COBCOD team is composed of researchers of different disciplines that have a long expertise in the study of the formation and diffusion of opinion both at the individual level and at the level of society.
The leading hypothesis of the project is that the global effects seen in online social networks (herding, polarization, echo chambers, etc.) are driven by well-known cognitive biases. Due to the unique features of online platforms—such as high diffusion speed, selective exposure, message size limitations, and potential algorithmic biases—these biases significantly shape interaction dynamics within the network, leading to phenomena not observed offline.
The amplification of cognitive biases through online interactions can drive large-scale collective shifts, distorting individuals’ perceptions of both the world and themselves as social beings. For example, confirmation bias leads people to ignore contradictory information and focus on content that reinforces their views. This bias plays a central role in the formation of “echo chambers”—online groups with similar opinions that have minimal or conflictual interactions with outsiders, further reinforcing and radicalizing their beliefs. Another key example is negativity bias, our tendency to focus more on negative events than positive ones. In the intense competition for attention on social networks, this bias is heavily exploited, shifting social attention disproportionately toward negative events, which can significantly impact users’ worldviews and well-being. A third example is the rise in collective harassment cases on social media, where individuals are aggressively targeted. This may stem from in-group preferences or self-enhancement biases that, when amplified through repeated online interactions, become distorted and intensified.
COBCOD’s primary objective is to better understand how cognitive biases are amplified and potentially altered through repeated online interactions. Specifically, we seek to identify the mechanisms driving the emergence of dynamic patterns unique to online environments and explore how these collective dynamics, in turn, influence individuals.
Our second objective is to investigate strategies for mitigating these effects and test them through models and experiments. We will explore two approaches: examining how users behave when informed about cognitive biases and their effects, and assessing the impact of modifications to the user interface during online interactions.
To achieve these objectives, we believe that a synergy of multiple approaches is essential. This includes individual-based modeling, complex computer simulations using both standard and generative agent-based models (ABM and G-ABM) to study the effects of biased agents in online networks, theoretical analysis of emergent phenomena, large-scale social network data analysis, online experiments with volunteer users, and fully controlled laboratory experiments.
Each of these methods plays a crucial role, but none is sufficient on its own. Their successful implementation requires the joint expertise of different specialists within the team. The structure of COBCOD is designed to integrate these approaches throughout the project, ensuring they complement and enrich one another.