The Journal of Machine Learning Research
Red Opal: product-feature scoring from reviews
Proceedings of the 8th ACM conference on Electronic commerce
Predicting response to political blog posts with topic models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Using a model of social dynamics to predict popularity of news
Proceedings of the 19th international conference on World wide web
Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A peek into the future: predicting the evolution of popularity in user generated content
Proceedings of the sixth ACM international conference on Web search and data mining
Characterizing the life cycle of online news stories using social media reactions
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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In this paper, we deal with the problem of predicting how much attention a newly submitted post would receive from fellow community members of closed communities in social networking sites. Though the concept of attention is subjective, the number of comments received by a post serves as a very good indicator of the same. Unlike previous work which primarily made use of either content features or the network features (friendship links on the network), we exploit both the content features and community level features (for instance, what time of the day is the community more active) for tackling this problem. Further, we focus on dedicated pages of corporate brands on social media websites and accordingly extract important features from the content and community activity of such brand pages. The attention prediction task finds direct application in the listening, monitoring and engaging activities of the businesses that have such brand-pages. In this paper, we formulate the problem of attention prediction on social media brand pages. We further propose Attention Prediction (AP) framework which integrates the various features that influence the attention received by a post using classification and regression based approaches. Experimental results on real world data extracted from some highly active brand pages on Facebook demonstrate the efficacy of the proposed framework.