Research in fake news detection and prevention has gained a lot of attention over the past decade, with most models using features generated from content and propagation paths. Complementary to these approaches, in this position paper we outline a framework inspired from the domain of epidemiology that proposes to identify people who are likely to become fake news spreaders. The proposed framework can serve as motivation to build fake news mitigation models, even for the scenario when fake news has not yet originated. Some models based on the framework have been successfully evaluated on real world Twitter datasets and can provide motivation for new research directions.
|Original language||English (US)|
|Journal||CEUR Workshop Proceedings|
|State||Published - 2020|
|Event||2020 International Conference on Information and Knowledge Management Workshops, CIKMW 2020 - Galway, Ireland|
Duration: Oct 19 2020 → Oct 23 2020
- Fake news spreaders
- Social networks