Computational methods in authorship attribution
01 Jan 2009-Journal of the Association for Information Science and Technology (John Wiley & Sons, Ltd)-Vol. 60, Iss: 1, pp 9-26
TL;DR: Three scenarios are considered here for which solutions to the basic attribution problem are inadequate; it is shown how machine learning methods can be adapted to handle the special challenges of that variant.
Abstract: Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample. In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant. © 2009 Wiley Periodicals, Inc.
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20 May 2012
TL;DR: In over 20% of cases, the classifiers can correctly identify an anonymous author given a corpus of texts from 100,000 authors; in about 35% of Cases the correct author is one of the top 20 guesses.
Abstract: We study techniques for identifying an anonymous author via linguistic stylometry, i.e., comparing the writing style against a corpus of texts of known authorship. We experimentally demonstrate the effectiveness of our techniques with as many as 100,000 candidate authors. Given the increasing availability of writing samples online, our result has serious implications for anonymity and free speech - an anonymous blogger or whistleblower may be unmasked unless they take steps to obfuscate their writing style. While there is a huge body of literature on authorship recognition based on writing style, almost none of it has studied corpora of more than a few hundred authors. The problem becomes qualitatively different at a large scale, as we show, and techniques from prior work fail to scale, both in terms of accuracy and performance. We study a variety of classifiers, both "lazy" and "eager," and show how to handle the huge number of classes. We also develop novel techniques for confidence estimation of classifier outputs. Finally, we demonstrate stylometric authorship recognition on texts written in different contexts. In over 20% of cases, our classifiers can correctly identify an anonymous author given a corpus of texts from 100,000 authors; in about 35% of cases the correct author is one of the top 20 guesses. If we allow the classifier the option of not making a guess, via confidence estimation we are able to increase the precision of the top guess from 20% to over 80% with only a halving of recall.
290 citations
01 Mar 2012
TL;DR: A new taxonomy of plagiarism is presented that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view, and supports deep understanding of different linguistic patterns in committing plagiarism.
Abstract: Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words.
275 citations
01 Mar 2011
TL;DR: This paper shows the precise relationship between attribution precision and four parameters: the size of the candidate set, the quantity of known-text by the candidates, the length of the anonymous text and a certain robustness score associated with a attribution.
Abstract: Most previous work on authorship attribution has focused on the case in which we need to attribute an anonymous document to one of a small set of candidate authors. In this paper, we consider authorship attribution as found in the wild: the set of known candidates is extremely large (possibly many thousands) and might not even include the actual author. Moreover, the known texts and the anonymous texts might be of limited length. We show that even in these difficult cases, we can use similarity-based methods along with multiple randomized feature sets to achieve high precision. Moreover, we show the precise relationship between attribution precision and four parameters: the size of the candidate set, the quantity of known-text by the candidates, the length of the anonymous text and a certain robustness score associated with a attribution.
255 citations
TL;DR: This article offers an (almost) unsupervised method for solving the authorship attribution problem by using repeated feature subsampling methods to determine if one document of the pair allows us to select the other from among a background set of “impostors” in a sufficiently robust manner.
Abstract: Almost any conceivable authorship attribution problem can be reduced to one fundamental problem: whether a pair of (possibly short) documents were written by the same author. In this article, we offer an (almost) unsupervised method for solving this problem with surprisingly high accuracy. The main idea is to use repeated feature subsampling methods to determine if one document of the pair allows us to select the other from among a background set of “impostors” in a sufficiently robust manner.
191 citations
TL;DR: It is argued that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.
Abstract: The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor has drastically complicated the task of identifying users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author’s identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.
189 citations
Cites background from "Computational methods in authorship..."
...Many other works exist in the broader field of authorship attribution, and we refer the interested reader to the existing surveys [89], [108], [175] that are more general in scope....
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