Improved Twitter Virality Prediction using Text and RNN-LSTM

  • Christian E. Maldonado-Sifuentes CIC-IPN
  • Grigory Sidorov
  • Olga Kolesnikova
Keywords: Twitter infuence, Twitter virality, Twitter popularity, Applied deep learning, Social networks

Abstract

The matter of influence and virality in social media has been studied since the popularity explosion of these platforms. A gargantuan amount of news and political messaging transits through Twitter every second, making it a formidable force for the propagation of information. In order to stay competitive, traditional media needs to participate in these platforms and attain influence. We propose a method to predict the influence of news tweets. To this end we use several thousand tweets to train a RNN-LSTM to classify news tweets as influential or not influential using a corpus of 5000 automatically labeled tweets according to their influence. Our method reaches an F1 of 0.83, while training and classifying in under 300 seconds.

Published
2021-09-11
How to Cite
Maldonado-Sifuentes, C. E., Sidorov, G., & Kolesnikova, O. (2021). Improved Twitter Virality Prediction using Text and RNN-LSTM. International Journal of Combinatorial Optimization Problems and Informatics, 12(3), 50-62. Retrieved from https://ijcopi.org/ojs/article/view/232
Section
Articles