes by which texts are produced. Drawing on communication theory, we identify three common processes by which documents might be detectably similar in their textual features—authors sharing subject matter, sharing goals, and sharing sources. We hypothesize that these processes produce distinct, detectable relationships between authors in different kinds of textual overlap. We develop a novel n‐gram extraction technique to capture such signatures based on n‐grams of different lengths. We test the hypothesis on a corpus where the author attributes are observable: the public statements of the members of the U.S. Congress. This article presents the first empirical finding that shows different social relationships are detectable through the structure of overlapping textual features. Our study has important implications for designing text modeling techniques to make sense of social phenomena from aggregate digital traces.

The emotional antecedents of solidarity in social media crowds

This study examines the organizational dynamics of social media crowds, in particular, the influence of a crowd’s emotional expression on its solidarity. To identify the relationship between emotions expressed and solidarity, marked by sustained participation in the crowd, the study uses tweets from a unique population of crowds—those tweeting about ongoing National Football League games. Observing this population permits the use of game results as quasi-random treatments on crowds, helping to reduce confounding factors. Results indicate that participation in these crowds is self-sustaining in the medium term (1 week) and can be stimulated or suppressed by emotional expression in a short term (1 hour), depending on the discrete emotion expressed. In particular, anger encourages participation while sadness discourages it. Positive emotions and anxiety have a more nuanced relationship with participation.

Tracing Coordination and Cooperation Structures via Semantic Burst Detection

Developing technologies that support collaboration requires understanding how knowledge and expertise are shared and distributed among community members. In this article, we explore two forms of knowledge distribution structures, coordination and cooperation, that are central to successful collaboration, among members in political communities. We propose a novel method for detecting the coordination of strategic communication among these members. Our method is grounded on diverse research in communications, centering on the idea that a “semantic burst” (a sudden burst in the usage of a particular set of concepts by multiple individuals within a short time) is a signature of communication coordination. We detect such semantic bursts as being the rapid emergence of unusual three-word sequences (trigrams). We apply our method to public statements by U.S. Senators in the 112th U.S. Congress. Several cases of bursts detected by this method are examined, each corresponding to a theoretical explanation for semantic convergence. Coordination and cooperation networks amongst Senators are then constructed from the semantic convergences that are detected. The structures of these networks are compared to one another and to networks and attributes gathered for the Senate. Results indicate that the detected networks reflect certain underlying tendencies in the social relationships amongst Senators. Analysis of the networks also reveals interesting differences in how members of the different parties select whom to converge with.

Catching fish in the stream: real time analysis of audience behavior in social media

In this interactive poster, we describe a system we designed for identifying and tracking the behavior of distinct audiences in social media streams.

A Multitheoretical Approach to Big Text Data: Comparing Expressive and Rhetorical Logics in Yelp Reviews:

This article uses a multitheoretical approach to investigate the relationship between language use and opinion expression on Yelp. Using review metadata (e.g., star rating) to observe variation in reviewer feelings and motivations, we test for the strength of different message design logics: expressive logics, where language reflects a reviewer’s underlying opinion, and rhetorical logics, where language reflects a reviewer’s desire to make his or her opinion credible and acceptable to their audience. Results suggest that emotional language is motivated by expression as higher rated businesses are reviewed with more positive and fewer negative emotion terms. Rhetorical logics are associated with the use of abstract and self-focused language, with analysis suggesting this may result from the reviewer’s decision to write either narratively or formally.