TY - GEN
T1 - Predicting the Influence of Fake and Real News Spreaders (Student Abstract)
AU - Zhang, Amy
AU - Brookhouse, Aaron
AU - Hammer, Daniel
AU - Spezzano, Francesca
AU - Babinkostova, Liljana
N1 - Publisher Copyright:
Copyright © 2022, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2022/6/30
Y1 - 2022/6/30
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85128836452&partnerID=8YFLogxK
U2 - 10.1609/aaai.v36i11.21690
DO - 10.1609/aaai.v36i11.21690
M3 - Conference contribution
AN - SCOPUS:85128836452
T3 - Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022
SP - 13107
EP - 13108
BT - IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
T2 - 36th AAAI Conference on Artificial Intelligence, AAAI 2022
Y2 - 22 February 2022 through 1 March 2022
ER -