System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Networks: An Introduction
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Proceedings of the fifth ACM international conference on Web search and data mining
Will this #hashtag be popular tomorrow?
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.