1. Networks and Flows in Organizational Communication. Part I: The Multitheoretical, Multilevel Framework. 2. Network Concepts, Measures, and the Multitheoretical, Multilevel Analytical Framework. 3. Communication and Knowledge Networks as Complex Systems. 4. Computational Modeling of Networks. Part II: Social Theories for Studying Communication Networks. 5. Theories of Self-Interest and Collective Action. 6. Contagion, Semantic, and Cognitive Theories. 7. Exchange and Dependency Theories. 8. Homophily, Proximity, and Social Support Theories. 9. Evolutionary and Coevolutionary Theories. Part I
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Abstract Racism can hurt by negatively impacting mental health. For instance, large-scale events tied to racism like the May 2020 police-involved murder of George Floyd have been linked to poor mental health indicators (e.g. depression and anxiety). Notably, racism can spark antiracist engagement—support for addressing systemic racism. For example, Floyd's murder sparked unprecedented antiracist engagement, including heightened Black Lives Matter (BLM) support and protest participation. The present research explored the potential that antiracist engagement can heal: be positively associated with well-being. First, study 1 found that state-level BLM engagement (i.e. protest numbers, antiracism information-seeking on Google/YouTube) during an 8-week period following Floyd's death was associated with positive mental health indicators (i.e. lower depression and anxiety, higher self-rated health). It found these effects among racial/ethnic minorities (e.g. Black/African Americans, Hispanics, N = 161,359) and Whites (N = 516,002). Then, study 2 examined social media data (i.e. tweets) and emotional well-being. It used a measure of happiness indexed across 144,649,285,571 tweets from 2019 through 2021. It found a positive correlation between the volume of tweets with antiracist engagement content (e.g. referenced efforts to address systemic racism) and the happiness measure. Finally, study 3 examined antiracism protest data/information-seeking and a sample of BLM tweets (N = 100,321) posted between April and July 2020. Conceptually replicating studies 1–2, study 3 found that antiracist engagement was associated with greater positive emotion/sentiment (e.g. happiness) relative to negative emotion/sentiment (e.g. anxiety). Relevant to theory and policy, the observed results suggest that antiracist engagement can be associated with benefits for well-being across racial/ethnic groups.
This article explores the relative influence of individual and network-level effects on the emergence of online social relationships. Using network modeling and data drawn from logs of social behavior inside the virtual world Second Life, we combine individual- and network-level theories into an integrated model of online social relationship formation. Results reveal that time spent online and the network pressure toward balance (individuals tending to form relationships with others who have relationships in common) predict the emergence of online relationship ties, while gender, age, proximity, homophily (the tendency of individuals to form relationships among people with similar traits), and preferential attachment are not significant predictors within the observed networks. We discuss these results in light of existing research on online social relationships and describe how digital data and network analytics enable novel insights about the emergence of online social relationships.
This study examines the influence of different types of diversity, both observable and unobservable, on the creation of innovative ideas. Our framework draws on theory and research on information processing, social categorization, coordination, and homophily to posit the influence of cognitive, gender, and country diversity on innovation. Our longitudinal model is based on a unique data set of 1,354 researchers who helped create the new scientific field of oncofertility, by collaborating on 469 publications over a 4-year period. We capture the differences among researchers along cognitive, country, and gender dimensions, as well as examine how the resulting diversity or homophily influences the formation of collaborative innovation networks. We find that innovation, operationalized as publishing in a new scientific discipline, benefits from both homophily and diversity. Homophily in country of residence and working with prior collaborators help reduce uncertainty in the interactions associated with innovation, while diversity in knowledge enables the recombinant knowledge required for innovation.
Wikipedia's coverage of breaking news and current events dominates editor contributions and reader attention in any given month. Collaborators on breaking news articles rapidly synthesize content to produce timely information in spite of steep coordination demands. Wikipedia's coverage of breaking news events thus presents a case to test theories about how open collaborations coordinate complex, time-sensitive, and knowledge-intensive work in the absence of central authority, stable membership, clear roles, or reliable information. Using the revision history from Wikipedia articles about over 3,000 breaking news events, we investigate the structure of interactions between editors and articles. Because breaking article collaborations unfold more rapidly and involve more editors than most Wikipedia articles, they potentially regenerate prior forms of organizing. We analyze whether the structures of breaking and nonbreaking article networks are (a) similarly structured over time, (b) exhibit features of organizational regeneration, and (c) have similar collaboration dynamics over time. Breaking and nonbreaking article exhibit similarities in their structural characteristics over the long run, and there is less evidence of organizational regeneration on breaking articles than nonbreaking articles. However, breaking articles emerge into well-connected collaborations more rapidly than nonbreaking articles, suggesting early contributors play a crucial role in supporting these high-tempo collaborations.
This article applies the concepts of alpha, beta, and gamma changes to test whether the implementation of a new office information system with networking capabilities changes the way organizational members conceptualize office work. The traditional approach (t‐test) was used to measure alpha change and indicated little change in how effectively the respondents felt they performed eight generic office activities before implementation (T1) and nine months after implementation (T2). However, considerable change was detected between effectiveness reported at T1 and a retrospective assessment of T1 effectiveness reported at T2 (called "then" assessments). Strong change was also detected between "then" assessments and T2 effectiveness reported at T2, indicating beta change. Multiple hierarchical tests showed that most of the change was actually gamma change; the T2 and the "then" factor structures and covariances differed significantly. This study supports propositions that using computers to accomplish organizational work may be associated with different conceptualizations of work, which may create ambiguity and uncertainty if training and management policies do not respond appropriately. Finally, this study provides an expanded version of a prior solution to detecting alpha, beta, and gamma changes.
Media skepticism is defined as the degree to which individuals tend to disbelieve or discount the picture of reality presented in the mass media. Media skepticism is caused in part by the process by which individuals are confronted with discrepancies between their personal experience of reality and the reality portrayed in the media. As a result, they discount the media portrayal. Given this conceptualization, it was hypothesized that exposure to nonmediated information that conflicts with information gained from a media source would cause an increase in media skepticism. The hypothesis was tested in a controlled experiment. Results support the hypothesis and suggest that media skepticism may be a useful construct for future research in communication processes and effects.
Abstract Nestedness is a common property of communication, finance, trade, and ecological networks. In networks with high levels of nestedness, the link positions of low-degree nodes (those with few links) form nested subsets of the link positions of high-degree nodes (those with many links), leading to matrix representations with characteristic upper triangular or staircase patterns. Recent theoretical work has connected nestedness to the functionality of complex systems and has suggested that it is a structural by-product of the skewed degree distributions often seen in empirical data. However, mechanisms for generating nestedness remain poorly understood, limiting the connections that can be made between system processes and observed network structures. Here, we show that a simple probabilistic model based on phenology—the timing of copresences among interaction partners—can produce nested structures and correctly predict around two-thirds of interactions in two fish market networks and around one-third of interactions in 22 plant–pollinator networks. Notably, the links most readily explained by frequent actor copresences appear to form a backbone of nested interactions, with the remaining interactions attributable to opportunistic interactions or preferences for particular interaction partners that are not routinely available.
Abstract Recent breakthroughs in machine learning and big data analysis are allowing our online activities to be scrutinized at an unprecedented scale, and our private information to be inferred without our consent or knowledge. Here, we focus on algorithms designed to infer the opinions of Twitter users toward a growing number of topics, and consider the possibility of modifying the profiles of these users in the hope of hiding their opinions from such algorithms. We ran a survey to understand the extent of this privacy threat, and found evidence suggesting that a significant proportion of Twitter users wish to avoid revealing at least some of their opinions about social, political, and religious issues. Moreover, our participants were unable to reliably identify the Twitter activities that reveal one's opinion to such algorithms. Given these findings, we consider the possibility of fighting AI with AI, i.e., instead of relying on human intuition, people may have a better chance at hiding their opinion if they modify their Twitter profiles following advice from an automated assistant. We propose a heuristic that identifies which Twitter accounts the users should follow or mention in their tweets, and show that such a heuristic can effectively hide the user's opinions. Altogether, our study highlights the risk associated with developing machine learning algorithms that analyze people's profiles, and demonstrates the potential to develop countermeasures that preserve the basic right of choosing which of our opinions to share with the world.
