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The Message in the Medium: Predicting influence and attention using attitude annotation and salience modeling

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: D13PC00041
Agency Tracking Number: D12B-002-0033
Amount: $100,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: ST12B-002
Solicitation Number: 2012.B
Solicitation Year: 2012
Award Year: 2013
Award Start Date (Proposal Award Date): 2013-02-13
Award End Date (Contract End Date): 2013-08-12
Small Business Information
12801 Worldgate Drive Suite 800
Herndon, VA -
United States
DUNS: 965542876
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 R.K. Prasanth
 Sr. Member of the Technical Staff
 (571) 612-8345
Business Contact
 Jordan Price
Title: Director of Contracts
Phone: (571) 612-8361
Research Institution
 University of Michigan
 Rebecca O'Brien
4365 North Quad 105 S. State Street
Ann Arbor, MI 48109-
United States

 (734) 615-9602
 Nonprofit College or University

We propose scalable, linear-time models and algorithms for online social network analysis that remedy limitations of current state-of-the-art models by creating a capability for tracking, predicting affiliations, and roles of participants in and across online communities. The model goes beyond simple clustering and community detection by using more of the message in the media, i.e. by taking into account the semantic, pragmatic and temporal content of computer-mediated-communication by an individual and within a community. Using parallel models of individual participants and groups, we propose to augment previous algorithms by incorporating a multi-dimensional network representation incorporating attitudes of participants and groups toward entities, issues, beliefs, and other participants. Further, individual participant models will represent their roles in the community based on the nature of their online interactions. This approach will reveal social ties among group participants and the relative strength of their group affiliations. The constructed representations will reveal user-specific and group-prevalent themes, sentiments, activities, and roles. This will allow us to predict patterns of group formation and dissolution, and to predict an individual participant's likelihood of initiating or maintaining an affiliation with a group based on a mathematical comparison of that individual's profile with the group's profile.

* Information listed above is at the time of submission. *

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