=Paper=
{{Paper
|id=Vol-1176/CLEF2010wn-PAN-HarpalaniEt2010
|storemode=property
|title=Wiki Vandalysis - Wikipedia Vandalism Analysis - Lab Report for PAN at CLEF 2010
|pdfUrl=https://ceur-ws.org/Vol-1176/CLEF2010wn-PAN-HarpalaniEt2010.pdf
|volume=Vol-1176
|dblpUrl=https://dblp.org/rec/conf/clef/HarpalaniPBHJ10
}}
==Wiki Vandalysis - Wikipedia Vandalism Analysis - Lab Report for PAN at CLEF 2010==
https://ceur-ws.org/Vol-1176/CLEF2010wn-PAN-HarpalaniEt2010.pdf
Wiki Vandalysis - Wikipedia Vandalism Analysis
Lab Report for PAN at CLEF 2010
Manoj Harpalani, Thanadit Phumprao, Megha Bassi, Michael Hart, and Rob Johnson
Stony Brook University
{mharpalani, tphumprao, mbassi, mhart, rob}@cs.stonybrook.edu
Abstract Wikipedia describes itself as the “free encyclopedia that anyone can
edit”. Along with the helpful volunteers who contribute by improving the arti-
cles, a great number of malicious users abuse the open nature of Wikipedia by
vandalizing articles. Deterring and reverting vandalism has become one of the
major challenges of Wikipedia as its size grows. Wikipedia editors fight van-
dalism both manually and with automated bots that use regular expressions and
other simple rules to recognize malicious edits[5]. Researchers have also pro-
posed Machine Learning algorithms for vandalism detection[19,15], but these
algorithms are still in their infancy and have much room for improvement. This
paper presents an approach to fighting vandalism by extracting various features
from the edits for machine learning classification. Our classifier uses information
about the editor, the sentiment of the edit, the “quality” of the edit (i.e. spelling
errors), and targeted regular expressions to capture patterns common in blatant
vandalism, such as insertion of obscene words or multiple exclamations. We have
successfully been able to achieve an area under the ROC curve (AUC) of 0.91 on
a training set of 15000 human annotated edits and 0.887 on a random sample of
17472 edits from 317443.
1 Introduction
Wikipedia defines vandalism as “any addition, removal, or change of content made in a
deliberate attempt to compromise the integrity of Wikipedia”[21]. Vandalism can take
many forms, including deleting all the content from a page, modifying a page to be
so long that is becomes difficult to load, and inserting profanity, nonsense, unrelated
information, inaccurate information, opinionated text, or spam.
Vandalism detectors attempt to automatically distinguish integrity-violating edits
from integrity-preserving edits. Wikipedia currently uses a combination of manual and
automated vandalism detection. The automated “bots” employed by Wikipedia use reg-
ular expressions and other simple rules to detect vandalism[5]. Researchers have previ-
ously suggested more advance vandalism detection algorithms based on content in the
edit[16], author information[20], compression ratio of the article with and without the
revision[10], and Bayesian-classifiers built on the differences between the new and old
revisions[19]. These early approaches leave much room for improvement.
This paper presents a machine-learning-based vandalism detector that uses several
features to classify vandalism and that achieves an AUC score of over 0.887. Measuring
the performance of the vandalism detection algorithm in terms of AUC instead of other
measures makes sense as AUC score denotes the probability by which a classifier is
able to distinguish a randomly sampled vandalism edit from a randomly sampled reg-
ular edit. Our results significantly outperform a baseline classifier based on a previous
approach[19]. Our classifier uses several simple features to catch obvious “silly” forms
of vandalism, such as inserting obscenities or long, repeating patterns of text. We also
use subtler content-based features, such as misspelled words, grammatical errors, and
changes in sentiment, which tend to indicate an edit violates the Wikipedia editing pol-
icy. Finally, the classifier uses information about the source of an edit, e.g. whether it is
anonymous, to make its decisions.
2 Training corpus & Feature Extraction
The training corpus for the classification was provided by the organizers of the PAN
workshop[1,14]. The corpus consisted of 15000 edits coupled with the previous revi-
sions. Along with the training corpus in WikiMarkup format, we were also provided
with meta-data including the edit id, the old revision id, the new revision id, the user
name or IP of the author who performed the edit, the comment of the author, and
whether the edit vandalized the article.
Wikipedia articles in the training set were formatted in WikiMarkup[22]. Wiki-
Markup includes not only the content, but link and display instructions, much like
HTML. For our purposes, we converted the WikiMarkup directly to plain text using
the Bliki engine[4], which eliminated the formatting and link information. Therefore,
we are unable to detect template or link vandalism.
We focus on content based vandalism as it is the most prominent type of vandalism
that exists. After analyzing samples of vandalized and regular edits, we observed that
certain attributes of the edits distinguished vandalism from regular edits. We used the
following features of the edits for classification:
Edit Distance: Our classifier calculates the Damerau-Levenstein Distance using the
LingPipe API[2] to determine the number of changes required to convert the old revi-
sion of an article to its new revision.
Edit Type: Our classifier determines whether the edit inserted, deleted and/or modified
text or a combination of these actions.
Text Changes: Our classifier determines the edit length, word count, words inserted,
words deleted, and words changed using java-diff[9]. It also uses LingPipe’s English
Sentence chunker to tokenize an article into sentences and calculate exact changes
sentence-by-sentence using java-diff.
Spelling Errors: Our classifier counts the number of apparent spelling mistakes in the
edit and the ratio of spelling errors to correctly spelled words. Our spell-checking[12]
software contained 200K English words, including named entities such as proper names
and geographic places.
Obscene Words: Our classifier enumerates the total number of obscene words in the
edit and the ratio of obscene words to benign words. We started with a dictionary of
obscene words[3] and manually added other obscene words that we observed frequently
in vandalized edits of our training set.
Repeated Patterns: “Silly” vandalism often employs repeated patterns like upper case
words, exclamation marks, and repetition of words/letters (e.g. “heyyyyyy”, “oh!!!!!”,
“wow wow wow”, “hahahaha”, “WIKIPEDIAAA”). Our classifier counts these patterns
using the regular expressions.
Grammatical errors: Wikipedia editors strive for good grammar, but vandals do not
generally follow these rules. They may insert words or phrases into the middle of ex-
isting sentences or write ungrammatical sentences deliberately or unintentionally. Our
classifier parses the edits into sentences that had been inserted or changed by using
java-diff and LingPipe’s sentence tokenizer and counts the grammatical errors in them
using CMU’s Link Grammar Parser[7].
Sentiment Analysis: Statements expressing opinions are common features of vandal-
ism. Our classifier performs a sentiment analysis of the edit to uncover subjective or
opinionated (positive or negative) text. We use LingPipe’s Sentiment Analysis Tool
trained on movie review data. The classifier counts objective or opinionated sentences
and measures the change in total number of positive or subjective sentences.
Sum of metrics: This simple but effective meta-feature is the sum of the number of Re-
peated Letters, Repeated Words, Capitalized Words and Multiple Exclamation Marks.
Article History: Vandals do not necessarily perform vandalism across all articles at
the same rate. Rather, some articles receive a disproportionate amount of vandalism
than others (e.g. Michael Jackson). The feature is the number of times an article was
vandalized in the previous 5000 edits on the article. We denote vandalism as a comment
left by an editor that contains “reverted”, “user” and “vandalism” in this order. We count
also how many times an article was reverted, regardless if it was explicitly vandalism
or not.
Editor information: Knowing who contributed an edit can give us some expectation of
the quality of the change. For example, we expect an active and registered Wikipedia
editor with several thousand edits to be more trustworthy compared to an unregistered
editor that is only identifiable from an IP address and who has not contributed before.
Our classifier uses several editor-based features: whether the editor is registered or un-
registered, how long the editor has been registered, the total number of contributions
made to Wikipedia during the period that training data was collected, the total number
of edits made to Wikipedia by the editor up to the date the training data was finalized,
the number of reverts on previous revisions by the author deemed to be vandalism (using
the same heuristic as for article history) and the total number of previous modifications
made by the editor on the article they are revising. We were careful to make sure for
edit totals to not include future modifications. If the user is not registered, the classifier
uses their IP address as a proxy for their identity.
3 Classifiers
Modeling Classifiers. We compare in this section two different models for building
classifiers. We first investigate the Naive Bayes with Bag Of Words approach described
in Smets et al[19]. We introduce a modification to this approach by combining Naive
Bayes with Rank Boost[6]. We compare this to a second approach that uses the NBTree
Reg(0) UnReg(1) User
<= 0.5 else
Author contrib total Misspelt Words
<= 15.5 else <= 0.5 else
Registered since (seconds) ObsceneWordRatio NB Model ArticleRevertCount
<= 1.2x10^9 else <= 0.16 else <= 17.5 else
NB Model NB Model NB Model NB Model SubjSentsInserted SentScoreDiff
<= -2.5 else <= -8.3 else
NB Model NB Model NB Model NB Model
Figure 1. The structure of the NBTree after building the classifier on all training examples. The
branches of the tree denote the decision path taken along with the threshold chosen for taking a
particular branch. The leaves are Naive Bayesian classifiers used to classify the edit.
classifier (a hybrid between decision trees and Bayesian classifiers) and the features
described above.
Baseline: Naive Bayes with the Bag Of Words model. In order to set a baseline for
comparison, we first started with classification of edits using the Bag of Words ap-
proach. We used Mallet[13] to create a vector space model based on unigrams from the
edit difference containing inserted and changed text in the new revision of the article
to create the Bag of Words and then used word count of the words after removing the
stop words as a feature to the Naive Bayes classifier. We used Rank Boost on this weak
classifier to maximize its area under the ROC curve. In this experiment, boosting the
Naive Bayes classifier 15 times performed the best.
Building classifiers. We built classifiers using the features presented in the previous
section with three different algorithms: Naive Bayes, C4.5 Decision Trees[18], and
NBTrees[11] using Weka[8], a machine learning and data mining library provided by
University of Waikato. The NBTree performed the best among all the other classifiers.
An NBTree is a decision tree with Bayesian classifiers as its leaves. The classifier builds
a decision tree to optimally partition the training data to build several Bayesian classi-
fiers that will perform better than either a decision tree or Bayesian classifier alone.
We combined all the features described in the previous section and performed a
10-fold cross validation on all three algorithms. NBTree yielded the best result with the
default settings, which uses 5-fold cross validation of Naive Bayes at a node to compute
its utility and a split threshold of 5% of the relative reduction on error. Figure 1 shows
the NBTree we constructed from the training data.
4 Evaluation & Results
Our test corpus contained 15000 edits. Registered users contributed 67% of these edits,
while anonymous users contributed 33%. Among these 93.9% of edits were not in-
Regular Edits Vandalism
Figure 2. The above Venn diagrams show how often the text of an article was inserted into,
changed, deleted or any combination of these actions
Feature Information Gain
Total number of author contributions 0.074
How long the author has been registered 0.067
If the author is a registered user 0.060
How frequently the author contributed in the training set 0.040
How often the article has been vandalized 0.035
How often the article has been reverted 0.034
The number of previous contributions by the author on the article 0.019
Change in sentiment score 0.019
Number of misspelled words 0.019
Sum of metrics 0.018
Table 1. Top ten features ranked by information gain
stances of vandalism. Not surprisingly, unregistered users more frequently vandalized
articles than registered users, 16% to 1% respectively. See Figure 2 for Venn diagrams
which highlight how often an article had text modified, deleted, inserted, or any combi-
nation of these actions. Note that vandals are significantly more likely to insert text and
are much less likely to make multiple changes in one revision.
In Table 1, we present our top 10 ranked features according to information gain.
Table 2 compares the performance of our classifiers using stratified 10-fold cross val-
idation on the training data. Table 3 presents the results on the PAN 2010 Wikipedia
Vandalism test corpus[1].
The authors of [17] observed that there are a small number of registered users
(0.1%) that generate over half the content on Wikipedia. Therefore, we report not only
the overall performance, but also statistics relating to important subsets of editors. This
will let us gauge, for example, if the false positive rate is much too high to be acceptable
for frequent contributors. We encourage future papers relating to the efficacy of vandal-
ism detectors to analyze the performance for important subsets of editors. We report
Metric NB+BoW NB+BoW+RankBoost NB C4.5 Decision Tree NBTree
Precision 27.8% 34.1% 15.8% 53.2% 64.3%
Recall 32.6% 26.6% 93.2% 36.9% 36.4%
Accuracy 87.5% 89.7% 69.2% 94.1% 94.8%
F-measure 30.1% 29.9% 27.1% 43.6% 46.5%
AUC 69% 62% 88.5% 80.5% 91%
Table 2. Overall performance of classifiers with 10 fold stratified cross validation on training set
Metric Naive Bayes C4.5 Decision Tree NBTree
Precision 19.0% 51.0% 61.5%
Recall 92.0% 26.7% 25.2%
Accuracy 72.0% 91.6% 92.3%
F-measure 35.5% 35.1% 35.8%
AUC 86.6% 76.9% 88.7%
Table 3. Overall performance of classifiers on PAN 2010 testing data
statistics for both registered and unregistered users as well as edits made by those users
or IP addresses that frequently contribute to the article in Table 4.
5 Discussion
Our NBTree-based classifier outperforms the baseline classifier for two reasons. First,
the features we selected – editor information, sentiment change, etc. – convey more
information about each edit than the simple bag of words model. Second, by using an
NBTree, we enable the classifier to partition the diverse space of edits into regions that
are more amenable to Bayesian classification.
Five of our top ten features involved editor information. Current Wikipedia bots
make limited use of editor information, but our results show that they could do more.
In fact, our NBTree approach benefited most from how many contributions a particular
Wikipedia User or IP address has made. Other important editor attributes were edit fre-
quency, i.e. whether the editor is currently active, and whether the editor had contributed
to the article being revised.
Another observation yielded by our highest ranked features is that simple features
outperformed our more sophisticated features. The only sophisticated feature that ap-
peared in the top ten ranked attributes was change in sentiment. Figure 3 shows the
values of the change in sentiment score. We note that for vandalizing edits, the mean
change in sentiment was −0.14 with a standard deviation of 2.0 and for regular edits the
mean change was 0.03 with a standard deviation of 1.1. Although most edits, vandal-
ism and otherwise, had zero change in sentiment score, we note that vandalism skews
towards a negative change in sentiment and regular edits positively, which appears to
make sense intuitively. Because of time and resource constraints, we were not able to
confirm if either A) vandalism introduces more polarizing than subjective statements,
B) the polarity detector is more effective than the subjectivity detector software or C)
Type of user FP rate Recall Precision
Registered users < 0.1% 22.0% 68.4%
Registered users that edited this article 10 times or more <0.01% 0.0% 0.0%
Unregistered users 3.9% 40.8% 67.2%
IP addresses that edited this article 10 times or more 1.7% 33.3% 50.0%
Table 4. Performance of our classifier for registered and unregistered users as well as frequent
contributors
Figure 3. This box plot captures the change in sentiment between regular and vandalized edits.
The outliers are shown as circles. The majority of edits had zero change in sentiment, but we do
see that vandalized edits skew negatively and regular edits skew in a positive difference
subjective or polarizing statements do not constitute a significant percentage of vandal-
ism. We approached the more sophisticated features like the sentiment analysis with an
off-the-shelf approach. Therefore, we used pre-existing datasets and software APIs to
gather feature values. We hypothesize that employing Wikipedia specific examples may
perform more precisely in detecting subjectivity and polarity in article revisions.
As mentioned before, the initial partition performed by the NBTree evaluated how
many contributions a registered user or an IP address has made to Wikipedia. Interest-
ingly the benefit of this feature is not exclusive to registered users. IP addresses that
have contributed significantly in the past have a lower vandalism to regular edit ratio in
this dataset than those IP addresses that have only contributed a handful of edits. In our
training data, the ratio of regular edits to vandalized edits for IP addresses with more
than 10000 previous edits and IP addresses with less than 100 previous edits is 76.0 and
3.6 respectively. We gathered information about 5 IP addresses in the training set with
the most contributions and found that they were located near universities and one was
owned by Microsoft (according to the reverse DNS). Therefore, what does this suggest
for Wikipedia? Certain IP addresses and geographic areas are conducive to recruiting
future Wikipedians - that these individuals have demonstrated a commitment to the in-
tegrity of the site. This would grant more accountability and perhaps better expose the
malicious edits from these “good” IP addresses.
Two of our features unexpectedly behaved similarly to the edit distance feature.
First, we note that the larger the edit distance, the more likely the edit is not vandalizing
the article. We found that our grammar and misspelled words feature ultimately be-
haved in a similar fashion. For misspelled words, the larger the edit, the more likely our
dictionary found misspelled words. Analyzing the top 10 edits with the most misspelled
words (all non-vandalizing edits), we observed that these articles contained many non-
English or unique pronouns. For example, one article on the movie “Blue Hills Avenue”
had many misspellings because the dictionary could not recognize several of the char-
acter names. Therefore, we suggest for future dictionaries to build an article specific
dictionary that incorporates words that survive several revisions or appear to be in-
tegral to the article (a summarization technique could be a good first approximation
for this approach). Our grammar checker also had similar issues, arising from difficul-
ties with part-of-speech tagging certain words. Another problem unique to the grammar
checker is that several different types of tabulations and enumerations are used through-
out Wikipedia. An obvious improvement for implementing a grammar checker is to ig-
nore any modification that occurs in either a table, list or infobox. In future testing, we
plan to implement these changes.
Our classifier had tremendous difficulty classifying vandalism by registered users.
A sizable chunk of these vandalizing edits come from users that registered just before
submitting the edit, and therefore are very suspicious, but the rest of these vandalizing
edits are much more difficult to detect. Registered users infrequently vandalize articles,
so we have little training data to learn from. We noted in two of ten cases that we man-
ually inspected that there was a strong argument to not classify the edit as vandalism.
For example, one edit introduced a link to “Sex Magic”, which may appear on face
value to be nonsense. On further investigation, it turns out that this is indeed an article
in Wikipedia. As we are indebted to Potthast et al. for their dataset[14], we recommend
that the edits labeled vandalism by frequent and active contributors to be examined
more closely by an active Wikipedian. Fortunately, this constitutes a small minority of
edits, but detecting vandalism among registered users is a challenging obstacle. In the
future, we will examine techniques for training with unbalanced datasets to see if we
can further improve our detection for this type of vandalism.
Lastly, we hypothesize the success of the NBTree arises from the fact that vandalism
is too diverse to be captured by a “one-size-fits-all” approach and requires partitioning
the training space to find edits with common features. In our experiments, the Weka
toolkit allowed us to test several different types of classifiers including meta-classifiers,
neural networks, support vector machines and decision trees. Ultimately, decision trees
fared better than other classification schemes and the C4.5 and NBTree the best. Some
partitions in the NBTree, as seen in Figure 1, take advantage of the fact that it is highly
unlikely that edits which meet a certain criterion will contain vandalism (e.g. a frequent
contributor to Wikipedia). We hope to explore how we can further identify particular
types of vandalism and build precise and effective classifiers for them automatically.
6 Conclusion
This paper presents new features and algorithms to improve classification of vandalism.
We found that features regarding information about the editor, misspellings, obscene
words, detection of repetitive patterns and change in sentiment effectively classified
vandalism. These features combined with the NBTree classifier gave an AUC score of
91% for a 10-fold stratified cross validation of our training corpus and 88.7% on the
PAN 2010 testing corpus. We hope to expand upon these features and look for new
ways to compare the content inserted, modified or deleted with previous revisions to
determine if such actions constitute vandalism.
7 Acknowledgements
We would like to acknowledge Tamara Berg and Luis Ortiz for their guidance and
suggestions.
References
1. 2010, P.W.: Evaluation Campaign on Plagiarism Detection and Wikipedia Vandalism
Detection. http://www.uni-weimar.de/medien/webis/research/
workshopseries/pan-10/task2-vandalism-detection.htm (2010)
2. Alias-i.: Lingpipe 4.0.0. http://alias-i.com/lingpipe/ (October 2008)
3. anonymous: English obscene wordlist. http://expressionengine.com/?ACT=
51&fid=8&aid=1830_mvLZ2WkoRucNvRegThbL&board_id=
4. axelclk: gwtwiki - Java Wikipedia API (Bliki engine).
http://code.google.com/p/gwtwiki/
5. Carter, C.: ClueBot. http://en.wikipedia.org/wiki/Wikipedia:CLUEBOT
6. Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for
combining preferences (1998)
7. Grinberg, D., Lafferty, J., Sleator, D.: A robust parsing algorithm for link grammars. In: In
Proceedings of the Fourth International Workshop on Parsing Technologies (1995)
8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data
mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
9. Incava.org: Difference algorithm for Java.
http://www.incava.org/projects/java/java-diff/
10. Itakura, K.Y., Clarke, C.L.A.: Using dynamic markov compression to detect vandalism in
the wikipedia. In: SIGIR ’09: Proceedings of the 32nd international ACM SIGIR
conference on Research and development in information retrieval. pp. 822–823. ACM, New
York, NY, USA (2009)
11. Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In:
Second International Conference on Knoledge Discovery and Data Mining. pp. 202–207
(1996)
12. LLC., S.: English dictionary. http://softcorporation.com/products/
spellcheck/dictionaries/english/
13. McCallum, A.: Mallet: A machine learning for language toolkit (2002)
14. Potthast, M.: Crowdsourcing a Wikipedia Vandalism Corpus. In: 33rd Annual International
ACM SIGIR Conference (to appear). ACM (Jul 2010)
15. Potthast, M., Stein, B., Gerling, R.: Automatic vandalism detection in wikipedia. In:
Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) Advances in
Information Retrieval, Lecture Notes in Computer Science, vol. 4956, chap. 75, pp.
663–668. Springer Berlin Heidelberg, Berlin, Heidelberg (2008)
16. Potthast, M., Stein, B., Gerling, R.: Automatic vandalism detection in wikipedia. In:
ECIR’08: Proceedings of the IR research, 30th European conference on Advances in
information retrieval. pp. 663–668. Springer-Verlag, Berlin, Heidelberg (2008)
17. Priedhorsky, R., Chen, J., Lam, S.T.K., Panciera, K., Terveen, L., Riedl, J.: Creating,
destroying, and restoring value in wikipedia. In: GROUP ’07: Proceedings of the 2007
international ACM conference on Supporting group work. pp. 259–268. ACM, New York,
NY, USA (2007)
18. Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San
Mateo, CA (1993)
19. Smets, K., Goethals, B., Verdonk, B.: Automatic vandalism detection in wikipedia: Towards
a machine learning approach. In: Proceedings of the Association for the Advancement of
Artificial Intelligence (AAAI) Workshop on Wikipedia and Artificial Intelligence: An
Evolving Synergy (WikiAI08). pp. 43–48. AAAI Press (2008)
20. West, A.G., Kannan, S., Lee, I.: Detecting wikipedia vandalism via spatio-temporal analysis
of revision metadata. In: EUROSEC ’10: Proceedings of the Third European Workshop on
System Security. pp. 22–28. ACM, New York, NY, USA (2010)
21. Wikipedia: Definition of vandalism.
http://en.wikipedia.org/wiki/Wikipedia:Vandalism
22. Wikipedia: WikiMarkup.
http://en.wikipedia.org/wiki/Help:Wiki_markup