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Information diffusion through blogspace
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A tutorial on support vector regression
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The dynamics of viral marketing
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Feedback for guiding reflection on teamwork practices
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Analyzing patterns of user content generation in online social networks
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Meme-tracking and the dynamics of the news cycle
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Learning influence probabilities in social networks
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Emerging topic detection on Twitter based on temporal and social terms evaluation
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
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Semi-supervised recognition of sarcastic sentences in Twitter and Amazon
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Patterns of temporal variation in online media
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Enhanced sentiment learning using Twitter hashtags and smileys
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Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach
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Influential nodes in a diffusion model for social networks
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Will this #hashtag be popular tomorrow?
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Discover breaking events with popular hashtags in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
Classification-Based prediction on the retweet actions over microblog dataset
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Meaning as collective use: predicting semantic hashtag categories on twitter
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No country for old members: user lifecycle and linguistic change in online communities
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Spatio-temporal dynamics of online memes: a study of geo-tagged tweets
Proceedings of the 22nd international conference on World Wide Web
On predicting Twitter trend: factors and models
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The social media genome: modeling individual topic-specific behavior in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Exploiting hashtags for adaptive microblog crawling
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Spatio-temporal meme prediction: learning what hashtags will be popular where
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The bursty dynamics of the Twitter information network
Proceedings of the 23rd international conference on World wide web
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Current social media research mainly focuses on temporal trends of the information flow and on the topology of the social graph that facilitates the propagation of information. In this paper we study the effect of the content of the idea on the information propagation. We present an efficient hybrid approach based on a linear regression for predicting the spread of an idea in a given time frame. We show that a combination of content features with temporal and topological features minimizes prediction error. Our algorithm is evaluated on Twitter hashtags extracted from a dataset of more than 400 million tweets. We analyze the contribution and the limitations of the various feature types to the spread of information, demonstrating that content aspects can be used as strong predictors thus should not be disregarded. We also study the dependencies between global features such as graph topology and content features.