Content area
Abstract
Objectives
Despite popular notions of “filter bubbles” and “echo chambers” contributing to radicalization, little evidence exists to support these hypotheses. However, social structure social learning theory would suggest a hereto untested interaction effect.
Methodology
An RCT of new Twitter users in which participants were randomly assigned to a treatment of “filter bubble” (personalization algorithm) suppression. Ego-centric network and survey data were combined to test the effects on justification for suicide bombings.
Findings
Statistically significant interaction effects were found for two proxies of the echo chamber, the E-I index and modularity. For the treatment group, higher scores on both factors decreased the likelihood for radicalization, with opposing trends in the control group.
Conclusions
The echo chamber effect may be dependent on the filter bubble. More research is needed on online network structures in radicalization. While personalization algorithms can potentially be harmful, they may also be leveraged to facilitate interventions.
Details
; Weisburd, David 2 ; Hasisi, Badi 1 1 Hebrew University of Jerusalem, Institute of Criminology, Faculty of Law, and the Cyber-Security Research Centre, Jerusalem, Israel (GRID:grid.9619.7) (ISNI:0000 0004 1937 0538)
2 Hebrew University of Jerusalem, Institute of Criminology, Faculty of Law, and the Cyber-Security Research Centre, Jerusalem, Israel (GRID:grid.9619.7) (ISNI:0000 0004 1937 0538); George Mason University, Department of Criminology, Law and Society, Fairfax, USA (GRID:grid.22448.38) (ISNI:0000 0004 1936 8032)