Abstract
Twitter and other social networks have become a fundamental source of information and a powerful tool to spread ideas and opinions. A crucial step in understanding the mechanisms that drive information diffusion in Twitter, is to study the influence of the social neighborhood of a user in the construction of her retweeting preferences. In particular, to what extent can the preferences of a user be predicted given the preferences of her neighborhood.
We build our own sample graph of Twitter users and study the problem of predicting retweets from a given user based on the retweeting behavior occurring in her second-degree social neighborhood (followed and followed-by-followed). We manage to train and evaluate user-centered binary classification models that predict retweets with an average F1 score of \(87.6\%\), based purely on social information, that is, without analyzing the content of the tweets.
For users getting low scores with such models (on a tuning dataset), we improve the results by adding features extracted from the content of tweets. To do so, we apply a Natural Language Processing (NLP) pipeline including a Twitter-specific adaptation of the Latent Dirichlet Allocation (LDA) probabilistic topic model.
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Notes
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Likes are represented by a small heart and are used to show appreciation for a tweet. The number of “likes” is the number of the users which express it for a given tweet.
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For Support Vector Classifier, name of classical Support Vector Machines (SVM) in scikit-learn.
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- 5.
We denote with social+lda10 the models that combine social features and classical LDA features with 10 topics. Similar notation applies for 20 topics and the TwitterLDA variation.
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Celayes, P.G., Domínguez, M.A. (2018). Prediction of User Retweets Based on Social Neighborhood Information and Topic Modelling. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Computational Intelligence. MICAI 2017. Lecture Notes in Computer Science(), vol 10633. Springer, Cham. https://doi.org/10.1007/978-3-030-02840-4_12
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DOI: https://doi.org/10.1007/978-3-030-02840-4_12
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