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Understanding and coping with extremism in an online collaborative environment: A data-driven modeling - PubMed

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Understanding and coping with extremism in an online collaborative environment: A data-driven modeling

Csilla Rudas et al. PLoS One. 2017.

Abstract

The Internet has provided us with great opportunities for large scale collaborative public good projects. Wikipedia is a predominant example of such projects where conflicts emerge and get resolved through bottom-up mechanisms leading to the emergence of the largest encyclopedia in human history. Disaccord arises whenever editors with different opinions try to produce an article reflecting a consensual view. The debates are mainly heated by editors with extreme views. 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. To validate the model, relevant quantities are measured both in simulations and Wikipedia, which show satisfactory agreement. 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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Distribution of relaxation time for different number of initial opinion groups for μA = 0.65 and ϵA = 0.25.
Fig 2
Fig 2. The time evolution of the two opinion groups for three sets of parameters: (a) ϵA = 0.2, μA = 0.2, (b) ϵA = 0.075, μA = 0.5, (c) ϵA = 0.1, μA = 0.5.

The points are simulation results, solid lines are result of Eq (6).

Fig 3
Fig 3. Colormaps of the logarithm of the relaxation time τ in function of RoE and r using simulations with N = 10000.

Top row 3, bottom row 4 groups, left regime II, and right regime III. The lower two graphs show cuts at r = 0.5 and RoE = 0.5 respectively. Let us note the log-linear scale and the relaxation time sometimes grows an order of magnitude within small changes of RoE.

Fig 4
Fig 4. Example time evolution for three initial groups.

N = 1000, r = 0.5, ϵA = 0.15, μA = 0.7, left: RoE = 0.5, right: RoE = 0.9.

Fig 5
Fig 5. Example time evolution for four initial groups.

N = 1000, r = 0.5, ϵA = 0.15, μA = 0.7, left: RoE = 0.2, right: RoE = 0.5.

Fig 6
Fig 6. The revert/edit ratio (corresponding to τ) vs. the ratio of edits to talk/article pages (corresponding to r) for 13 different Wikipedia language editions (en: English, West European (red): de: German, fr: French, es: Spanish, pt: Portugal, Eastern European (blue): cs: Czech, hu: Hungarian, ro: Romanian, Middle-East (pink): ar: Arabic, fa: Persian, he: Hebrew, Far-East (green): zh: Chinese, ja: Japanese.
Fig 7
Fig 7. Simulation results: (a) The logarithm of the relaxation time of the original model with three opinion groups and RoE = 0.5, (b) The ratio of the relaxation times with and without banning.
Fig 8
Fig 8. The distribution of the number of times users were banned in different regimes and in Wikipedia.

Notation: en: English, de: German, fa: Persian, RI, RII, RIII stands for Simulation in regime I, II, III respectively. On the main plot only regime III is shown the other regimes for the model are shown in the inset.

Fig 9
Fig 9. Number of edits versus number of bannings (minus 1) in Wikipedia and in the model.

Notation: en: English, de: German, fa: Persian. On the main plot only simulation results of regime III is shown the numerical results of the other regimes of the model are only shown in the inset.

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TY has received funding from the European Union’s Horizon2020 research and innovation program under grant agreement No 645043; "HUMANE: a typology, method and roadmap for HUman-MAchine NEtworks".

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