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

Amy Zhang, Aaron Brookhouse, Daniel Hammer, Francesca Spezzano, Liljana Babinkostova

AAAI 2022 pp. 13107-13108

doi:10.1609/AAAI.V36I11.21690 /aaai/2022/zhang2022aaai-predicting/

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.

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Cite

Text

Zhang et al. "Predicting the Influence of Fake and Real News Spreaders (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21690

Markdown

[Zhang et al. "Predicting the Influence of Fake and Real News Spreaders (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/zhang2022aaai-predicting/) doi:10.1609/AAAI.V36I11.21690

BibTeX

@inproceedings{zhang2022aaai-predicting,
  title     = {{Predicting the Influence of Fake and Real News Spreaders (Student Abstract)}},
  author    = {Zhang, Amy and Brookhouse, Aaron and Hammer, Daniel and Spezzano, Francesca and Babinkostova, Liljana},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13107-13108},
  doi       = {10.1609/AAAI.V36I11.21690},
  url       = {https://mlanthology.org/aaai/2022/zhang2022aaai-predicting/}
}