-IntroductionGovernment officials, first responders and the general public are increasingly looking to social media as a critical communication and monitoring tool for disaster management. Due to widespread press coverage and ease of data collection, Twitter in particular is viewed as a critical communication media for disaster management. Key advantages include crowd-sourcing, speed, and the ability to access from mobile devices. Key potential disadvantages include bias in user base; inaccurate, false, and out-of-date information; and reduction in access due to cell-tower and electricity outages. The discussion of the strengths and weaknesses of Twitter for disaster management is typically based on anecdotes and isolated case studies of usage during a disaster or in its immediate aftermath. Such studies, however, do not address the value of Twitter data for early warning and planning vis-à-vis disasters such as Tsunamis. For early warning and planning the key is not how do people use Twitter once the disaster has occurred; but, how can the normal usage of Twitter be leveraged to support early warning and planning? The relevant questions include: How can we leverage the information garnered from everyday normal uses of Twitter to improve disaster planning and inform an effective early warning system? How should Twitter data be collected to support early warning and planning for disasters? How can information extracted from the normal usage of Twitter be leveraged to support early warning and planning? In this paper, we address such questions for Padang Indonesia.This research is part of a larger effort that seeks to provide a socio-technical system that supports early warning and planning for Tsunamis. A key feature of this socio-technical system is the use of Twitter to provide early warning that a Tsunami is coming (Landwehr, et al, this issue), to provide up-to-date information and guidance for movement to shelters (Santos et al, this issue), and to support planning for Tsunami's (Landwehr, et al, this issue). Effective planning and alerts require knowing where the local population is, who is on-line, what languages they can communicate in, whom they communicate with, and who the local opinion leaders are. Such information cannot be adequately gained from a census, as a census only provides information on average and is typically out-of-date. In theory, social media has the potential to improve the accuracy and timeliness of this "census" information and so improve planning and alerts.To determine whether or not such "census" information is attainable from Twitter, its accuracy, and what collection strategy improves the information quality, a baseline analysis of the use of Twitter in Indonesia in general, and Padang in particular was conducted. This baseline assessment was then used to identify the best data collection strategy, which was then implemented in the Tsunami Warning and Response Social Media System (TWRsms) (Landwehr, et al, this issue), and to identify the limits of and biases in Twitter data tha...
OBJECTIVEThe aim of this study was to compare information sharing and advice networks' relationships with patient safety outcomes.BACKGROUNDCommunication contributes to medical errors, but rarely is it clear what elements of communication are key.METHODSWe investigated relationships of information-sharing and advice networks to patient safety outcomes in 24 patient care units from 3 hospitals over 7 months. Web-based questionnaires completed via Android tablets provided data to create 2 networks using ORA, a social network analysis application. Each hospital provided nurse-sensitive patient safety outcomes.RESULTSIn both networks, medication errors correlated positively with node count and average distance and negatively with clustering coefficient. Density and weighted density negatively correlated with medication errors and falls in both networks. Eigenvector and total degree centrality correlated negatively with both safety outcomes, whereas betweenness centrality positively related to falls in the information-sharing network.CONCLUSIONTechnology-enabled social network analysis data collection is feasible and can provide managers actionable system-level information.
Construct is a multi-agent simulation tool that is commonly used to investigate dynamic behavior in complex socio-cultural systems. This technical report describes the parameters necessary to specify agents in the simulation, focusing especially on the features which help describe agent behavior. It also introduces a number of pre-defined agent classes, stock-agents which can be used to quickly build up a simulation. This document is intended both as an introduction to Construct model and as a reference guide for simulation experts and casual modelers.
Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.
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