Predicting the Influence of Fake and Real News Spreaders (Student Abstract)

Authors

  • Amy Zhang University of Minnesota - Twin Cities
  • Aaron Brookhouse Michigan State University
  • Daniel Hammer Western Carolina University
  • Francesca Spezzano Boise State University
  • Liljana Babinkostova Boise State University

DOI:

https://doi.org/10.1609/aaai.v36i11.21690

Keywords:

Misinformation, Fake News Spreaders, User Influence Prediction

Abstract

We study the problem of predicting the influence of a user in spreading fake (or real) news on social media. We propose a new model to address this problem which takes into account both user and tweet characteristics. We show that our model achieves an F1 score of 0.853, resp. 0.931, at predicting the influence of fake, resp. real, news spreaders, and outperforms existing baselines. We also investigate important features at predicting the influence of real vs. fake news spreaders.
AAAI-22 Proceedings Cover

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Published

2022-06-28

How to Cite

Zhang, A., Brookhouse, A., Hammer, D., Spezzano, F., & Babinkostova, L. (2022). Predicting the Influence of Fake and Real News Spreaders (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13107-13108. https://doi.org/10.1609/aaai.v36i11.21690

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program