118,794 research outputs found
Internet Information and Communication Behavior during a Political Moment: The Iraq War, March 2003
This article explores the Internet as a resource for political information and communication in March 2003, when American troops were first sent to Iraq, offering us a unique setting of political context, information use, and technology. Employing a national survey conducted by the Pew Internet & American Life project. We examine the political information behavior of the Internet respondents through an exploratory factor analysis; analyze the effects of personal demographic attributes and political attitudes, traditional and new media use, and technology on online behavior through multiple regression analysis; and assess the online political information and communication behavior of supporters and dissenters of the Iraq War. The factor analysis suggests four factors: activism, support, information seeking, and communication. The regression analysis indicates that gender, political attitudes and beliefs, motivation, traditional media consumption, perceptions of bias in the media, and computer experience and use predict online political information behavior, although the effects of these variables differ for the four factors. The information and communication behavior of supporters and dissenters of the Iraq War differed significantly. We conclude with a brief discussion of the value of "interdisciplinary poaching" for advancing the study of Internet information practices
A Method to Assess the Organizing Behaviors Used in Physicians\u27 Counseling of Standardized Parents after Newborn Genetic Screening
Well-organized conversation can improve peopleâs ability to comprehend and retain information. As part of a long-term effort to adapt Quality Improvement techniques for communication, we developed an explicit criteria method to assess usage of three organizing behaviors (OBs): âopening behaviorsâ to establish goals; âstructuring behaviorsâ to guide patients through conversation; and âemphasizing behaviorsâ that signal a need for attention. Pairs of abstractors independently reviewed transcripts in a demonstration sample of conversations between physicians and standardized parents after newborn screening identifies carrier status for sickle cell disease. Criteria for at least one OB were identified in 50/84 transcripts (60%), including 27 with at least one opening behavior (32%), 5 with at least one structuring behavior (6%), and 38 with at least one emphasizing behavior (45%). The limited number of OBs raises concern about communication after newborn screening. Assessment and improvement of OB usage may improve understanding and allow parents to more actively participate in health care
Effects of temporal correlations on cascades: Threshold models on temporal networks
A person's decision to adopt an idea or product is often driven by the
decisions of peers, mediated through a network of social ties. A common way of
modeling adoption dynamics is to use threshold models, where a node may become
an adopter given a high enough rate of contacts with adopted neighbors. We
study the dynamics of threshold models that take both the network topology and
the timings of contacts into account, using empirical contact sequences as
substrates. The models are designed such that adoption is driven by the number
of contacts with different adopted neighbors within a chosen time. We find that
while some networks support cascades leading to network-level adoption, some do
not: the propagation of adoption depends on several factors from the frequency
of contacts to burstiness and timing correlations of contact sequences. More
specifically, burstiness is seen to suppress cascades sizes when compared to
randomised contact timings, while timing correlations between contacts on
adjacent links facilitate cascades.Comment: 9 pages, 7 figures, Published versio
Characterizing the community structure of complex networks
Community structure is one of the key properties of complex networks and
plays a crucial role in their topology and function. While an impressive amount
of work has been done on the issue of community detection, very little
attention has been so far devoted to the investigation of communities in real
networks. We present a systematic empirical analysis of the statistical
properties of communities in large information, communication, technological,
biological, and social networks. We find that the mesoscopic organization of
networks of the same category is remarkably similar. This is reflected in
several characteristics of community structure, which can be used as
``fingerprints'' of specific network categories. While community size
distributions are always broad, certain categories of networks consist mainly
of tree-like communities, while others have denser modules. Average path
lengths within communities initially grow logarithmically with community size,
but the growth saturates or slows down for communities larger than a
characteristic size. This behaviour is related to the presence of hubs within
communities, whose roles differ across categories. Also the community
embeddedness of nodes, measured in terms of the fraction of links within their
communities, has a characteristic distribution for each category. Our findings
are verified by the use of two fundamentally different community detection
methods.Comment: 15 pages, 20 figures, 4 table
Modeling bursts and heavy tails in human dynamics
Current models of human dynamics, used from risk assessment to
communications, assume that human actions are randomly distributed in time and
thus well approximated by Poisson processes. We provide direct evidence that
for five human activity patterns the timing of individual human actions follow
non-Poisson statistics, characterized by bursts of rapidly occurring events
separated by long periods of inactivity. We show that the bursty nature of
human behavior is a consequence of a decision based queuing process: when
individuals execute tasks based on some perceived priority, the timing of the
tasks will be heavy tailed, most tasks being rapidly executed, while a few
experiencing very long waiting times. We discuss two queueing models that
capture human activity. The first model assumes that there are no limitations
on the number of tasks an individual can hadle at any time, predicting that the
waiting time of the individual tasks follow a heavy tailed distribution with
exponent alpha=3/2. The second model imposes limitations on the queue length,
resulting in alpha=1. We provide empirical evidence supporting the relevance of
these two models to human activity patterns. Finally, we discuss possible
extension of the proposed queueing models and outline some future challenges in
exploring the statistical mechanisms of human dynamics.Comment: RevTex, 19 pages, 8 figure
- âŠ