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Analyzing the Retweeting Behavior of Influencers to Predict Popular Tweets, with and Without Considering their Content

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Information Management and Big Data (SIMBig 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 898))

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Abstract

Twitter and social networks in general, participate more and more in everyday life. This is why they have become a fundamental source of information that reflects the ideas and opinions of their users. This paper shows how the most influential users, called influencers, can be decisive in defining whether a publication becomes popular or not, regardless of its content. To achieve this, we build a dataset of Spanish-writing users sampled from Twitter, along with the content generated and shared by them within a year. In a first phase, we use different algorithms to detect users who are “influencers”. In a second phase, we train a binary classifier to predict if a given tweet will be a trending publication, based on information about the activity of the influencers on the given tweet. We obtain a model with an \(F_1\)-score close to \(79\%\), based on the retweeting behavior of a \(10\%\) of the users dataset considered as influencers. Finally, we add two Natural Language Processing (NLP) techniques to analyze the content: Twitter-LDA topic modeling, and FastText word embeddings. While both models alone have an \(F_1\) of less than \(50\%\) for trending prediction, FastText combined with the social model reaches an \(86.7\%\) score. We conclude that while analyzing the content can help to predict the popularity of a tweet, the influence of a user’s environment in the retweeting decision is surprisingly high.

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Notes

  1. 1.

    We assume that acceptance is the most usual way to use a retweet. However, it is true that not always a retweet represents acceptance, in some cases a retweet could be used to be ironic about a publication, or also, to make visible some topic with which we disagree.

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Authors and Affiliations

  1. FaMAF, Universidad Nacional de Cordoba, Córdoba, Argentina

    Matías Gastón Silva, Martín Ariel Domínguez & Pablo Gabriel Celayes

Authors
  1. Matías Gastón Silva
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  2. Martín Ariel Domínguez
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  3. Pablo Gabriel Celayes
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Corresponding authors

Correspondence to Matías Gastón Silva , Martín Ariel Domínguez or Pablo Gabriel Celayes .

Editor information

Editors and Affiliations

  1. Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Juan Antonio Lossio-Ventura

  2. Fondazione Bruno Kessler, Trento, Italy

    Denisse Muñante

  3. Facultad de Ingeniería, University of the Pacific, Jesús María, Lima, Peru

    Hugo Alatrista-Salas

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Silva, M.G., Domínguez, M.A., Celayes, P.G. (2019). Analyzing the Retweeting Behavior of Influencers to Predict Popular Tweets, with and Without Considering their Content. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-11680-4_9

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  • Online ISBN: 978-3-030-11680-4

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