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Activity Number: 402
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #312331 View Presentation
Title: Analysis of Multiview Legislative Networks with Structured Matrix Factorization: Does Twitter Influence Translate to the Real World?
Author(s): Shawn Mankad*+ and George Michailidis
Companies: and University of Michigan
Keywords: Matrix factorization ; Twitter ; Influence ; Networks
Abstract:

Do Twitter relations between legislators predict their real-world position and influence? We investigate this question by analyzing multiple Twitter networks that feature different types of link relations between the Members of Parliament (MPs) in the United Kingdom. We develop and apply a matrix factorization technique that allows the analyst to emphasize nodes with contextual local network structures by specifying network statistics that guide the factorization solution. Relying only on link relation data, we find that important MPs in Twitter networks are associated with real-world leadership positions, and that rankings from the proposed method can be predictive of future media headlines. Comparable results are found when analyzing a similar dataset involving Twitter networks among legislators in the national parliament of Ireland.


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