ORA User's Guide 2011

Carley, Kathleen M.; Reminga, Jeff; Storrick, Jon; Columbus, Dave

doi:10.21236/ada550789

Cited by 32 publications

(9 citation statements)

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“…Once the messages had been retrieved, Netlytic, a web-based system for automated text analysis and discovery of social networks (Gruzd, 2011) from electronic communication was used to discover who mentioned whom in this data set, and to build a communication network based on the discovered connections. Figure 1 shows the resulting network visualized using ORA, a popular software package designed for social network analysis (SNA; Carley et al, 2007). More connected nodes appear in the center of this network, forming a relatively dense core, demonstrating that there are a number of people (i.e., not just a few) who are actively engaged in discussions with one another, using the hashtag #elxn41.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Investigating Political Polarization on Twitter: A Canadian Perspective

Gruzd¹,

Roy²

2014

Policy &Amp; Internet

240

165

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This article investigates political polarization in social media by undertaking social network analysis of a sample of 5,918 tweets posted by 1,492 Twitter users during the 2011 Canadian Federal Election. On the one hand, we observed a clustering effect around shared political views among supporters of the same party in the Twitter communication network, suggesting that there are pockets of political polarization on Twitter. At the same time, there was evidence of cross‐ideological connections and exchanges, which may facilitate open, cross‐party, and cross‐ideological discourse, and ignite wider debate and learning as they are observed by nonaffiliated voters and the media at large. However, what appeared to be far less likely was any increased willingness or tendency for committed partisans to shift their allegiances as a result of their Twitter engagements, and we postulate that Twitter usage at present is likely to further embed partisan loyalties during electoral periods rather than loosen them; a dynamic that would seemingly contribute to political polarization.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Investigating Political Polarization on Twitter: A Canadian Perspective

Gruzd¹,

Roy²

2014

Policy &Amp; Internet

240

165

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“…In this paper, we use the datasets provided by WMN deployments: UCSB MeshNet' and 'MIT's Roofnet'. To perform social analysis on these networks, we use ORA [9] which is an interactive network analysis tool that maintains the internal structure of an organization/social network as a set of agents, tasks, and resources.…”

Section: Wireless Mesh Network (Wmn) Datasetsmentioning

confidence: 99%

What if wireless routers were social? approaching wireless mesh networks from a social networks perspective

Kas

Appala

Wang

et al. 2012

IEEE Wireless Commun.

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Wireless Mesh Networks (WMNs) consist of radio nodes organized in a mesh topology for serving wireless mesh clients to communicate with one another or to connect to the Internet. Nodes in a mesh network can communicate with each other either directly or through one or more intermediate nodes, similar to social networks. WMNs share many common properties with social networks. We first identify the differences and similarities between social networks and WMNs and then use metrics that are typically used for social network analysis (SNA) to assess real WMNs. Analyzing real WMN data collected from the UCSB MeshNet and MIT Roofnet testbeds reveals that using SNA metrics are helpful in designing WMNs with better performance. We demonstrate the validity of our conclusions and this new approach by focusing on two sample applications of social networks: network reliability assessment and channel access scheduling.

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“…The data layer contains specialized tools and environments that handle raw inputs. The open-source CASOS/ORA environment contains tools that replicate most of the more expensive vendor-tailored solutions [Carley et al, 2011]. A data management system must be capable of populating node attributes from vast amounts of dynamic written, audio, video, structured, and unstructured data, including data from other intelligence domains.…”

Section: Layered Network Intelligencementioning

confidence: 99%