The interplay between extremism and communication in a collaborative project
Collaboration is among the most fundamental social behaviours. The Internet and particularly the Web have been originally developed to foster large scale collaboration among scientists and technicians. The more recent emergence of Web 2.0 and ubiquity of user-generated content on social web, has provided us with even more potentials and capacities for large scale collaborative projects. Projects such as Wikipedia, Zooniverse, Foldit, etc are only few examples of such collective actions for public good.
Despite the central role of collaboration in development of our societies, data-driven studies and computational approaches to understand mechanisms and to test policies are rare.
In a recent paper titled “Understanding and coping with extremism in an online collaborative environment: A data-driven modeling” that is published in PLoS ONE, we use an agent-based modelling framework to study opinion dynamics and collaboration in Wikipedia.
Our model is very simple and minimalistic and therefore the results can be generalized to other examples of large scale collaboration rather easily.
We particularly focus on the role of extreme opinions, direct communication between agents, and punishing policies that can be implemented in order to facilitate a faster consensus.
The results are rather surprising! In the abstract of the paper we say:
… Using a model of common value production, we show that the consensus can only be reached if groups with extreme views can actively take part in the discussion and if their views are also represented in the common outcome, at least temporarily. We show that banning problematic editors mostly hinders the consensus as it delays discussion and thus the whole consensus building process. We also consider the role of direct communication between editors both in the model and in Wikipedia data (by analyzing the Wikipedia talk pages). While the model suggests that in certain conditions there is an optimal rate of “talking” vs “editing”, it correctly predicts that in the current settings of Wikipedia, more activity in talk pages is associated with more controversy.
Read the whole paper here!