SIGIR WORKSHOP REPORT

Plagiarism Analysis, Authorship Identification,and Near-Duplicate Detection
PAN’07
Benno Stein, Bauhaus University Weimar
Moshe Koppel, Bar-Ilan University, Israel
Efstathios Stamatatos , University of the Aegean, Greece
benno.stein@medien.uni-weimar.de, moishk@gmail.com, stamatatos@aegean.gr

About
Goal of the workshop was to bring together experts and prospective researchers around the exciting
and future-oriented topic of plagiarism analysis, authorship identification, and high similarity
search. This topic receives increasing attention, which results, among others, from the fact that
information about nearly any subject can be found on the World Wide Web.
At first sight, plagiarism, authorship, and near-duplicates may pose very different challenges;
however, they are closely related in several technical respects:• Plagiarism analysis is a collective term for computer-based methods to identify a plagiarism
offense. In connection with text documents we distinguish between corpus-based and intrinsic analysis: the former compares suspicious documents against a set of potential original
documents, the latter identifies potentially plagiarized passages by analyzing the suspicious
document with respect to changes in writing style.
• Authorship identification divides into so-called attribution and verification problems. In theauthorship attribution problem, one is given examples of the writing of a number of authors
and is asked to determine which of them authored given anonymous texts. In the authorshipverification problem, one is given examples of the writing of a single author and is asked todetermine if given texts were or were not written by this author. Authorship verificationand intrinsic plagiarism analysis represent two sides of the same coin.
• Near-duplicate detection is mainly a problem of the World Wide Web: duplicate Web pages
increase the index storage space of search engines, slow down result serving, and decrease
the retrieval precision. Near-duplicate detection relates directly to plagiarism analysis: atthe document level, near-duplicate detection and plagiarism analysis represent also two sidesof the same coin. For a plagiarism analysis at the paragraph level, the same specialized document models (e. g. shingling, fingerprinting, hashing) can be applied, where a key problem
is the selection of useful chunks from a document.
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Contributions¿From the submissions the program committee accepted 7 research papers for presenting original
work and covering the workshop themes. The presentations were split into three sessions and theworkshop was attended by about 30 people. An interactive form of communication was encouraged
with continuous discussions in between and after the presentations.
The workshop was opened by Efstathios Stamatatos (University of the Aegean), who gave an
invited talk on The Class Imbalance Problem in Author Identification. First, Efstathios described
the state-of-the-art in author identification techniques focusing on instance-based vs. profile-based
approaches as well as on text representation methods. Then, he presented solutions to deal withclass imbalance, an important problem in author identification especially in the framework offorensic applications, for both instance-based and profile-based approaches.
Einat Amitay presented her joint work with Silvan Yogev and Elad Yom-Tov (IBM Research,Israel) about an interesting phenomenon observed on the Web, which they call “Serial Sharers”:
authors who publish excessively, in multiple forms, and under different identities. She focused on
analyzing patterns in the contributions of a relatively small group of people who author too much
web content (serial sharers). Based on a collection of Web pages authored by a couple of thousand
different users and a compression-based classification algorithm she presented a quite interesting
approach for detecting the identity of web authors.
The second session begun with a paper on Forensic Authorship Attribution for Small Texts
by Ol’ga Feiguina and Graeme Hirst (University of Toronto). Graeme presented an approach toidentifying the author of short texts (200-1,000 words) examining the significance of new syntactic
information (bigrams of syntactic labels). The proposed method performs better in forensic texts
rather than literary texts. However, part-of-speech and lexical features are still useful especiallywhen combined with the proposed bigrams of syntactic labels.
The second paper of this session by George Mikros and Eleni Argyri (University of Athens)
analyzed Topic Influence in Authorship Attribution. Eleni Argyri presented a study to explore
topic-independent features for authorship attribution. A wide range of stylometric features wereexamined including vocabulary richness variables, lexical features, sentence level measures, wordlength measures, and character level measures. Results on a Modern Greek corpus showed thatthe majority of the examined features are correlated with topic. By removing all these features
the author identification performance remains practically unaffected.
The last paper of this session by Jussi Karlgren and Gunnar Eriksson (Swedish Institute ofComputer Science) entitled Authors, Genre, and Linguistic Convention was presented by Jussi.
He proposed features for quantifying the use of clauses and adverbials within sentences taking into
account sequence patterns rather than averaging pointwise non-contextual observations. Anotherbasic claim of their work is that intrinsic rather than extrinsic evaluation should be used to estimate
the optimal features for both genre detection and authorship attribution. To this end, they propose
the Kullback-Leibler divergence. The afternoon session was opened by the paper Adaption ofString Matching Algorithms for Identification of Near-Duplicate Music Documents by Mattias
Robine, Pierre Hanna, Pascal Ferraro, and Julien Allali (Universite de Bordeaux 1). Mattias
presented a plagiarism detection approach for symbolic music documents. The proposed method
mainly considers melody but takes also into account elements of music theory to detect musicallyimportant differences. Several experiments using known cases of music plagiarism indicate theACM SIGIR Forum69Vol. 41 No. 2 December 2007

viability of the proposed approach.
Finally, a work about Intrinsic Plagiarism Analysis with Meta Learning by Benno Stein andSven Meyer zu Eissen (Bauhaus University Weimar) was presented by Sven. He focused on the caseof plagiarism detection without a reference collection. Essentially, this implies the quantificationof writing style changes within a document. Using a test corpus they showed that the unmasking
technology for authorship verification (developed by Koppel and Schler) also works for pretty shorttexts. In particular Benno and Sven use unmasking to obtain further evidence whether certain
portions of a text, which were extracted with style heuristics, stem from another author. As areal-world case Sven exhibited the results of discriminating the plagiarized sections of a German
habilitation thesis.

Open Questions
One of the key issues for both authorship attribution and plagiarism analysis is the lack of benchmark corpora to evaluate the proposed approaches. There is need for publicly-available large
corpora covering several natural languages and providing control over topic, genre, period, etc.
Thus, it is not yet feasible to conduct a wide range of comparative evaluation of different methods
on the same data.
Another interesting open question is the optimal set of features for representing text in theframework of stylistic analysis. So far, the proposed features range from low-level measures suchas character n-grams to lexical features and high-level measures such as syntactic analysis featuresor sequential features. Moreover, it is not yet clear how certain factors such as genre, authorship,
and theme affect text representation features. On the other hand, compression-based approaches
offer a parameter-free solution to this problem. However, they usually require significantly higher
computational cost and their performance in many cases are poor.
Perhaps the most crucial factor is text-length. Although there are now robust methods able todeal with short texts (with less than 1,000 words), it is yet impossible to estimate a certain limitof text-length for appropriately quantifying the stylistic properties of the text. The definition ofsuch a shortest text unit would allow the efficient segmentation of long texts into chunks so thatto enable the detection of changes of writing style within a document.
Finally, another open question is the development of systems for plagiarism analysis, authorship
attribution, or authorship verification that are able to explain their decisions. To illustrate thisfurther, an author identification system used in the framework of a forensic application should be
able to determine why a certain person is considered quite likely to be the author of a particular
document the way a forensic science human expert would do it. To this end, the stylistic analysis
systems should be able to match certain textual patterns with high-level stylistic characteristics.

FutureIt turns out that the most important use cases for the uncovering of plagiarism and authorshipwill come from the World Wide Web: the WWW is the world’s largest document repository,communication platform, and exhibition place. And, the Web provides individual access, public

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address—along with anonymity at the same time. Most of the participating researchers at theworkshop declared to address Web issues explicitly in their current and future work.
Especially with the Web we see another kind malicious user behavior and its problems coming
into the research focus: the detection of individual content manipulation, e. g. in the form ofvandalism and edit wars. Though this a sort of “adversarial” behavior this kind of phenomena
has not been addressed by the adversarial IR community: Adversarial Information Retrieval on
the Web (see the workshop on the SIGIR’06 among others) deals primarily with mass phenomenaand “pollution” problems such as link-bombing, comment or blog spam, or search engine spamand optimization.

Acknowledgements
We would like to thank the SIGIR, the authors and presenters, and the renowned experts who
served on the program committee for their contributions to make this high-level and exciting
workshop possible!

References• The workshop program can be found at
http://www.uni-weimar.de/medien/webis/research/pan-07/program.html• The workshop proceedings can be found at
http://ceur-ws.org/Vol-276

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