NetLogo User Community Models

(back to the NetLogo User Community Models)

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## WHAT IS IT?

This model is NetLogo implementation of the model described in "Modelling the Wikipedia to Understand the Dynamics of Long Disputes and Biased Articles" by Csilla Rudas and János Török, it was developed as a study project for the course "Computational Modelling for Complex Systems" at University of Pisa.

The model simulates the editing process of a Wikipedia article, it is a specific case of the opinion dynamics model, bound-confidence interactions, described by Deffuant et al. [2000]. It shows how editors communicate and influence each other and the article, changing its tone. The article final tone depends critically on the ratio of editors with extreme opinions, but also on the tolerance of the editors towards each other and on on adopted by community banning strategies.

## HOW IT WORKS

The editors, represented by green circles, are "born" with random opinions between 0 and 1. Editors can communicate between each other, if their opinions are not too far from each other (tolerance level), or can edit the article, represented by green square, changing its bias or read the article and change their opinion.
Editing process reaches consensus when all editors are satisfied with the tone of the article (its opinion is in their tolerance range).

## HOW TO USE IT

Click the SETUP button to set up the editors' pool (green circles) and an article (green square on the right-hand side). The position of editors and the article along the y-axis of the world reflects the value of their opinion/bias.
Turtles change their shape to the outlined circle when their opinion is less than 0.1 or more than 0.9.

Click the GO button to start simulation

The NUMBER-OF-EDITORS slider controls the number of editors in the editor pool of the article (Note: Changes in the NUMBER-OF-EDITORS slider do not take effect until the next SETUP.)

The RATIO-OF-EXTREMISTS slider controls the proportion of editors with extreme opinions (above 0.9 and below 0.1). The rest of editors are created as mainstreams, with opinions between 0.45 and 0.55. When the value is 0 a pool of NUMBER-OF-EDITORS editors created with opinions randomly distributed between 0 and 1.

The INHOMOGENEITY chooser lets you set up the proportion of difference between editors' opinions that are taken to calculate their tolerance.

The SENSITIVITY chooser controls the maximum level of tolerance in the world.

The CONVERGENCE slider simulates how difficult it is to change the tone of the article (it's inverse of the article's length), lower values denote more difficult change.

The PROBABILITY-BANNING chooser controls the banning strategy adopted in the editors' pool. The second and third choices prevent editors from editing the article for one time step with different probabilities. The "BASED ON DISSATISFACTION + OFFENDED" strategy also let editors leave the pool in half cases. After an editor leaves, she is replaced with the new one, the opinion of the new editor is chosen randomly.

Behaviour space has a setup for two simulations described in the article.

## THINGS TO NOTICE

The model explores how different conditions affect RELAXATION TIME for the article's editing (shown in the output window at the end of simulation).
Watch how the opinion of the editors changes during the interaction and editing process. Editors and the article become outlined, when their opinion is in "extreme" range.
You can see the clusters of editors move in the world window or follow sample editors on the plot TRAJECTORY OF SAMPLE EDITORS, notice that when the editor leaves the pool his trajectory is interrupted.

When editors are BANNED, their color turns red for 2 time steps and newly arrived editors have violet color for the same amount of time. You can monitor the quantity of editors of each type with EDITORS BY TYPE plot, notice how large is the pool of new editors at the end of the editing cycle with the banning strategy "BASED ON DISSATISFACTION + OFFENDED".

Follow how ARTICLE-BIAS changes over time with the monitor BIAS OF ARTICLE, notice on the RUNNING MU AND STD OF THE ARTICLE plot that running mean and standard deviation is fairly regular, so signal-to-noise ratio is high.

## THINGS TO TRY

Set the INHOMOGENEITY to high value, SENSITIVITY and CONVERGENCE to low values and "no banning" PROBABILITY-BANNING. Observe how the editing process runs for a very long time and how limited the communication is. Change the banning strategy and see what happens.

Fix all parameters to average values and only change RATIO-OF-EXTREMISTS value with the slider, watch the behavior of editors and the final bias value of the article.

## EXTENDING THE MODEL

Try to build networks inside the editor pool.

## NETLOGO FEATURES

The article is designed as a separate breed, so are the extremists and mainstream editors.
All interactions are chosen randomly.

## CREDITS AND REFERENCES

This model is based on the paper:
Rudas, C., & Török, J. (2018).
Modeling the Wikipedia to Understand the Dynamics of Long Disputes and Biased Articles.
Historical Social Research, 43(1), 72-88. https://doi.org/10.12759/hsr.43.2018.1.72-88

Guillaume Deffuanft at al, 2000. Mixing beliefs among interacting agents. Advances in Complex Systems 3: 87-98

The bar plot code is adapted from the example of Nicolas Payette at
https://stackoverflow.com/questions/49122819/how-do-you-generate-a-histogram-in-netlogo

## HOW TO CITE
If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.

For the model itself:

Mikhaylova, D., Milazzo, P. (2021). NetLogo model for Wikipedia editing process. Department of Computer Science, University of Pisa, Italy.

Please cite the NetLogo software as:

Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

## COPYRIGHT AND LICENSE
Copyright 2021 Daria Mikhaylova, Paolo Milazzo


This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.