Using coherencies to examine network evolution and co-evolution

Barnett, George A.; Jiang, Ke; Hammond, Jesse

doi:10.1007/s13278-015-0297-6

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…In a related note, depending on the nature of the data and goals of the researchers, other related models might be appropriate as well. For network evolution, where interactions are best modeled when they are put into different panels/ waves rather than in timestamps or order from interaction to interaction (e.g., Barnett, Jiang, & Hammond, 2015), stochastic actor-oriented modeling (Snijders, van de Bunt, & Steglich, 2010) may be useful to understand the factors that influence network reproduction and evolution. Similarly, if the researcher is interested in life cycles (e.g., Gersick, 1988) or different group phases (e.g., Moreland & Levine, 1988), than various longitudinal sequence analysis methods (Cornwell, 2015) might be useful too.…”

Section: When and How To Use Remmentioning

confidence: 99%

An illustration of the relational event model to analyze group interaction processes.

Pilny

Schecter²,

Poole

et al. 2016

Group Dynamics: Theory, Research, and Practice

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A fundamental assumption in the study of groups is that they are constituted by various interaction processes that are critical to survival, success, and failure. However, there are few methods available sophisticated enough to empirically analyze group interaction. To address this issue, we present an illustration of relational event modeling (REM). A relational event is a "discrete event generated by a social actor and directed toward 1 or more targets" (Butts, 2008, p. 159). Because REM provides a procedure to model relational event histories, it has the ability to figure out which patterns of group interaction are more or less common than others. For instance, do past patterns of interaction influence future interactions, (e.g., reciprocity), do individual attributes make it more likely that individuals will create interactions (e.g., homophily), and do specific contextual factors influence interaction patterns (e.g., complexity of a task)?The current paper provides an REM tutorial from a multiteam system experiment in which 2 teams navigated a terrain to coordinate their movement to arrive at a common destination point. We use REM to model the dominant patterns of interactions, which included the principle of inertia (i.e., past contacts tended to be future contacts) and trust (i.e., group members interacted with members they trusted more) in the current example. An online appendix that includes the example data set and source code is available as supplemental material in order to demonstrate the utility REM, which mainly lies in its ability to model rich, time-stamped trace data without severely simplifying it (e.g., aggregating interactions into a panel).

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Section: When and How To Use Remmentioning

confidence: 99%

An illustration of the relational event model to analyze group interaction processes.

Pilny

Schecter²,

Poole

et al. 2016

Group Dynamics: Theory, Research, and Practice

View full text Add to dashboard Cite

show abstract

“…The slope of the phase spectrum can also be examined to ascertain the time lag to determine potential direction of causality between highly co-evolved pair of nodes. This approach has been used to examine the co-evolutions of international news networks and news frames (Barnett et al, 2015 ; Barnett and Algara, 2019 ) and the Trade War discourse on Twitter (Jiang and Xu, 2021 ).…”

Section: Method: Analyzing the Dynamics Of Social Media Texts Via Coh...mentioning

confidence: 99%

“…. Method: analyzing the dynamics of social media texts via coherency networks Coherency measures the association between two time series, indicating how well they are correlated (Barnett et al, 2015). Therefore, when analyzing social media texts, it is necessary to calculate time series values that describe the performance of words in the texts.…”

mentioning

confidence: 99%

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate

Jiang,

2023

Front. Res. Metr. Anal.

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IntroductionThis study examines the associations between time series, termed “coherency,” using spectral analysis. Coherence squared, analogous to the squared correlation coefficient, serves as a metric to quantify the degree of interdependence and co-evolution of individual nodes.MethodsWe utilized spectral analysis to compute coherence squared, unveiling relationships and co-evolution patterns among individual nodes. The resultant matrix of these relationships was subjected to network analysis.ResultsBy conducting a case study analyzing tweets associated with the co-hashtags #StopAsianHate and #BlackLivesMatter, we present a novel approach utilizing coherency network analysis to investigate the dynamics of social media text. Frequency domain analysis aided in calculating coherence squared, effectively illustrating the relationships and co-evolution of individual nodes. Furthermore, an analysis of the phase spectrum's slope facilitated the determination of time lag and potential causality direction between highly co-evolved node pairs.DiscussionOur findings underline the potential of coherency network analysis in comprehending the intricate dynamics of social media text. This approach offers valuable insights into how topics, sentiments, or movements manifest and evolve within the digital realm. Future research should explore diverse datasets and domains to broaden our understanding of this novel analytical technique.

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“…Communication scholars have conceptualized media systems as networks (Ognyanova and Monge, 2013). One stream of research has explored the evolution of media systems by tracking longitudinal changes in the global hyperlink (Barnett et al, 2016) and news (Barnett et al, 2015) networks. These studies uncover the structure of Internet infrastructure and content through a media-centric perspective.…”

Section: Evolution Of Audience Duplication: a Network Perspectivementioning

confidence: 99%

Evolution of audience duplication networks among social networking sites: Exploring the influences of preferential attachment, audience size, and niche width

2021

New Media & Society

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This study examines the evolution of social networking sites (SNSs) from a networked audience duplication perspective. Guided by social network theory, the theory of double jeopardy, and niche theory, this study proposes an integrated framework to explain the evolution of SNS choices of the US audience between 2016 and 2019. Shared traffic data were retrieved from comScore’s Media Metrix Multi-Platform database. The empirical results of the separable temporal exponential random graph model (STERGM) confirm that preferential attachment, audience size, and niche width significantly drive the likelihood of tie formation and dissolution in the evolving audience duplication network. These effects hold true even when other endogenous structural features and exogenous nodal attributes are taken into account. Theoretical implications for the networked media landscape are discussed.

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Using coherencies to examine network evolution and co-evolution

Cited by 7 publications

References 24 publications

An illustration of the relational event model to analyze group interaction processes.

An illustration of the relational event model to analyze group interaction processes.

Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate

Evolution of audience duplication networks among social networking sites: Exploring the influences of preferential attachment, audience size, and niche width

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