DocumentCode :
3739319
Title :
Optimizing the Popularity of Twitter Messages through User Categories
Author :
Rupert Lemahieu;Steven Van Canneyt;Cedric De Boom;Bart Dhoedt
Author_Institution :
Dept. of Inf. Technol., Ghent Univ. - iMinds, Ghent, Belgium
fYear :
2015
Firstpage :
1396
Lastpage :
1401
Abstract :
In this paper, we investigate how the category of a Twitter user can be used to better predict and optimize the popularity of tweets. The contributions of this paper are threefold. First, we compare the influence of content features on the popularity of tweets for different user categories. Second, we present a regression model to predict the popularity of tweets given the content features as input. To construct this model, we interpolate a generic regression model, which is trained on all data, and a category-specific model, which is only trained on tweets from users of the same category as the user of the given tweet. In this way we can combine the advantage of the robustness of a generic model, with the ability of category-specific models to pick up on category-specific influence of content features. The third contribution is the investigation of the feasibility of boosting the popularity of a tweet by setting up an experiment in which we proactively adapt content features in order to optimize the popularity of tweets. Based on this research, we conclude that the introduction of user categories leads to a more precise analysis and better predictions. In the hands-on experiment, we observed a gain in popularity by proactively adapting content features.
Keywords :
"Twitter","Predictive models","Media","Adaptation models","Entertainment industry","Data models","Tagging"
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2015 IEEE International Conference on
Electronic_ISBN :
2375-9259
Type :
conf
DOI :
10.1109/ICDMW.2015.39
Filename :
7395833
Link To Document :
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