On the Usefulness of Compression Models for Authorship Verification

Halvani, Oren; Winter, Christian; Graner, Lukas

doi:10.1145/3098954.3104050

Cited by 21 publications

(21 citation statements)

References 14 publications

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“…As a second corpus, we compiled C Perv , which represents a collection of 1,645 chat conversations of 550 sex offenders crawled from the Perverted-Justice portal 10 . The chat conversations stem from a variety of sources including emails and instant messengers (e. g., MSN, AOL or Yahoo), where for each conversation, we ensured that only chat lines from the offender were extracted.…”

Section: Perverted Justice Corpusmentioning

confidence: 99%

Assessing the Applicability of Authorship Verification Methods

Halvani

Winter

Graner

2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respective studies reveals that the underlying characteristics of these methods are rarely addressed, which raises doubts regarding their applicability in real forensic settings. The objective of this paper is to fill this gap by proposing clear criteria and properties that aim to improve the characterization of existing and future AV approaches. Based on these properties, we conduct three experiments using 12 existing AV approaches, including the current state of the art. The examined methods were trained, optimized and evaluated on three self-compiled corpora, where each corpus focuses on a different aspect of applicability. Our results indicate that part of the methods are able to cope with very challenging verification cases such as 250 characters long informal chat conversations (72.7% accuracy) or cases in which two scientific documents were written at different times with an average difference of 15.6 years (> 75% accuracy). However, we also identified that all involved methods are prone to cross-topic verification cases.

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Section: Perverted Justice Corpusmentioning

confidence: 99%

Assessing the Applicability of Authorship Verification Methods

Halvani

Winter

Graner

2019

Proceedings of the 14th International Conference on Availability, Reliability and Security

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“…As baselines, we use the best results reported by Halvani and Graner () on the same corpus. In more detail, the best results so far in this corpus are obtained by a profile‐based and intrinsic verification method based on text compression (Halvani et al, ) and the GI method (Seidman, ). As expected, the set of e‐mail impostors assists extrinsic methods to achieve higher results in comparison to the where case genre‐agnostic external documents are used.…”

Section: Experimental Studymentioning

confidence: 99%

“…It is remarkable that the top-performing approaches in all three PAN shared tasks in author verification follow this paradigm (Bagnall, 2015;Khonji & Iraqi, 2014;Seidman, 2013). However, recent studies show that intrinsic methods can be equally competitive (Ding et al, 2019;Halvani, Winter, & Graner, 2017;Potha & Stamatatos, 2018). Extrinsic methods are heavily influenced by the suitability of documents in D external for a given verification case.…”

Section: Previous Workmentioning

confidence: 99%

“…LSI* and LDA* refer to topic models extracted from the enriched document collection while LSI and LDA are topic models extracted from the training documents only. text compression (Halvani et al, 2017) and the GI method (Seidman, 2013). As expected, the set of e-mail impostors assists extrinsic methods to achieve higher results in comparison to the where case genre-agnostic external documents are used.…”

Section: Effect Of Genre Of External Documentsmentioning

confidence: 99%

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Improving author verification based on topic modeling

Potha

Stamatatos

2019

Asso for Info Science & Tech

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Authorship analysis attempts to reveal information about authors of digital documents enabling applications in digital humanities, text forensics, and cyber‐security. Author verification is a fundamental task where, given a set of texts written by a certain author, we should decide whether another text is also by that author. In this article we systematically study the usefulness of topic modeling in author verification. We examine several author verification methods that cover the main paradigms, namely, intrinsic (attempt to solve a one‐class classification task) and extrinsic (attempt to solve a binary classification task) methods as well as profile‐based (all documents of known authorship are treated cumulatively) and instance‐based (each document of known authorship is treated separately) approaches combined with well‐known topic modeling methods such as Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). We use benchmark data sets and demonstrate that LDA is better combined with extrinsic methods, while the most effective intrinsic method is based on LSI. Moreover, topic modeling seems to be particularly effective for profile‐based approaches and the performance is enhanced when latent topics are extracted by an enriched set of documents. The comparison to state‐of‐the‐art methods demonstrates the great potential of the approaches presented in this study. It is also demonstrates that even when genre‐agnostic external documents are used, the proposed extrinsic models are very competitive.

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“…Syntactic features, on the other hand, are less sensitive to content but require the use of a robust part-of-speech tagger. In addition, non-linguistic features such as compression-based models were shown to be successful as well [13,14]. The employed compression model, i.e.…”

Section: Introductionmentioning

confidence: 99%

Similarity Learning for Authorship Verification in Social Media

Boenninghoff

Nickel

Zeiler

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not. A range of successful technical approaches has been proposed for this task, many of which are based on traditional linguistic features such as n-grams. These algorithms achieve good results for certain types of written documents like books and novels. Forensic authorship verification for social media, however, is a much more challenging task since messages tend to be relatively short, with a large variety of different genres and topics. At this point, traditional methods based on features like n-grams have had limited success. In this work, we propose a new neural network topology for similarity learning that significantly improves the performance on the author verification task with such challenging data sets.

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On the Usefulness of Compression Models for Authorship Verification

Cited by 21 publications

References 14 publications

Assessing the Applicability of Authorship Verification Methods

Assessing the Applicability of Authorship Verification Methods

Improving author verification based on topic modeling

Similarity Learning for Authorship Verification in Social Media

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