Introduction to Network Analysis of Digital and Social Media Minitrack

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Social Network Analysis Using Author Co-Citation Data

This study examines the social network of scholars in the field of Communication by using author co-citation data. A matrix containing the number of co-cited documents between pairs of authors is created for social network analysis of scholars who are on the editorial board of Journal of Communication, and the networked map of the scholars is used to visualize the knowledge structure of the field by identifying groups of authors who are more central than others. Social Science Citation Index (SSCI) is used to collect the author co-citation data, and UCInet is employed for social network analysis as well as network visualization

Omaha Unemployment Feasibility Study: Final Report

The research reported here was designed to test the feasibility of a practical approach to the reduction of unemployment, particularly among Negroes. The need for the particular kind of emphasis used in this approach was recently stated by the director of the U.S. Employment Service in pointing out the need to obtain more understanding of the things that make the community tick, that keep it from solving its problems, and that lead to the discovery of the real barriers to coordination..

A generalized method for multiple robotic manipulator programming applied to vertical-up welding

The application is described of a weld programming algorithm for vertical-up welding, which is frequently desired for variable polarity plasma arc welding (VPPAW). The Basic algorithm performs three tasks simultaneously: control of the robotic mechanism so that proper torch motion is achieved while minimizing the sum-of-squares of joint displacement; control of the torch while the part is maintained in a desirable orientation; and control of the wire feed mechanism location with respect to the moving welding torch. Also presented is a modification of this algorithm which permits it to be used for vertical-up welding. The details of this modification are discussed and simulation examples are provided for illustration and verification

Coupled catastrophes: sudden shifts cascade and hop among interdependent systems

An important challenge in several disciplines is to understand how sudden changes can propagate among coupled systems. Examples include the synchronization of business cycles, population collapse in patchy ecosystems, markets shifting to a new technology platform, collapses in prices and in confidence in financial markets, and protests erupting in multiple countries. A number of mathematical models of these phenomena have multiple equilibria separated by saddle-node bifurcations. We study this behavior in its normal form as fast--slow ordinary differential equations. In our model, a system consists of multiple subsystems, such as countries in the global economy or patches of an ecosystem. Each subsystem is described by a scalar quantity, such as economic output or population, that undergoes sudden changes via saddle-node bifurcations. The subsystems are coupled via their scalar quantity (e.g., trade couples economic output; diffusion couples populations); that coupling moves the locations of their bifurcations. The model demonstrates two ways in which sudden changes can propagate: they can cascade (one causing the next), or they can hop over subsystems. The latter is absent from classic models of cascades. For an application, we study the Arab Spring protests. After connecting the model to sociological theories that have bistability, we use socioeconomic data to estimate relative proximities to tipping points and Facebook data to estimate couplings among countries. We find that although protests tend to spread locally, they also seem to "hop" over countries, like in the stylized model; this result highlights a new class of temporal motifs in longitudinal network datasets.Comment: 20 pages, 4 figures, plus a 6-page supplementary material that contains 5 figures. Accepted at Journal of the Royal Society Interfac

Identifying Vaccine Hesitant Communities on Twitter and their Geolocations: A Network Approach

Vaccine misinformation online may contribute to the increase of anti-vaccine sentiment and vaccine-hesitant behaviors. Social network data was used to identify Twitter vaccine influencers, their online twitter communities, and their geolocations to determine pro-vaccine and vaccine-hesitant online communities. We explored 139,433 tweets and identified 420 vaccine Twitter influencers—opinion leaders and assessed 13,487 of their tweets and 7,731 of their connections. Semantic network analysis was employed to determine twitter conversation themes. Results suggest that locating social media influencers is an efficient way to identify and target vaccine-hesitant communities online. We discuss the implications of using this process for public health education and disease management