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.

Authorship attribution attempts to reveal the authors of documents. In recent years, research in this field has grown rapidly. However, the performance of state‐of‐the‐art methods is heavily affected when text of known authorship and texts under investigation differ in topic and/or genre. So far, it is not clear how to quantify the personal style of authors in a way that is not affected by topic shifts or genre variations. In this paper, a set of text distortion methods are used attempting to mask topic‐related information. These methods transform the input texts into a more topic‐neutral form while maintaining the structure of documents associated with the personal style of the author. Using a controlled corpus that includes a fine‐grained range of topics and genres it is demonstrated how the proposed approach can be combined with existing authorship attribution methods to enhance their performance in very challenging tasks, especially in cross‐topic attribution. We also examine cross‐genre attribution and the most challenging, yet realistic, cross‐topic‐and‐genre attribution scenarios and show how the proposed techniques should be tuned to enhance performance in these tasks. Finally, we demonstrate that there are important differences in attribution effectiveness when either conversational genres, nonconversational genres, or a mix of them are considered.

Authorship attribution is associated with important applications in forensics and humanities research.A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre.In this paper, we present a novel method that enhances authorship attribution effectiveness by introducing a text distortion step before extracting stylometric measures.The proposed method attempts to mask topicspecific information that is not related to the personal style of authors.Based on experiments on two main tasks in authorship attribution, closed-set attribution and authorship verification, we demonstrate that the proposed approach can enhance existing methods especially under cross-topic conditions, where the training and test corpora do not match in topic.

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.

Similarly to natural language texts, source code documents can be distinguished by their style. Source code author identification can be viewed as a text classification task given that samples of known authorship by a set of candidate authors are available. Although very promising results have been reported for this task, the evaluation of existing approaches avoids focusing on the class imbalance problem and its effect on the performance. In this paper, we present a systematic experimental study of author identification in skewed training sets where the training samples are unequally distributed over the candidate authors. Two representative author identification methods are examined, one follows the profile-based paradigm (where a single representation is produced for all the available training samples per author) and the other follows the instance-based paradigm (where each training sample has its own individual representation). We examine the effect of the source code representation on the performance of these methods and show that the profile-based method is better able to handle cases of highly skewed training sets while the instance-based method is a better choice in balanced or slightly-skewed training sets.

The discovery of web documents about certain topics is an important task for web-based applications including web document retrieval, opinion mining and knowledge extraction. In this paper, we propose an agent-based focused crawling framework able to retrieve topic- and genre-related web documents. Starting from a simple topic query, a set of focused crawler agents explore in parallel topic-specific web paths using dynamic seed URLs that belong to certain web genres and are collected from web search engines. The agents make use of an internal mechanism that weighs topic and genre relevance scores of unvisited web pages. They are able to adapt to the properties of a given topic by modifying their internal knowledge during search, handle ambiguous queries, ignore irrelevant pages with respect to the topic and retrieve collaboratively topic-relevant web pages. We performed an experimental study to evaluate the behavior of the agents for a variety of topic queries demonstrating the benefits and the capabilities of our framework.

In this paper a novel method for detecting plagiarized passages in document collections is presented. In contrast to previous work in this field that uses mainly content terms to represent documents, the proposed method is based on structural information provided by occurrences of a small list of stopwords (i.e., very frequent words). We show that stopword n-grams are able to capture local syntactic similarities between suspicious and original documents. Moreover, an algorithm for detecting the exact boundaries of plagiarized and source passages is proposed. Experimental results on a publicly-available corpus demonstrate that the performance of the proposed approach is competitive when compared with the best reported results. More importantly, it achieves significantly better results when dealing with difficult plagiarism cases where the plagiarized passages are highly modified by replacing most of the words or phrases with synonyms to hide the similarity with the source documents.

In this paper a novel method for detecting plagiarized passages in document collections is presented. In contrast to previous work in this field that uses content terms to represent documents, the proposed method is based on a small list of stopwords (i.e., very frequent words). We show that stopword n-grams reveal important information for plagiarism detection since they are able to capture syntactic similarities between suspicious and original documents and they can be used to detect the exact plagiarized passage boundaries. Experimental results on a publicly available corpus demonstrate that the performance of the proposed approach is competitive when compared with the best reported results. More importantly, it achieves significantly better results when dealing with difficult plagiarism cases where the plagiarized passages are highly modified and most of the words or phrases have been replaced with synonyms.

Abstract Authorship attribution supported by statistical or computational methods has a long history starting from the 19th century and is marked by the seminal study of Mosteller and Wallace (1964) on the authorship of the disputed “Federalist Papers.” During the last decade, this scientific field has been developed substantially, taking advantage of research advances in areas such as machine learning, information retrieval, and natural language processing. The plethora of available electronic texts (e.g., e‐mail messages, online forum messages, blogs, source code, etc.) indicates a wide variety of applications of this technology, provided it is able to handle short and noisy text from multiple candidate authors. In this article, a survey of recent advances of the automated approaches to attributing authorship is presented, examining their characteristics for both text representation and text classification. The focus of this survey is on computational requirements and settings rather than on linguistic or literary issues. We also discuss evaluation methodologies and criteria for authorship attribution studies and list open questions that will attract future work in this area.

Author identification is a text categorization task with applications in intelligence, criminal law, computer forensics, etc. Usually, in such cases there is shortage of training texts. In this paper, we propose the use of second order tensors for representing texts for this problem, in contrast to the traditional vector space model. Based on a generalization of the SVM algorithm that can handle tensors, we explore various methods for filling the matrix of features taking into account that similar features should be placed in the same neighborhood. To this end, we propose a frequency-based metric. Experiments on a corpus controlled for genre and topic and variable amount of training texts show that the proposed approach is more effective than traditional vector-based SVM when only limited amount of training texts is used.

An important factor for discriminating between Web pages is their genre (e.g., blogs, personal homepages, e-shops, online newspapers, etc). Web page genre identification has a great potential in information retrieval since users of search engines can combine genre-based and traditional topic-based queries to improve the quality of the results. So far, various features have been proposed to quantify the style of Web pages including word and HTML-tag frequencies. In this paper, we propose a low-level representation for this problem based on character n-grams. Using an existing approach, we produce feature sets of variable-length character n- grams and combine this representation with information about the most frequent HTML-tags. Based on two benchmark corpora, we present Web page genre identification experiments and improve the best reported results in both cases.

Source code author identification deals with the task of identifying the most likely author of a computer program, given a set of predefined author candidates. This is usually .based on the analysis of other program samples of undisputed authorship by the same programmer. There are several cases where the application of such a method could be of a major benefit, such as authorship disputes, proof of authorship in court, tracing the source of code left in the system after a cyber attack, etc. We present a new approach, called the SCAP (Source Code Author Profiles) approach, based on byte-level n-gram profiles in order to represent a source code author's style. Experiments on data sets of different programming-language (Java or C++) and varying difficulty (6 to 30 candidate authors) demonstrate the effectiveness of the proposed approach.A comparison with a previous source code authorship identification study based on more complicated information shows that the SCAP approach is language independent and that n-gram author profiles are better able to capture the idiosyncrasies of the source code authors. Moreover, the SCAP approach is able to deal surprisingly well with cases where only a limited amount of very short programs per programmer is available for training. It is also demonstrated that the effectiveness of the proposed model is not affected by the absence of comments in the source code, a condition usually met in cyber-crime cases.

Presents a character segmentation algorithm for unconstrained cursive handwritten text. The transformation-based learning method and a simplified variation of it are used in order to extract automatically rules that detect the segment boundaries. Comparative experimental results are given for a collection of multiwriter handwritten words. The achieved accuracy in detecting segment boundaries exceeds 82%. Moreover limited training data can provide very satisfactory results.

The two main factors that characterize a text are its content and its style, and both can be used as a means of categorization. In this paper we present an approach to text categorization in terms of genre and author for Modern Greek. In contrast to previous stylometric approaches, we attempt to take full advantage of existing natural language processing (NLP) tools. To this end, we propose a set of style markers including analysis-level measures that represent the way in which the input text has been analyzed and capture useful stylistic information without additional cost. We present a set of small-scale but reasonable experiments in text genre detection, author identification, and author verification tasks and show that the proposed method performs better than the most popular distributional lexical measures, i.e., functions of vocabulary richness and frequencies of occurrence of the most frequent words. All the presented experiments are based on unrestricted text downloaded from the World Wide Web without any manual text preprocessing or text sampling. Various performance issues regarding the training set size and the significance of the proposed style markers are discussed. Our system can be used in any application that requires fast and easily adaptable text categorization in terms of stylistically homogeneous categories. Moreover, the procedure of defining analysis-level markers can be followed in order to extract useful stylistic information using existing text processing tools.

This paper describes a user-assisted business letter generator that meets the ever-increasing demand for more flexible and modular letter generators which draw on explicit thematic models and which are easily adaptable to specific user needs. Based on a detailed analysis of requirements and taking full advantage of end-user feedback, the presented generator not only creates a business letter according to the user's choices, it also refines it, taking into consideration stylistic aspects like written style and tone.

The presented work is strongly motivated by the need for categorizing unrestricted text in terms of a functional style (FS) in order to attain a satisfying outcome in style processing. Towards this aim, a three level description of FS is given that comprises: (a) the basic categories of FS; (b) the main features that characterize each one of the above categories; and (c) the linguistic identifiers that act as style markers in text for the identification of the above features. Special emphasis is put on the problems that faced the computational implementation of the aforementioned findings, as well as the selection of the most appropriate stylometrics (i.e., stylistic scores) to achieve better results on text categorization. This approach is language independent, empirically driven, and can be used in various applications including grammar and style checking, natural language generation, style verification in real world text, and recognition of style shift between adjacent portions of text.