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  1. Proceedings of the 3rd ACM SIGMM international workshop on Social media (WSM '11)
  2. Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors
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Mining social media: issues and challenges
Using social media to identify events
Large-scale social media mining in facebook
Multimodal location estimation on Flickr videos
Photo stream alignment for collaborative photo collection and sharing in social media
Sentiment analysis of social media content using N-Gram graphs
IM2MAP: deriving maps from georeferenced community contributed photo collections
Near2me: an authentic and personalized social media-based recommender for travel destinations
Laplacian adaptive context-based SVM for video concept detection
Tag suggestion and localization for web videos by bipartite graph matching
Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors
Chase display of social live streams (SOLISs)

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Why do we converse on social media?: an analysis of intrinsic and extrinsic network factors

Content Provider ACM Digital Library
Author De Choudhury, Munmun Sundaram, Hari
Abstract We are motivated in our work by the following question: what factors influence individual participation in social media conversations? Conversations around user posted content, is central to the user experience in social media sites, including Facebook, YouTube and Flickr. Therefore, understanding why people participate, can have significant bearing on fundamental research questions in social network and media analysis, such as, network evolution, and information diffusion. Our approach is as follows. We first identify several key aspects of social media conversations, distinct from both online forum discussions and other social networks. These aspects include intrinsic and extrinsic network factors. There are three factors intrinsic to the network: social awareness, community characteristics and creator reputation. The factors extrinsic to the network include: media context and conversational interestingness. Thereafter we test the effectiveness of each factor type in accounting for the observed participation of individuals using a Support Vector Regression based prediction framework. Our findings indicate that factors that influence participation depend on the media type: YouTube participation is different from a weblog such as Engadget. We further show that an optimal factor combination improves prediction accuracy of observed participation, by ~9--13% and ~8--11% over using just the best hypothesis and all hypotheses respectively. Implications of this work in understanding individual contributions in social media conversations, and the design of social sites in turn, are discussed.
Starting Page 53
Ending Page 58
Page Count 6
File Format PDF
ISBN 9781450309899
DOI 10.1145/2072609.2072625
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2011-11-30
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Participation Social media Conversations
Content Type Text
Resource Type Article
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