[TMP-018] Algorithmic bias and media effects

Contact person: Giulio Rossetti (giulio.rossetti@isti.cnr.it) 

Internal Partners:

1. Consiglio Nazionale delle Ricerche (CNR), Giulio Rossetti, giulio.rossetti@isti.cnr.it
2. Università di Pisa UNIPI, Dino Pedreschi, dino.pedreschi@unipi.it
3. Central European University (CEU), Janos Kertesz, kerteszj@ceu.edu

Recent polarisation of opinions in society has triggered a lot of research into the mechanisms involved. Personalised recommender systems embedded into social networks and online media have been hypothesized to contribute to polarisation through a mechanism known as algorithmic bias. In a recent work [1], we have introduced a model of opinion dynamics with algorithmic bias, where interaction is more frequent between similar individuals, simulating the online social network environment. In this project, we enhance this model by adding the biased interaction with media, in an effort to understand whether this facilitates polarisation. Media interaction are modelled as external fields that affect the population of individuals. Furthermore, we studied whether moderate media can be effective in counteracting polarisation.

Results Summary

In this micro project, we studied 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.

Tangible Outcomes

1.  Pansanella, V., Sîrbu, A., Kertesz, J., & Rossetti, G. (2023). Mass media impact on opinion evolution in biased digital environments: a bounded confidence model. Scientific Reports, 13(1), 14600. https://www.nature.com/articles/s41598-023-39725-y.pdf 
2. Model implementation https://github.com/ValentinaPansanella/AlgBiasMediaModel