Computational social science: Making the links

Giles, Jim

doi:10.1038/488448a

Cited by 108 publications

(55 citation statements)

References 11 publications

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“…Many early contributions within the computational social sciences have been criticised for simply confirming already existing social theory [19] and few have been published in traditional social science journals or attempted to engage deeply with social science literature [51]. One explanation is that to date, datadriven social sciences studies have largely been defined by, and conducted within, computer science departments [19]. Too often there is an emphasis on either using existing methods, or developing new generalizable computational techniques, without adequate consideration of the degree to which such solutions deliver meaningful or important findings [52].…”

Section: Rethinking Visualization Design Modelsmentioning

confidence: 99%

Section: Rethinking Visualization Design Modelsmentioning

confidence: 99%

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Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization

Wood

Beecham

Dykes

2014

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We reflect on a four-year engagement with transport authorities and others involving a large dataset describing the use of a public bicycle-sharing scheme. We describe the role visualization of these data played in fostering engagement with policy makers, transport operators, the transport research community, the museum and gallery sector and the general public. We identify each of these as 'channels' -evolving relationships between producers and consumers of visualization -where traditional roles of the visualization expert and domain expert are blurred. In each case, we identify the different design decisions that were required to support each of these channels and the role played by the visualization process. Using chauffeured interaction with a flexible visual analytics system we demonstrate how insight was gained by policy makers into gendered spatio-temporal cycle behaviors, how this led to further insight into workplace commuting activity, group cycling behavior and explanations for street navigation choice. We demonstrate how this supported, and was supported by, the seemingly unrelated development of narrative-driven visualization via TEDx, of the creation and the setting of an art installation and the curating of digital and physical artefacts. We assert that existing models of visualization design, of tool/technique development and of insight generation do not adequately capture the richness of parallel engagement via these multiple channels of communication. We argue that developing multiple channels in parallel opens up opportunities for visualization design and analysis by building trust and authority and supporting creativity. This rich, non-sequential approach to visualization design is likely to foster serendipity, deepen insight and increase impact. Permanent repository link

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Section: Rethinking Visualization Design Modelsmentioning

confidence: 99%

Section: Rethinking Visualization Design Modelsmentioning

confidence: 99%

Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization

Wood

Beecham

Dykes

2014

IEEE Trans. Visual. Comput. Graphics

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“…Next, we show one practical application in public health domain as an exemplary case. Over the past few years, many computational studies highlighted the importance of social data to tackle public health challenges (10,47). Among them, epidemic spreading is perhaps one of the most prominent (48)(49)(50)(51).…”

Section: B Amentioning

confidence: 99%

Scaling identity connects human mobility and social interactions

DeVillé

Eagle

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality.human mobility | social interactions | mobile phone data | social networks | spatial networks O ver the past few years, we have witnessed tremendous progress in uncovering patterns behind human mobility (1-7) and social networks (8-10), owing partly to the increasing availability of large-scale datasets capturing human behavior in a new level of detail, resolution, and scale (11,12). Building on rich, fundamental literature from the social sciences (13)(14)(15)(16)(17)(18)(19), these data offer a huge opportunity for research, fueling concomitant advances in areas of both human mobility and social networks with profound consequences in broad domains. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws. Indeed, previous studies have shown that human travels adhere to spatial constraints (20), characterized by levy flights and continuous time random walk models (1, 2, 4), a scaling law that has proven to be critical in various phenomena driven by human mobility, from spread of viruses (21-23) to migrations (2, 6) and emergency response (24-26). In another related yet distinct area, there has been much empirical evidence about the geographic effect on communication patterns (20), documenting that the probability for two individuals to communicate decays with distance, following power law distributions (20,(27)(28)(29)(30). This robust pattern plays an important role in navigating the social network (31), from routing (32, 33) to search of experts (34, 35) to spread of information (27, 36) and innovations (37). Although human movements and social interactions bear high-level similarities in the role spatial distance plays, and are often referred to as two prominent examples of spatial networks (20), they remai...

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“…Traces of online communication such as Facebook or Twitter messaging offer another important source of data to understand peoples' interests, political and social opinions and sharing dynamics (Adamic and Glance 2005;Grabowicz et al 2012;Watts 2007) offering new opportunities for research that would have been impossible otherwise. Within this scenario, the approach to social science has been radically transformed to such an extent that the scientific community refers to the analysis of social networks as a new emerging type of science: the network science or computational social science (Barabási 2003;Giles 2012;Lazer et al 2009;Watts 2004). At the same time, social science and social network analysis have become central tools for many organizational consultants and companies seeking to understand and model the connections between patterns of interactions and business outcomes such as job performance or satisfaction, adoption of new practices and technologies, information sharing, creation and propagation of new ideas/products/services.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Temporal Patterns of Communication in Social Networks

Miritello¹

2013

Springer Theses

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Computational social science: Making the links

Cited by 108 publications

References 11 publications

Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization

Moving beyond sequential design: Reflections on a rich multi-channel approach to data visualization

Scaling identity connects human mobility and social interactions

Temporal Patterns of Communication in Social Networks

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