Network Visualization and Problem-Solving Support: A Cognitive Fit Study

This study examines the relative effectiveness of four different social network representations for improving human problem-solving accuracy and speed: node-link diagrams, adjacency matrices, tables, and text. Results suggest that visual network representations improve problem-solving accuracy and speed, compared with text. Among the visual representations, tables produced superior problem-solving outcomes for symbolic tasks and link-node diagrams produced superior problem-solving outcomes for spatial tasks. These results partially support a cognitive fit model of problem-solving support. There is not “one best way” to represent network data. Instead, it is important to match network representations and problem-solving tasks

The Battle for #Baltimore: Networked Counterpublics and the Contested Framing of Urban Unrest

A growing body of research suggests that Twitter has become a key resource for networked counterpublics to intervene in popular discourse about racism and policing in the United States. At the same time, claims that online communication necessarily results in polarized echo chambers are common. In response to these seemingly contrary impulses in communication research, we explore how the contested online network comprised of tweets about the April 2015 protests in Baltimore, Maryland, evolved as users constructed meaning and debated questions of protest and race. We find that even within this highly polarized debate, counterpublic frames found widespread support on Twitter. Progressive racial justice messages were advanced, in part, by brokers who worked across polarized subcommunities in the network to build mutual understanding and model effective strategies for reconciling disparate accounts of protest events

Women Tweet on Violence: From #YesAllWomen to #MeToo

From the earliest feminist press to Twitter, women have used technology to create and sustain narratives that demand attention and redress for gendered violence. Herein we argue that the #MeToo boom was made possible by the digital labor, consciousness-raising, and alternative storytelling created through the #YesAllWomen, #SurvivorPrivilege, #WhyIStayed, and #TheEmptyChair hashtag networks. Each of these hashtags highlight women’s experiences with interpersonal and institutionally-enabled violence and each was precipitated by high-profile news events. Alongside an examination of Twitter networks, we consider the social and cultural conditions that made each hashtag significant at particular moments, examining the ideological and political work members of these hashtag networks perform. We find that feminist hashtags have been successful in creating an easy-to-digest shorthand that challenges and changes mainstream narratives about violence and victimhood

Contrasting social and non-social sources of predictability in human mobility

Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual’s movement can be present in the mobility patterns of both acquaintances and strangers. We develop a “colocation” network to distinguish the mobility patterns of an ego’s social ties from those not socially connected to the ego but who arrive at a location at a similar time as the ego. Using entropic measures, we analyze and bound the predictive information of an individual’s mobility pattern and its flow to both types of ties. While the former generically provide more information, replacing up to 94% of an ego’s predictability, significant information is also present in the aggregation of unknown colocators, that contain up to 85% of an ego’s predictive information. Such information flow raises privacy concerns: individuals sharing data via mobile applications may be providing actionable information on themselves as well as others whose data are absent

igraph enables fast and robust network analysis across programming languages

Networks or graphs are widely used across the sciences to represent relationships of many kinds. igraph (https://igraph.org) is a general-purpose software library for graph construction, analysis, and visualisation, combining fast and robust performance with a low entry barrier. igraph pairs a fast core written in C with beginner-friendly interfaces in Python, R, and Mathematica. Over the last two decades, igraph has expanded substantially. It now scales to billions of edges, supports Mathematica and interactive plotting, integrates with Jupyter notebooks and other network libraries, includes new graph layouts and community detection algorithms, and has streamlined the documentation with examples and Spanish translations. Modern testing features such as continuous integration, address sanitizers, stricter typing, and memory-managed vectors have also increased robustness. Hundreds of bug reports have been fixed and a community forum has been opened to connect users and developers. Specific effort has been made to broaden use and community participation by women, non-binary people, and other demographic groups typically underrepresented in open source software.Comment: 5 pages, 4 figure

On minorities and outliers: The case for making Big Data small

In this essay, I make the case for choosing to examine small subsets of Big Data datasets—making big data small. Big Data allows us to produce summaries of human behavior at a scale never before possible. But in the push to produce these summaries, we risk losing sight of a secondary but equally important advantage of Big Data—the plentiful representation of minorities. Women, minorities and statistical outliers have historically been omitted from the scientific record, with problematic consequences. Big Data affords the opportunity to remedy those omissions. However, to do so, Big Data researchers must choose to examine very small subsets of otherwise large datasets. I encourage researchers to embrace an ethical, empirical and epistemological stance on Big Data that includes minorities and outliers as reference categories, rather than the exceptions to statistical norms