'Enclaves of exposure' : a conceptual viewpoint to explore cross-ideology exposure on social network sites

Previous studies indicate mixed results as to whether social media constitutes ideological echo chambers. This inconsistency may arise due to a lack of theoretical frames that acknowledge the fact that contextual and technological factors allow varying levels of cross-cutting exposure on social media. This study suggests an alternative theoretical lens, divergence of exposure – co-existence of user groups with varying degrees of cross-ideology exposure related to the same issue – as a notion that serves as an overarching perspective. We suggest that mediated spaces, such as social media groups, can serve as enclaves of exposure that offer affordances for formation of user groups irrespective of offline social distinctions. Yet social elements cause some of them to display more cross-ideology exchange than others. To establish this claim empirically, we examine two Facebook page user networks (‘Sri Lanka’s Killing Fields’ and ‘Sri Lankans Hate Channel 4’) that emerged in response to Sri Lanka’s Killing Fields, a controversial documentary broadcast by Channel 4 that accused Sri Lankan armed forces of human rights violation during the final stage of the separatist conflict in Sri Lanka. The results showed that the Facebook group network that supported the claims made by Channel 4 is more diverse in terms of ethnic composition, and is neither assortative nor disassortative across ethnicity, suggesting the presence of cross-ethnicity interaction. The pro-allegiant group was largely homogenous and less active, resembling a passive echo chamber. ‘Social mediation’ repurposes enclaves of exposure to represent polarized ideologies where some venues display cross-ideology exposure, while others resemble an ‘echo chamber’

Representational practices in VMT.

This chapter analyzes the interaction of three students working on mathematics problems over several days in a virtual math team. Our analysis traces out how successful collaboration in a later session was contingent upon the work of prior sessions, and shows how representational practices are important aspects of these participants’ mathematical problem solving. We trace the formation, transformation and refinement of one problem-solving practice—problem decomposition—and three representational practices—inscribe first solve second, modulate perspective and visualize decomposition. The analysis is of theoretical interest because it suggests that “situated cognition” is contingent upon not only the immediate situation but also the chronologically prior resources and associated practices; shows how inscriptions become representations for the group through an interactive process of interpretation; and sheds light on “group cognition” as an interactional process that is not identical to individual cognition yet that draws upon a dynamic interplay of individual contributions

Characterizing Communication Networks Associated with Political Hashtags.

Among the diverse forms of communication and information networks found in the Web 2.0 environment, “social” and “informational” communication networks have been characterized in terms of their network metrics. Although Twitter is partly based on relationships between actors, activity has been shown to reflect characteristics of information networks. This study examines activity in Twitter within spaces defined by hashtags on political topics. We gathered our own data on a hashtag associated with the 2012 Hawaii senatorial race and compared our results to those from other political hashtag networks, and to typical social and information networks as well as random graphs. Results show that hashtag-centered reply and retweet networks in this domain do not fall clearly into the social or informational categories. There appears to be a third kind of network associated with political debate. More generally, it may be productive to conceive of communication networks in terms of multidimensional characteristics rather than categories

A template for mapping emotion expression within hashtag publics

Current literature on networked publics lacks research that examines how emotions are mobilised around specific actors, and quantitative analysis of affective phenomena is limited to vanity metrics. We address this issue by developing a network analytic routine, which guides the attribution of emotions contained in hashtagged tweets to their sources and targets. The proposed template enables identification of networked inconsequentiality (i.e., inability to trigger dialogue), reply targets (i.e., individuals targeted in replies), and voice agents (i.e., senders of replicated utterances). We demonstrate this approach with two datasets based on the hashtags #Newzealand (n= 131,523) and #SriLanka (n= 145,868) covering two major incidents of terrorism related to opposing extremist ideologies. In addition to the methodological contribution, the study demonstrates that user-driven emergence of networked leadership takes place based on conventional structures of power in which individuals with high power and social status are likely to emerge as targets as well as sources of emotions

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript