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. and
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.
The percentage of true positives in detection of controversial articles compared to human judgment at different values of M
. A threshold of
for controversiality is selected according to this diagram.
Figure 3.
Histogram of articles according to their controversiality measure
. There are some 84 k articles with
, 12 k controversial articles with M
, and less than 100 super-controversial articles with
.
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.
PDF of the average time 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.
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 .
Figure 7.
Scatter plot of burstiness and the controversy measure.
Color coding according to logarithm of the density of points. The correlation coefficient .
Figure 8.
Histogram of burstiness of A) all edits, B) reverts, and C) mutual reverts for four classes of articles.
High controversy (, topmost panels),listed as controversial (2nd panels), randomly selected (3rd panels), and featured articles (bottom panels).
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 for all articles is about 10 hours. Circles are empirical data and solid lines are model fit, with values
,
and
,
respectively for disputed and non-disputed samples. The dashed line in the left panel is the power law with exponent
.
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.
Distribution of 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 for shuffled data and solid line in the left panel is a power-law with
.
Table 1.
Scaling exponents for the two samples of controversial and peaceful articles, and users.
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.
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 tends to saturate, corresponding to the reaching to consensus.
Figure 14.
Evolution of controversy measure with number of edits of of Iran – the insets depict focuses of some of the local war periods.
is normalized to the final value
. Cycles of peace and war appear consequently, activated by internal and external causes.
Figure 15.
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 is 1300 edits.
Figure 16.
Evolution of controversy measure with number of edits of Anarchism and Barack Obama.
is normalized to the final value
. There is no consensus even for a short period and editorial wars continue nonstop.
Figure 17.
Relative share of each category at different M.
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.
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.
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, .
Figure 20.
Scatter plot of talk page length vs.
. Color coding is according to logarithm of the density of points. There is a rather clear correlation,
between the length of the talk page and the controversality of the article.
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.
Average , color coded for different M
’s and n
’s.
For a wide range of articles and in a long time of their lives , 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.