A Classifier System for Author Recognition Using Synonym-Based Features

Clark, Jonathan H.; Hannon, Charles

doi:10.1007/978-3-540-76631-5_80

Cited by 25 publications

(11 citation statements)

References 17 publications

(17 reference statements)

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“…This method exploits the choice of a specific word given all the possible alternatives that exist. The theory behind this method is that when a word has a large number of synonyms, the choice the author makes is significant in understanding his or her writing style [Clark and Hannon 2007]. An example analysis of three sentences can be seen in Figure 1.…”

Section: Methods and Feature Setsmentioning

confidence: 99%

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Adversarial stylometry

Brennan

Afroz

Greenstadt

2012

ACM Trans. Inf. Syst. Secur.

131

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The use of stylometry, authorship recognition through purely linguistic means, has contributed to literary, historical, and criminal investigation breakthroughs. Existing stylometry research assumes that authors have not attempted to disguise their linguistic writing style. We challenge this basic assumption of existing stylometry methodologies and present a new area of research: adversarial stylometry. Adversaries have a devastating effect on the robustness of existing classification methods. Our work presents a framework for creating adversarial passages including obfuscation, where a subject attempts to hide her identity, and imitation, where a subject attempts to frame another subject by imitating his writing style, and translation where original passages are obfuscated with machine translation services. This research demonstrates that manual circumvention methods work very well while automated translation methods are not effective. The obfuscation method reduces the techniques' effectiveness to the level of random guessing and the imitation attempts succeed up to 67% of the time depending on the stylometry technique used. These results are more significant given the fact that experimental subjects were unfamiliar with stylometry, were not professional writers, and spent little time on the attacks. This article also contributes to the field by using human subjects to empirically validate the claim of high accuracy for four current techniques (without adversaries). We have also compiled and released two corpora of adversarial stylometry texts to promote research in this field with a total of 57 unique authors. We argue that this field is important to a multidisciplinary approach to privacy, security, and anonymity.

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Section: Methods and Feature Setsmentioning

confidence: 99%

“…In this case the unknown sentence is correctly attributed to author X because of the highly salient feature of choosing the word "verdant"[Clark and Hannon 2007].…”

mentioning

confidence: 94%

Adversarial stylometry

Brennan

Afroz

Greenstadt

2012

ACM Trans. Inf. Syst. Secur.

131

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“…It could also be the case that it is easier for finer-grained language style to show distinctions. One evident example is author recognition based on an individual's frequent word choices (Clark and Hannon, 2007). Another example is persona-based dialog system that not only captures background knowledge of a user (Li et al, 2016) but also speaking style (Mizukami et al, 2015).…”

Section: Gendered and Personalizedmentioning

confidence: 99%

From Shakespeare to Twitter: What are Language Styles all about?

Xu¹

2017

Proceedings of the Workshop on Stylistic Variation

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As natural language processing research is growing and largely driven by the availability of data, we expanded research from news and small-scale dialog corpora to web and social media. User-generated data and crowdsourcing opened the door for investigating human language of various styles with more statistical power and real-world applications. In this position/survey paper, I will review and discuss seven language styles that I believe to be important and interesting to study: influential work in the past, challenges at the present, and potential impact for the future.

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“…We then compared the results with those of two other state-of-the-art authorship attribution methods in the literature: the Writeprints method and the synonym-based approach [17]. The experiments with JStylo were conducted using a SVM classifier, over two feature sets: the Basic-9 and the Writeprints (Limited).…”

Section: Effectiveness Of Jstylomentioning

confidence: 99%

Use Fewer Instances of the Letter “i”: Toward Writing Style Anonymization

McDonald

Afroz

Çalışkan

et al. 2012

Lecture Notes in Computer Science

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Abstract. This paper presents Anonymouth, a novel framework for anonymizing writing style. Without accounting for style, anonymous authors risk identification. This framework is necessary to provide a tool for testing the consistency of anonymized writing style and a mechanism for adaptive attacks against stylometry techniques. Our framework defines the steps necessary to anonymize documents and implements them. A key contribution of this work is this framework, including novel methods for identifying which features of documents need to change and how they must be changed to accomplish document anonymization. In our experiment, 80% of the user study participants were able to anonymize their documents in terms of a fixed corpus and limited feature set used. However, modifying pre-written documents were found to be difficult and the anonymization did not hold up to more extensive feature sets. It is important to note that Anonymouth is only the first step toward a tool to acheive stylometric anonymity with respect to state-of-the-art authorship attribution techniques. The topic needs further exploration in order to accomplish significant anonymity.

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A Classifier System for Author Recognition Using Synonym-Based Features

Cited by 25 publications

References 17 publications

Adversarial stylometry

Adversarial stylometry

From Shakespeare to Twitter: What are Language Styles all about?

Use Fewer Instances of the Letter “i”: Toward Writing Style Anonymization

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