TY - CHAP
T1 - Modeling Misinformation Diffusion in Social Media: Beyond Network Properties
T2 - 3rd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2021
AU - Spezzano, Francesca
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - In this paper, we discuss the current limitations of existing models for misinformation diffusion in social media and present our current work suggesting that other factors beyond network properties play an important node in modeling misinformation spread and profiling fake news spreaders. These factors include news and user characteristics such as user demographics, profile properties, and behavior and activity, and news style and content complexity.
AB - In this paper, we discuss the current limitations of existing models for misinformation diffusion in social media and present our current work suggesting that other factors beyond network properties play an important node in modeling misinformation spread and profiling fake news spreaders. These factors include news and user characteristics such as user demographics, profile properties, and behavior and activity, and news style and content complexity.
KW - computational social science
KW - diffusion of innovation
KW - fake news diffusion
KW - misinformation
KW - profiling fake news spreaders
KW - technological innovation
UR - https://scholarworks.boisestate.edu/cs_facpubs/317
UR - https://doi.org/10.1109/CogMI52975.2021.00030
UR - http://www.scopus.com/inward/record.url?scp=85128827929&partnerID=8YFLogxK
U2 - 10.1109/CogMI52975.2021.00030
DO - 10.1109/CogMI52975.2021.00030
M3 - Chapter
T3 - Proceedings - 2021 IEEE 3rd International Conference on Cognitive Machine Intelligence, CogMI 2021
SP - 168
EP - 171
BT - 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI)
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2021 through 15 December 2021
ER -