Spillovers in networks of user generated content : evidence from 23 natural experiments on Wikipedia


Kummer, Michael E.


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URL: https://ub-madoc.bib.uni-mannheim.de/35260
URN: urn:nbn:de:bsz:180-madoc-352600
Document Type: Working paper
Year of publication: 2013
The title of a journal, publication series: ZEW Discussion Papers
Volume: 13-098
Place of publication: Mannheim
Publication language: English
Institution: Sonstige Einrichtungen > ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
MADOC publication series: Veröffentlichungen des ZEW (Leibniz-Zentrum für Europäische Wirtschaftsforschung) > ZEW Discussion Papers
Subject: 020 Library and information sciences
Classification: JEL: L17 , D62 , D85 , D29,
Keywords (English): Social media , information , knowledge , spillovers , networks , natural experiment
Abstract: Endogeneity in network formation hinders the identification of the role that social networks play in generating spillovers, peer effects and other externalities. This paper tackles this problem and investigates how the link network between articles on the German Wikipedia influences the attention and content generation individual articles receive. Identification exploits local exogenous shocks on a small number of nodes in the network. It can thus avoid the usually required, but strong, assumptions of exogenous observed characteristics and link structure in networks. Exogenous variation is generated by natural and technical disasters or by articles being featured on the German Wikipedia’s start page. The effects on neighboring pages are substantial; I observe an increase of almost 100 percent in terms of both views and content generation. The aggregate effect over all neighbors is also large: I find that a view on a treated article converts one for one into a view on a neighboring article. However, the resulting content generation is small in absolute terms. My approach also applies if, due to a lack of network data, identification through partial overlaps in the network structure fails (e.g. in classrooms). It helps bridge the gap between the experimental and social network literatures on peer effects.

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Kummer, Michael E. (2013) Spillovers in networks of user generated content : evidence from 23 natural experiments on Wikipedia. Open Access ZEW Discussion Papers Mannheim 13-098 [Working paper]
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