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The dark web is a part of the deep web that ensures anonymity to users, thus facilitating various malicious activities, such as the sales of drugs, firearms, and personal information or the dissemination of malware and cyberattack tools. These activities extend beyond the dark web and have negative effects on the surface web, which is commonly accessed by internet users. Recent studies on the dark web are limited to the detection and classification of specific malicious activities; that is, they cannot trace or identify the authors of dark web content or the source of a given information Therefore, we herein propose a method for identifying similar authors between the surface and dark webs using BERTopic and authorship attribution. We applied BERTopic to the surface and dark webs to extract previously unidentified topics and measured the similarity between the topics to detect similar topics between the two webs. In addition, we applied authorship attribution to the contents written by the authors of similar topics to extract the unique author characteristics. The similarity between the authors was measured to identify authors with similar characteristics. Thus, we identified authors who had written contents on similar topics on both the surface and dark webs as well as authors who are simultaneously active on both webs. [ABSTRACT FROM AUTHOR]

Automated author identification is one of the important fields in forensic linguistics. In this study, the effectiveness of systemic functional grammar (Halliday and Matthiessen, 2014) features in Persian authorship attribution was compared with that of function words. First, a corpus composed of documents written by seven contemporary Iranian authors was collected. Second, a list of function words was extracted from the corpus. Moreover, conjunction, modality and comment adjunct system networks were applied to form a lexicon using linguistics resources. Then, the relative frequency of function words in addition to systemic functional features were calculated in each document. Multilayer perceptron classifier, a type of neural network, was used for learning phase which resulted in a desirable accuracy in evaluation phase. The results of the study showed that using function words method is superior to systemic functional approach alone in Persian author identification, however, simultaneous use of the two methods increases the effectiveness in comparison to each alone. Introduction Recently, automated author identification has become a key focus for forensic linguistics. Author identification involves determining the writer of a text from a set of potential authors. The text in question could be a threatening letter, an email, a literary work, or a scientific article or book. The basis for author identification rests on the idea that different authors may write about the same topic using overlapping, yet distinct, lexico-grammatical units—an issue referred to as idiolect (Coulthard, 2004). The first significant attempt to identify writing styles was Mendenhall's study of Shakespeare's plays (1887). The play Henry VIII is widely recognized as a collaborative work, not solely authored by William Shakespeare. Plechac (2020) investigated the use of accent or stress to identify the contributions of other authors to the play. In Persian, several studies have been conducted to determine authorship (Farahamandpour et al., 2013; Arefi et al., 2021). These studies utilized repetitive features, such as lexical richness, frequency of syntactic groups, collocations, and the relative frequency of punctuation marks, to detect writing styles. Measuring the frequency of function words is one valid method for author identification. Function words, which have limited meanings, indicate the functional relationships between components of a sentence. Golshaie (2019) and Dabagh (2007) applied the frequency of Persian function words to identify authors. This study aims to compare the efficacy of function word frequency with systemic functional grammar methods in automatically identifying writing styles. Theoretical Framework Systemic Functional Grammar (SFG) is a component of the social semiotic approach to language known as systemic functional linguistics (Halliday & Matthiessen, 2014). SFG conceptualizes language as a network of systems, or interrelated sets of options for creating meaning. Since the 1960s, SFG has been applied in various contexts within computational linguistics (Matthiessen & Bateman, 1991; Teich, 1995). In SFG, the clause is considered the fundamental unit of language, and it is analyzed through three perspectives, defined as the ideational, interpersonal, and textual metafunctions. The ideational function is further divided into the experiential and logical aspects (Halliday & Matthiessen, 2014). This study employs three system networks: conjunction, modality, and comment. These networks correspond to three types of adjuncts: conjunctive adjuncts, mood adjuncts, and comment adjuncts, respectively. In the systemic environment of conjunction, conjunctions function as conjunctive adjuncts within the clause structure. They establish relationships where one segment of text elaborates on, extends, or enhances another segment (Halliday & Matthiessen, 2014). Modal adjuncts express the speaker's or writer's judgment or attitude toward the content of the message. There are two types of modal adjuncts: (i) mood adjuncts and (ii) comment adjuncts. Mood adjuncts and comment adjuncts are categorized within the modality and comment adjunct system networks, respectively. Modality encompasses intermediate degrees between positive and negative poles, defining the region of uncertainty between 'yes' and 'no.' The modality system allows writers to qualify events or entities in terms of their probability, typicality, obligation, or inclination (Halliday & Matthiessen, 2014). The comment adjunct system provides a means for the writer to comment on the status of a message concerning the textual and interactive context of the discourse (Argamon et al., 2007). Comments can target either the ideational content of the proposition or the interpersonal aspects of the speech function (Halliday & Matthiessen, 2014). Method A corpus was compiled from the works of seven contemporary Persian writers: Hoshang Golshiri, Bozorg Alavi, Ahmad Mahmoud, Mahmoud Dolatabadi, Nader Ebrahimi, Jalal Al-e Ahmad, and Gholamhossein Saedi, totaling 2,069,243 words. From this corpus, a list of 197 function words was extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) technique. Conjunction, modality, and comment adjunct system networks were then used to create a lexicon. An author identification system was designed using machine learning techniques. The system tokenized the texts, extracted instances of lexical units specified in the lexicon, and computed the relative frequencies of semantic attribute values for each text, resulting in an overall "feature vector" that described each text. This approach was inspired by the method introduced by Argamon et al. (2007). For the learning phase, a multilayer perceptron classifier, a type of neural network, was utilized. Results To evaluate the system, the collected corpus was divided into five segments, and a 5-fold cross-validation method was applied. The 5-fold cross-validation demonstrated a satisfactory accuracy when focusing exclusively on function words. The combined use of function words and SFG methods achieved an accuracy of 74.47% for Persian author identification. Subsequent feature selection identified the most effective features for the machine learning phase. The results indicated that the relative frequency of function words outperformed SFG-based attributes in terms of effectiveness. Discussion and Conclusions The evaluation phase revealed that the function words-based method outperformed the systemic functional grammar (SFG) approach in identifying authors. However, the simultaneous use of both methods improved effectiveness compared to using either method alone. The superior performance of the function words-based method may be attributed to the high frequency of function words and the author’s unconscious control over their use. Among the SFG-based features, the combination of top features—namely conjunctive, mood, and comment adjuncts—produced higher accuracy than any single system network alone. Additionally, the results from feature selection indicated that features derived from the modality system network were more effective than those from the conjunction and comment adjunct system networks for Persian author identification. Overall, while the function words-based method proved to be highly effective on its own, integrating it with SFG-based methods provided a more comprehensive approach, enhancing the accuracy of author identification.

The authors used various methods and models in the computer system for text author recognition to recognize the authorship of texts via the example of Azerbaijani writers. They compared the effectiveness of using different text features and proposed feature selection procedures. The authors conducted computer experiments on the works of several famous Azerbaijani writers in the Azerbaijani language and analyzed the results obtained. [ABSTRACT FROM AUTHOR]

Various methods and models are used in the text author recognition computer system for recognizing the authorship of texts on the example of Azerbaijani writers. A comparative analysis is carried out for the efficiency of using different text features and proposed feature selection procedures. Computer experiments are conducted using the works of several famous Azerbaijani writers in Azerbaijani language, and the results are analyzed. [ABSTRACT FROM AUTHOR]

Automated author identification is one of the important fields in forensic linguistics. In this study, the effectiveness of systemic functional grammar (Halliday and Matthiessen, 2014) features in Persian authorship attribution was compared with that of function words. First, a corpus composed of documents written by seven contemporary Iranian authors was collected. Second, a list of function words was extracted from the corpus. Moreover, conjunction, modality and comment adjunct system networks were applied to form a lexicon using linguistics resources. Then, the relative frequency of function words in addition to systemic functional features were calculated in each document. Multilayer perceptron classifier, a type of neural network, was used for learning phase which resulted in a desirable accuracy in evaluation phase. The results of the study showed that using function words method is superior to systemic functional approach alone in Persian author identification, however, simultaneous use of the two methods increases the effectiveness in comparison to each alone. [ABSTRACT FROM AUTHOR]

This paper presents various machine learning methods with different text features that are explored and evaluated to determine the authorship of the texts in the example of the Azerbaijani language. We consider techniques like artificial neural network, convolutional neural network, random forest, and support vector machine. These techniques are used with different text features like word length, sentence length, combined word length and sentence length, n-grams, and word frequencies. The models were trained and tested on the works of many famous Azerbaijani writers. The results of computer experiments obtained by utilizing a comparison of various techniques and text features were analyzed. The cases where the usage of text features allowed better results were determined. [ABSTRACT FROM AUTHOR]

Key points: The ORCID DE project, from 2016 to 2022, promoted ORCID in Germany through a project‐driven approach.The project promoted the dissemination of ORCID in Germany. The number of IDs registered for Germany increased from approximately 44,000 in 2016 to 300,070 in 2023.The linkage of ORCID with the bibliographic Gemeinsame Normdatei (GND; translated: Integrated Authority File) standard in German‐speaking countries has contributed to the success of ORCID in Germany.The promotion of ORCID in Germany was based on six central areas: information, consortium, standardization, implementation, monitoring, and networking.The ORCID DE Monitor captures and analyses the development of ORCID on a national and international level.The ACTION principles highlight crucial measures in promoting a standard, such as ORCID. ACTION stands for Awareness, ConnectiviTy, IntegratiOn, and Networking. [ABSTRACT FROM AUTHOR]

This article deals with the topic of anonymous extortion letters. Anonymous extortion letters are a challenge for linguists and criminal intelligence services and raise a whole range of questions that ultimately allow a creation of the psycholinguistic portrait of the possible author/-perpetrator. One of these questions concerns the emotional layer of the anonymous letter and the way anonymous authors express their emotions in language. This article describes a specific type of speech genre, which is anonymous extortion letter and analyses, in short, emotions in language. The last part presents the author’s own research, focusing on the characteristics of simulated and falsified extortion letters in the Dutch and Polish languages.

This article deals with the topic of anonymous extortion letters. Anonymous extortion letters are a challenge for linguists and criminal intelligence services and raise a whole range of questions that ultimately allow a creation of the psycholinguistic portrait of the possible author/-perpetrator. One of these questions concerns the emotional layer of the anonymous letter and the way anonymous authors express their emotions in language. This article describes a specific type of speech genre, which is anonymous extortion letter and analyses, in short, emotions in language. The last part presents the author's own research, focusing on the characteristics of simulated and falsified extortion letters in the Dutch and Polish languages. [ABSTRACT FROM AUTHOR]

Identifying authors relies on their unique writing patterns, also known as stylometry. However, as each individual's stylometry may differ, it can be challenging to determine the true author, especially when multiple documents exist. This difficulty is compounded by similarities in writing styles, such as font and language, which can obscure the author's identity. To address this issue, machine learning techniques can be employed to identify human attitudes in written documents. By analyzing patterns in human behavior, it is possible to enhance privacy and security by detecting malicious users and malware programs. However, the use of behavior analysis may raise privacy concerns, particularly for individuals seeking to conceal their identity. Authorship attribution involves using stylometry techniques to identify the authors of multiple documents, and can aid in accurately identifying authors. In this research, we propose using stylometry to extract the number of programmers from a given database, and to analyze different datasets to determine whether a program's coding style remains consistent. This analysis can enhance the reliability and quality of programming, ultimately improving the overall efficiency of programming tasks. [ABSTRACT FROM AUTHOR]

Given an anonymous text, automatically attributing a name from a group of known writers is called "Authorship Attribution" (AA). It is a classification problem, and feature extraction techniques are initially applied, followed by the training of a model using a collection of texts whose authors are known. Numerous features, such as lexical, semantic, structural, n-grams, etc., can be used to identify the stylistic characteristics of writers. The authors of this research propose a novel approach to this problem by using sequential pattern mining on part-of-speech (PoS) tags. This paper introduces and discusses the concept of a Part-of-Speech Skip-Gram (PoSSG) that is different from traditional n-gram. A sequential pattern mining algorithm is applied to obtain PoSSG patterns, which are then used for authorship attribution tasks. Experimental studies on two different datasets: novels extracted from Project Gutenberg and Stamatatos06 Author Identification: C10-Attribution confirms that this approach of mining PoSSG patterns facilitates author identification. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing amount of data has brought the need to classify the data. Text classification is the process of categorizing similar text data. This paper aims to make a modeling study for author and genre identification, which is one of the important challenges of text classification, for Turkish news texts by using machine and deep learning algorithms. For this purpose, firstly, a total of 13 large-scale datasets having multi classes are built as new datasets. In the modeling stage, Multinomial Naïve Bayes (MNB), Random Forest (RF), Convolutional Neural Network (CNN), and Long Short Term Memory (LSTM) algorithms were applied to the datasets. Results showed that for dataset AI-TNKU-7, the CNN algorithm demonstrated the highest accuracy for author identification at 95.81%. In relation to genre identification, the LSTM algorithm for the dataset GI-TNKU-6 demonstrated the highest accuracy at 96.73%. [ABSTRACT FROM AUTHOR]

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electronic text stylometry is concerned with analyzing the writing styles of input electronic texts to extract information about their authors. For example, such extracted data could be the authors’ identity or other aspects, such as their gender and age group. This survey paper presents the following contributions: 1) A description of all stylometry problems in probability terms, under a unified notation. 2) A survey of data representation (or feature extraction) methods. 3) A comprehensive evaluation of 23, 760 feature extraction methods followed by a thorough discussion of the results. This extensive evaluation is critical since the known data representation methods are often not evaluated under the same unified testbed.

Yazar tanıma, adli dilbilimin belki de en önemli uygulamalarından bir tanesidir. Türkçe alan yazınına bakıldığında ise bu konudaki çalışmaların nispeten kısa bir süredir yapıldığı ve bu çalışmalarda çok kısa metinlerin kullanılmadığı görülmektedir (Amasyalı ve Diri, 2006; Taş ve Görür, 2007; Doğan ve Diri, 2010). Oysaki adli dilbilim çerçevesinde sadece birkaç sözcükten ibaret olan metinler dahi bir inceleme konusu olabilmektedir. Geçmişte, Twitter isimli mikro-blog sitesinde yayınlanan metinler (tweet) üzerinde yapılan bir çalışma neticesinde Türkçe alan yazınında bir yöntem önerilmiştir (Tanrıvere, 2018). Bu çalışma kapsamında ise söz konusu yöntem, yine Twitter iletilerinden oluşan yeni bir bütünce ile sınanmış ve yöntemi oluşturan ölçütler sayıca arttırılıp nitel bağlamda da detaylandırılarak geliştirilmeye çalışılmıştır. Yöntem, yazarının kim olduğu bilinmeyen iletilerin yazarı bilinen iletilerle karşılaştırılması neticesinde olumlu sonuçlar vermiştir. Uygulama sürecinde öngörülmemiş başka birtakım ayırt edici ölçütlerin de varlığı gözlenmiş ve bunlar detaylıca sunularak, yöntemdeki ilgili ölçüt gruplarına dahil edilmeleri önerilmiştir. Elde edilen sonuçlar genel olarak, çok kısa metinlerde dahi yazar tespitinin mümkün olduğunu ve yöntemin adli bağlamlarda da kullanılabileceğini göstermektedir.

Anonyme Briefe sind eine Herausforderung für Sprachwissenschaftler und werfen eine ganze Reihe von Fragen auf, die letztendlich ein sprachliches Porträt des wahrscheinlichen Autors ermöglichen sollen. Eine dieser Fragen betrifft die emotionale Ebene des anonymen Briefes und die Art und Weise, wie anonyme Autoren ihre Gefühle in der Sprache ausdrücken. Dieser Artikel ist ein Beitrag zur Diskussion und Analyse einer bestimmten Art von Sprachgenre, den anonymen Brief. Die Daten stammen aus einer Pilotstudie der originellen anonymen Briefe, deren Hauptzweck die Erstellung eines Korpus ist. // Anonymous letters are a challenge for linguists and raise a whole range of questions that ultimately allow a creation of the linguistic portrait of the possible author. One of these questions concerns the emotional layer of the anonymous letter and the way anonymous authors express their emotions in language. This article is a contribution to the discussion and analysis of a specific type of speech genre, which is anonymous letter. The data comes from a pilot study of the original anonymous let-ters, the primary purpose of which is to create a corpus of those letters.

Researcher services have proliferated in recent years and numerous free or fee-based sites now promise increased visibility and impact for authors or contributors of publications and other research products. Not all services have the same goals, however, and it can be difficult to know with which services researchers should engage. In this article we establish three categories (author/researcher identification, academic/professional networking, and reference/citation management) and examine nineteen services that fit into those categories. Column Editor's Note This JLA column posits that academic libraries and their services are dominated by information technologies, and that the success of librarians and professional staff is contingent on their ability to thrive in this technology-rich environment. The column will appear in odd-numbered issues of the journal, and will delve into all aspects of library-related information technologies and knowledge management used to connect users to information resources, including data preparation, discovery, delivery and preservation. Prospective authors are invited to submit articles for this column to the editor at kenning.arlitsch@montana.edu [ABSTRACT FROM AUTHOR]

Authorship Attribution is a process to determine and/or identify the author of a given text document. The relevance of this research area comes to the fore when two or more writers claim to be the prospective authors of an unidentified or anonymous text document or are unwilling to accept any authorship. This research work aims to utilize various Machine Learning techniques in order to solve the problem of author identification. In the proposed approach, a number of textual features such as Token n-grams, Stylometric features, bag-of-words and TF-IDF have been extracted. Experimentation has been performed on three datasets viz. Spooky Author Identification dataset, Reuter_50_50 dataset and Manual dataset with 3 different train-test split ratios viz. 80-20, 70-30 and 66.67-33.33. Models have been built and tested with supervised learning algorithms such as Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree and Random Forest. The proposed system yields promising results. For the Spooky dataset, the best accuracy score obtained is 84.14% with bag-of-words using Naïve Bayes classifier. The best accuracy score of 86.2% is computed for the Reuter_50_50 dataset with 2100 most frequent words when the classifier used is Support Vector Machine. For the Manual dataset, the best score of 96.67% is obtained using the Naïve Bayes Classification Model with both 5-fold and 10-fold cross validation when both syntactic features and 600 most frequent unigrams are used in combination. [ABSTRACT FROM AUTHOR]

Author Attribution (AA) is a critical stylometry problem that tries to deduce the identity of the authors of electronic texts (e-texts) by only examining the texts. AA is essential for enhancing various application domains, such as recommender systems and forensics. Nevertheless, existing techniques in AA have not been assessed with Emirati social media e-texts. The reason is that no suitable dataset exists for evaluating AA techniques in this context. This paper introduces the Khonji-Iraqi Emirati Tweets Author Identification (AID) dataset with 30 authors (KIT-30), and detailed evaluations. Compound grams, a new definition of grams, are introduced, which allows us to achieve higher classification accuracy. Also, when the number of suspect authors increases, the classification accuracy degradation is not as severe as previously reported, when using suitable data representation. Furthermore, in order to work towards addressing the lack of conveniently-available implementations of stylometry methods, we have developed an extensive e-text feature extraction library, namely Fextractor, with a highly intuitive API. The library generalizes all existing $n$ -gram-based feature extraction methods under the at least $l$ -frequent, $\texttt {dir}$ -directed, $k$ -skipped $n$ -grams, and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as Part of Speech (POS) tags, as well as lower-level ones, such as the distribution of function words and word shapes.

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.

Identifying the authors of a given set of text is a well addressed and complicated task. It requires thorough knowledge of different authors' writing styles and discriminating them. As the main contribution of this paper, we propose to perform this task using machine learning and deep learning methods, state-of-the-art algorithms, and methods used in numerous complex Natural Language Processing (NLP) problems. We used a text corpus of daily newspaper columns written by thirty authors to perform our experiments. The experimental results proved that document embeddings trained via neural network architecture achieve cutting edge accuracy in learning writing styles and identifying authors of given writings even though the dataset has a considerably unbalanced distribution. We represent our experimental results and outsource our codes for interested readers and natural language processing (NLP) enthusiasts as a GitHub repository. They can reproduce and confirm the results and modify them according to their own needs. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Faculty of Letters / Edebiyat Fakultesi Dergisi is the property of Hacettepe University Faculty of Letters and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Textual deception constitutes a major problem for online security. Many studies have argued that deceptiveness leaves traces in writing style, which could be detected using text classification techniques. By conducting an extensive literature review of existing empirical work, we demonstrate that while certain linguistic features have been indicative of deception in certain corpora, they fail to generalize across divergent semantic domains. We suggest that deceptiveness as such leaves no content-invariant stylistic trace, and textual similarity measures provide a superior means of classifying texts as potentially deceptive. Additionally, we discuss forms of deception beyond semantic content, focusing on hiding author identity by writing style obfuscation. Surveying the literature on both author identification and obfuscation techniques, we conclude that current style transformation methods fail to achieve reliable obfuscation while simultaneously ensuring semantic faithfulness to the original text. We propose that future work in style transformation should pay particular attention to disallowing semantically drastic changes. [ABSTRACT FROM AUTHOR]

Author identification always plays an important role in the public security and literary inspection work. Texts feature extraction is cumbersome and not universal. To solve this problem, this paper proposed the CABLSTM Chinese microblog author identification model without expert feature modeling, and tested the accuracy of the model in the open microblog corpus. This model maximized the extraction of short text features, fused the attention mechanism in the CNN and removed the pooling layer, and obtained context-related information through the bidirectional LSTM. The identity recognition result was output through the softmax layer. Experimental results show that the model has a certain improvement in accuracy, recall rate, and F-measure in comparison with traditional machine learning algorithms and TextCNN and LSTM algorithms in the identification task of Chinese microblog authors. [ABSTRACT FROM AUTHOR]

In this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment -final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes. [ABSTRACT FROM AUTHOR]

The study aims to trace the development of Indian research data repositories (RDRs) and explore their content with the view of identifying prospects and possibilities. Further, it analyses the distribution of data repositories on the basis of content coverage, types of content, author identification system followed, software and the application programming interface used, subject wise number of repositories etc. The study is based on data repositories listed on the registry of data repositories accessible at http://www.re3data.org. The dataset was exported in Microsoft Excel format for analysis. A simple percentage method was followed in data analyses and results are presented through Tables and Figures. The study found a total of 2829 data repositories in existence worldwide. Further, it was seen that 1526 (53.9%) are open and 924 (32.4%) are restricted data repositories. Also, there are embargoed data repositories numbering 225 (8.0%) and closed ones numbering 154 (5.4%). There are 2829 RDRs covering 72 countries in the world. The study found that out of total 45 Indian RDRs, only 30 (67%) are open, followed by restricted 12 (27%) and 3 (6%) that are closed. Majority of Indian RDRs (20) were developed in the year 2014. The study found that the majority of Indian RDRs (17) are'disciplinary'. Further, the study also revealed that statistical data formats are available in a maximum of 31 (68.9%) Indian RDRs. It was also seen that the majority of Indian RDRs (28) has datasets relating to 'Life Sciences'. It was identified that only 20% of data repositories have been using metadata standards in metadata; the remaining 80% do not use any standards in metadata entry. This study covered only the research data repositories in India registered on the registry of data repositories. RDRs not listed in the registry of data repositories are left out. [ABSTRACT FROM AUTHOR]

Authorship attribution is a task to identify the writer of unknown text and categorize it to known writer. Writing style of each author is distinct and can be used for the discrimination. There are different parameters responsible for rectifying such changes. When the writing samples collected for an author when it belongs to small period, it can participate efficiently for identification of unknown sample. In this paper author identification problem considered where writing sample is not available on the same time period. Such evidences collected over long period of time. And character n-gram, word n-gram and pos n-gram features used to build the model. As they are contributing towards style of writer in terms of content as well as statistic characteristic of writing style. We applied support vector machine algorithm for classification. Effective results and outcome came out from the experiments. While discriminating among multiple authors, corpus selection and construction were the most tedious task which was implemented effectively. It is observed that accuracy varied on feature type. Word and character n-gram have shown good accuracy than PoS n-gram. [ABSTRACT FROM AUTHOR]

Identifying authors by their style of writing is a very challenging task. This problem has several applications, one of which is to identify fake online reviews written by spam accounts. The existence of such fake reviews degrades the credibility of the whole review collection, hence these fake reviews should be identified and removed. This process, however, needs to be automated since it is impossible to perform it manually in large review collections. Current authorship identification approaches identify authors based on large-scale texts such as documents. For this reason, these methods do not scale well to short texts such as online reviews that have limited features to learn from. This paper introduces a new method of author identification in short texts using combinations of machine learning algorithms and natural language processing techniques. The experiments we conducted on Yelp reviews gave promising results. [ABSTRACT FROM AUTHOR]

Die Universitätsbibliothek Dortmund (UB) steht vor der Herausforderung, künftig Aussagen zum Publikationsverhalten ihrer Angehörigen treffen zu müssen. Dabei liegt die eine Schwierigkeit darin, Personen eindeutig Publikationen zuzuordnen. Zweitens müssen die Publikationsleistungen möglichst sowohl für einzelne Autoren als auch für alle Disziplinen umfassend nachgewiesen werden. Um beiden Herausforderungen zu begegnen, wurde ein Metadatenmanagementsystem entwickelt. Bereits bei der Konzeption dieses Systems wurde eine ORCID-Integration angestrebt, denn die UB sieht für beide genannten Herausforderungen die ORCID iD als Teil einer möglichen Lösung an. Die Ziele und die technische Umsetzung der Integration werden anhand der fünf Integrationsstufen, die ORCID bietet, vorgestellt. TU Dortmund's university library faces two main challenges when attempting to make statements about the publication behavior of its researchers: first, associating authors to their publications unambiguously and second, obtaining an overview of publications of an individual author as well as of all disciplines. To meet both challenges a metadata management system was developed. This system was designed to be compatible with ORCID, as TU Dortmund's university library believes that the ORCID iD will be part of a potential solution to both of these challenges. The objectives and technical implementation of the integration are presented using the five levels of integration offered by ORCID. [ABSTRACT FROM AUTHOR]

The aim of this article is to verify whether and to what extent the imitation of another person’s writing style is possible. For this purpose, two literary texts ‒ Saturnin by Zdeněk Jirotka (1943) and Saturnin se vrací by Miroslav Macek (2017) ‒ have been analyzed. The text comparison makes use of some identification methods from forensic linguistics that are focused on language features that are less striking, but significant from the identification perspective. [ABSTRACT FROM AUTHOR]