TY - JOUR
T1 - Mass media impact on opinion evolution in biased digital environments
T2 - a bounded confidence model
AU - Pansanella, Valentina
AU - Sîrbu, Alina
AU - Kertesz, Janos
AU - Rossetti, Giulio
N1 - © 2023. Springer Nature Limited.
PY - 2023/9/5
Y1 - 2023/9/5
N2 - People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four different media landscapes and found that an open-minded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users’ biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.
AB - People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four different media landscapes and found that an open-minded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users’ biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.
UR - http://www.scopus.com/inward/record.url?scp=85169762109&partnerID=8YFLogxK
U2 - 10.1038/s41598-023-39725-y
DO - 10.1038/s41598-023-39725-y
M3 - Article
C2 - 37670041
AN - SCOPUS:85169762109
SN - 2045-2322
VL - 13
SP - 14600
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 14600
ER -