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. 2012;7(6):e38869.
doi: 10.1371/journal.pone.0038869. Epub 2012 Jun 20.

Dynamics of conflicts in Wikipedia

Taha Yasseri  1 , Robert Sumi, András Rung, András Kornai, János Kertész
Affiliations

Dynamics of conflicts in Wikipedia

Taha Yasseri et al. PLoS One. 2012.

Abstract

In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.

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Conflict of interest statement

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

Figures

Figure 1
Figure 1. Revert and mutual revert maps of Benjamin Franklin (left) and Israel and the apartheid analogy (right).
Diagrams in upper row show the map of all reverts, whereas only mutual reverts are depicted on the diagrams in the lower row. [Image: see text] and [Image: see text] are the number of edits made by the reverting and reverted editors respectively. Size of the dots is proportional to the number of reverts by the same reverting and reverted pair of editors.
Figure 2
Figure 2. The percentage of true positives in detection of controversial articles compared to human judgment at different values of M
[Image: see text] . A threshold of [Image: see text] for controversiality is selected according to this diagram.
Figure 3
Figure 3. Histogram of articles according to their controversiality measure
[Image: see text] . There are some 84 k articles with [Image: see text], 12 k controversial articles with M [Image: see text], and less than 100 super-controversial articles with [Image: see text].
Figure 4
Figure 4. Temporal edit patterns of Lady Gaga and Homosexuality during a one month period (12/2009).
The horizontal axis is time, each vertical line represents a single edit. Despite the large differences in average time intervals between successive edits, the bursty editing pattern is common to both cases.
Figure 5
Figure 5. PDF of the average time [Image: see text] between two successive edits of articles measured in days.
In any two week period most of the articles are edited twice or more.
Figure 6
Figure 6. Scatter plot of the average time interval between successive edits and the controversy measure.
Color coding is according to logarithm of the density of points. The correlation coefficient [Image: see text].
Figure 7
Figure 7. Scatter plot of burstiness and the controversy measure.
Color coding according to logarithm of the density of points. The correlation coefficient [Image: see text].
Figure 8
Figure 8. Histogram of burstiness of A) all edits, B) reverts, and C) mutual reverts for four classes of articles.
High controversy ([Image: see text], topmost panels),listed as controversial (2nd panels), randomly selected (3rd panels), and featured articles (bottom panels).
Figure 9
Figure 9. PDF of intervals between two successive edits on an article (in seconds) for two samples of highly/weakly disputed articles (left/right panel).
Each sample contains 20 articles and the average [Image: see text] for all articles is about 10 hours. Circles are empirical data and solid lines are model fit, with values [Image: see text], [Image: see text] and [Image: see text], [Image: see text] respectively for disputed and non-disputed samples. The dashed line in the left panel is the power law with exponent [Image: see text].
Figure 10
Figure 10. Autocorrelation function of edits sequences for two samples of highly/weakly disputed articles (left/right panel).
Circles are for the original sequences, empty squares correspond to the shuffled sequences. Dashed lines are power-law fits.
Figure 11
Figure 11. Distribution of [Image: see text] for two samples of highly/weakly disputed articles (left/right panel).
Circles are for the original sequences, whereas empty squares correspond to the shuffled sequences. Dashed lines are exponential fits to the [Image: see text] for shuffled data and solid line in the left panel is a power-law with [Image: see text].
Figure 12
Figure 12. Time evolution of the controversy measure of Michael Jackson.
A: Jackson is acquitted on all counts after five month trial. B: Jackson makes his first public appearance since the trial to accept eight records from the Guinness World Records in London, including Most Successful Entertainer of All Time. C: Jackson issues Thriller 25. D: Jackson dies in Los Angeles.
Figure 13
Figure 13. Evolution of controversy measure with number of edits of Jyllands-Posten Muhammad cartoons controversy, with Gompertz fit shown in red.
The initial rapid growth in [Image: see text] tends to saturate, corresponding to the reaching to consensus.
Figure 14
Figure 14. Evolution of controversy measure with number of edits of of Iran – the insets depict focuses of some of the local war periods.
[Image: see text] is normalized to the final value [Image: see text]. Cycles of peace and war appear consequently, activated by internal and external causes.
Figure 15
Figure 15. Length of peacful periods.
Histogram of number of edits between two successive war periods for a selected sample of 44 articles which are not driven by external events. The average value of [Image: see text] is 1300 edits.
Figure 16
Figure 16. Evolution of controversy measure with number of edits of Anarchism and Barack Obama.
[Image: see text] is normalized to the final value [Image: see text]. There is no consensus even for a short period and editorial wars continue nonstop.
Figure 17
Figure 17. Relative share of each category at different M [Image: see text].
Blue: category (a), consensus. Red: category (b), multi-consensus. Yellow: Category (c), never-ending war. For the precise definition of each category see the main text.
Figure 18
Figure 18. Length distribution of articles and talk pages with log-normals fits.
The distribution of articles length is better described by a log-normal distribution compared to the talk length distribution, which tends to be more like a power-law.
Figure 19
Figure 19. Scatter plot of talk page vs. article length.
Color coding is according to logarithm of the density of points. The correlation between the length of the article and the corresponding talk page is weak, [Image: see text].
Figure 20
Figure 20. Scatter plot of talk page length vs.
[Image: see text] . Color coding is according to logarithm of the density of points. There is a rather clear correlation, [Image: see text] between the length of the talk page and the controversality of the article.
Figure 21
Figure 21. Network representation of editors’ interactions in the discussion page of Safavid dynasty.
Each circle is an editor, red arrows represent comments opposing the target editor, T-end green lines represent positive comments (agreeing with the other editor), and yellow lines with round end represent neutral comments. Line thickness is proportional to the number of times that the same interaction occurs. Data based entirely on subjective assessments (manual review).
Figure 22
Figure 22. Average [Image: see text], color coded for different M [Image: see text]’s and n [Image: see text]’s.
For a wide range of articles and in a long time of their lives [Image: see text], the relative contribution of the top 5 most reverting pair of editors, is very close to 1, making clear the important role of the top 5 pairs of fighting editors.
See this image and copyright information in PMC

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