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 plan to enhance this model by adding the biased interaction with media, in an effort to understand whether this facilitates polarisation. Media interaction will be modelled as external fields that affect the population of individuals. Furthermore, we will study whether moderate media can be effective in counteracting polarisation.

[1] Sîrbu, A., Pedreschi, D., Giannotti, F. and Kertész, J., 2019. Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model. PloS one, 14(3), p.e0213246.

Output

A paper on opinion dynamics in a complex systems or interdisciplinary journal.

Presentations

Project Partners:

  • Consiglio Nazionale delle Ricerche (CNR), Giulio Rossetti
  • Central European University (CEU), Janos Kertesz
  • Università di Pisa (UNIPI), Alina Sirbu

 

Primary Contact: Giulio Rossetti, Consiglio Nazionale delle Ricerche, Pisa, Italy

Main results of micro project:

The project has run for less than 50% of its allocated time (it started on the 1st of July and will run for 4 months).

So far the algorithmic bias model has been extended to integrate media effects and preliminary correctness tests have been performed.
Moreover, the experimental settings have been fixed and a first preliminary analysis of initial results performed.

Contribution to the objectives of HumaneAI-net WPs

The recent polarization of opinions in society has triggered a lot of research into the mechanisms involved. Personalized recommender systems embedded into social networks and online media have been hypothesized to contribute to polarisation, through a mechanism known as algorithmic bias.

In recent work 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 plan to enhance this model by adding the biased interaction with media, in an effort to understand whether this facilitates polarisation. Media interaction will be modelled as external fields that affect the population of individuals. Furthermore, we will study whether moderate media can be effective in counteracting polarisation.

Tangible outputs

Attachments

RPReplay-Final1634060242_Berlin.mov