StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors

This work aims to provide an overview on the open-source multilanguage tool called StyloMetrix. It offers stylometric text representations that cover various aspects of grammar, syntax and lexicon. StyloMetrix covers four languages: Polish as the primary language, English, Ukrainian and Russian. The normalized output of each feature can become a fruitful course for machine learning models and a valuable addition to the embeddings layer for any deep learning algorithm. We strive to provide a concise, but exhaustive overview on the application of the StyloMetrix vectors as well as explain the sets of the developed linguistic features. The experiments have shown promising results in supervised content classification with simple algorithms as Random Forest Classifier, Voting Classifier, Logistic Regression and others. The deep learning assessments have unveiled the usefulness of the StyloMetrix vectors at enhancing an embedding layer extracted from Transformer architectures. The StyloMetrix has proven itself to be a formidable source for the machine learning and deep learning algorithms to execute different classification tasks.Comment: 26 pages, 6 figures, pre-print for the conferenc

Security and Authenticity of AI-generated code

The intersection of security and plagiarism in the context of AI-generated code is a critical theme through- out this study. While our research primarily focuses on evaluating the security aspects of AI-generated code, it is imperative to recognize the interconnectedness of security and plagiarism concerns. On the one hand, we do an extensive analysis of the security flaws that might be present in AI-generated code, with a focus on code produced by ChatGPT and Bard. This analysis emphasizes the dangers that might occur if such code is incorporated into software programs, especially if it has security weaknesses. This directly affects developers, advising them to use caution when thinking about integrating AI-generated code to protect the security of their applications. On the other hand, our research also covers code plagiarism. In the context of AI-generated code, plagiarism, which is defined as the reuse of code without proper attribution or in violation of license and copyright restrictions, becomes a significant concern. As open-source software and AI language models proliferate, the risk of plagiarism in AI-generated code increases. Our research combines code attribution techniques to identify the authors of AI-generated insecure code and identify where the code originated. Our research emphasizes the multidimensional nature of AI-generated code and its wide-ranging repercussions by addressing both security and plagiarism issues at the same time. This complete approach adds to a more profound understanding of the problems and ethical implications associated with the use of AI in code generation, embracing both security and authorship-related concerns

Arabic Text Mining

The rapid growth of the internet has increased the number of online texts. This led to the rapid growth of the number of online texts in the Arabic language. The enormous amount of text must be organized into classes to make the analysis process and text retrieval easier. Text classification is, therefore, a key component of text mining. There are numerous systems and approaches for categorizing literature in English, European (French, German, Spanish), and Asian (Chinese, Japanese). In contrast, there are relatively few studies on categorizing Arabic literature due to the difficulty of the Arabic language. In this work, a brief explanation of key ideas relevant to Arabic text mining are introduced then a new classification system for the Arabic language is presented using light stemming and Classifier Na\"ive Bayesian (CNB). Texts from two classes: politics and sports, are included in our corpus. Some texts are added to the system, and the system correctly classified them, demonstrating the effectiveness of the system

StyleCounsel: Seeing the (Random) Forest for the Trees in Adversarial Code Stylometry

Authorship attribution has piqued the interest of scholars for centuries, but had historically remained a matter of subjective opinion, based upon examination of handwriting and the physical document. Midway through the 20th Century, a technique known as stylometry was developed, in which the content of a document is analyzed to extract the author's grammar use, preferred vocabulary, and other elements of compositional style. In parallel to this, programmers, and particularly those involved in education, were writing and testing systems designed to automate the analysis of good coding style and best practice, in order to assist with grading assignments. In the aftermath of the Morris Worm incident in 1988, researchers began to consider whether this automated analysis of program style could be combined with stylometry techniques and applied to source code, to identify the author of a program. The results of recent experiments have suggested this code stylometry can successfully identify the author of short programs from among hundreds of candidates with up to 98\% precision. This potential ability to discern the programmer of a sample of code from a large group of possible authors could have concerning consequences for the open-source community at large, particularly those contributors that may wish to remain anonymous. Recent international events have suggested the developers of certain anti-censorship and anti-surveillance tools are being targeted by their governments and forced to delete their repositories or face prosecution. In light of this threat to the freedom and privacy of individual programmers around the world, and due to a dearth of published research into practical code stylometry at scale and its feasibility, we carried out a number of investigations looking into the difficulties of applying this technique in the real world, and how one might effect a robust defence against it. To this end, we devised a system to aid programmers in obfuscating their inherent style and imitating another, overt, author's style in order to protect their anonymity from this forensic technique. Our system utilizes the implicit rules encoded in the decision points of a random forest ensemble in order to derive a set of recommendations to present to the user detailing how to achieve this obfuscation and mimicry attack. In order to best test this system, and simultaneously assess the difficulties of performing practical stylometry at scale, we also gathered a large corpus of real open-source software and devised our own feature set including both novel attributes and those inspired or borrowed from other sources. Our results indicate that attempting a mass analysis of publicly available source code is fraught with difficulties in ensuring the integrity of the data. Furthermore, we found ours and most other published feature sets do not sufficiently capture an author's style independently of the content to be very effective at scale, although its accuracy is significantly greater than a random guess. Evaluations of our tool indicate it can successfully extract a set of changes that would result in a misclassification as another user if implemented. More importantly, this extraction was independent of the specifics of the feature set, and therefore would still work even with a more accurate model of style. We ran a limited user study to assess the usability of the tool, and found overall it was beneficial to our participants, and could be even more beneficial if the valuable feedback we received were implemented in future work

Using data mining to repurpose German language corpora. An evaluation of data-driven analysis methods for corpus linguistics

A growing number of studies report interesting insights gained from existing data resources. Among those, there are analyses on textual data, giving reason to consider such methods for linguistics as well. However, the field of corpus linguistics usually works with purposefully collected, representative language samples that aim to answer only a limited set of research questions. This thesis aims to shed some light on the potentials of data-driven analysis based on machine learning and predictive modelling for corpus linguistic studies, investigating the possibility to repurpose existing German language corpora for linguistic inquiry by using methodologies developed for data science and computational linguistics. The study focuses on predictive modelling and machine-learning-based data mining and gives a detailed overview and evaluation of currently popular strategies and methods for analysing corpora with computational methods. After the thesis introduces strategies and methods that have already been used on language data, discusses how they can assist corpus linguistic analysis and refers to available toolkits and software as well as to state-of-the-art research and further references, the introduced methodological toolset is applied in two differently shaped corpus studies that utilize readily available corpora for German. The first study explores linguistic correlates of holistic text quality ratings on student essays, while the second deals with age-related language features in computer-mediated communication and interprets age prediction models to answer a set of research questions that are based on previous research in the field. While both studies give linguistic insights that integrate into the current understanding of the investigated phenomena in German language, they systematically test the methodological toolset introduced beforehand, allowing a detailed discussion of added values and remaining challenges of machine-learning-based data mining methods in corpus at the end of the thesis

Advanced Machine Learning Techniques and Meta-Heuristic Optimization for the Detection of Masquerading Attacks in Social Networks

According to the report published by the online protection firm Iovation in 2012, cyber fraud ranged from 1 percent of the Internet transactions in North America Africa to a 7 percent in Africa, most of them involving credit card fraud, identity theft, and account takeover or h¼acking attempts. This kind of crime is still growing due to the advantages offered by a non face-to-face channel where a increasing number of unsuspecting victims divulges sensitive information. Interpol classifies these illegal activities into 3 types: • Attacks against computer hardware and software. • Financial crimes and corruption. • Abuse, in the form of grooming or “sexploitation”. Most research efforts have been focused on the target of the crime developing different strategies depending on the casuistic. Thus, for the well-known phising, stored blacklist or crime signals through the text are employed eventually designing adhoc detectors hardly conveyed to other scenarios even if the background is widely shared. Identity theft or masquerading can be described as a criminal activity oriented towards the misuse of those stolen credentials to obtain goods or services by deception. On March 4, 2005, a million of personal and sensitive information such as credit card and social security numbers was collected by White Hat hackers at Seattle University who just surfed the Web for less than 60 minutes by means of the Google search engine. As a consequence they proved the vulnerability and lack of protection with a mere group of sophisticated search terms typed in the engine whose large data warehouse still allowed showing company or government websites data temporarily cached. As aforementioned, platforms to connect distant people in which the interaction is undirected pose a forcible entry for unauthorized thirds who impersonate the licit user in a attempt to go unnoticed with some malicious, not necessarily economic, interests. In fact, the last point in the list above regarding abuses has become a major and a terrible risk along with the bullying being both by means of threats, harassment or even self-incrimination likely to drive someone to suicide, depression or helplessness. California Penal Code Section 528.5 states: “Notwithstanding any other provision of law, any person who knowingly and without consent credibly impersonates another actual person through or on an Internet Web site or by other electronic means for purposes of harming, intimidating, threatening, or defrauding another person is guilty of a public offense punishable pursuant to subdivision [...]”. IV Therefore, impersonation consists of any criminal activity in which someone assumes a false identity and acts as his or her assumed character with intent to get a pecuniary benefit or cause some harm. User profiling, in turn, is the process of harvesting user information in order to construct a rich template with all the advantageous attributes in the field at hand and with specific purposes. User profiling is often employed as a mechanism for recommendation of items or useful information which has not yet considered by the client. Nevertheless, deriving user tendency or preferences can be also exploited to define the inherent behavior and address the problem of impersonation by detecting outliers or strange deviations prone to entail a potential attack. This dissertation is meant to elaborate on impersonation attacks from a profiling perspective, eventually developing a 2-stage environment which consequently embraces 2 levels of privacy intrusion, thus providing the following contributions: • The inference of behavioral patterns from the connection time traces aiming at avoiding the usurpation of more confidential information. When compared to previous approaches, this procedure abstains from impinging on the user privacy by taking over the messages content, since it only relies on time statistics of the user sessions rather than on their content. • The application and subsequent discussion of two selected algorithms for the previous point resolution: – A commonly employed supervised algorithm executed as a binary classifier which thereafter has forced us to figure out a method to deal with the absence of labeled instances representing an identity theft. – And a meta-heuristic algorithm in the search for the most convenient parameters to array the instances within a high dimensional space into properly delimited clusters so as to finally apply an unsupervised clustering algorithm. • The analysis of message content encroaching on more private information but easing the user identification by mining discriminative features by Natural Language Processing (NLP) techniques. As a consequence, the development of a new feature extraction algorithm based on linguistic theories motivated by the massive quantity of features often gathered when it comes to texts. In summary, this dissertation means to go beyond typical, ad-hoc approaches adopted by previous identity theft and authorship attribution research. Specifically it proposes tailored solutions to this particular and extensively studied paradigm with the aim at introducing a generic approach from a profiling view, not tightly bound to a unique application field. In addition technical contributions have been made in the course of the solution formulation intending to optimize familiar methods for a better versatility towards the problem at hand. In summary: this Thesis establishes an encouraging research basis towards unveiling subtle impersonation attacks in Social Networks by means of intelligent learning techniques

Computational Stylistics in Poetry, Prose, and Drama

The contributions in this edited volume approach poetry, narrative, and drama from the perspective of Computational Stylistics. They exemplify methods of computational textual analysis and explore the possibility of computational generation of literary texts. The volume presents a range of computational and Natural Language Processing applications to literary studies, such as motif detection, network analysis, machine learning, and deep learning

On the Detection of False Information: From Rumors to Fake News

Tesis por compendio[ES] En tiempos recientes, el desarrollo de las redes sociales y de las agencias de noticias han traído nuevos retos y amenazas a la web. Estas amenazas han llamado la atención de la comunidad investigadora en Procesamiento del Lenguaje Natural (PLN) ya que están contaminando las plataformas de redes sociales. Un ejemplo de amenaza serían las noticias falsas, en las que los usuarios difunden y comparten información falsa, inexacta o engañosa. La información falsa no se limita a la información verificable, sino que también incluye información que se utiliza con fines nocivos. Además, uno de los desafíos a los que se enfrentan los investigadores es la gran cantidad de usuarios en las plataformas de redes sociales, donde detectar a los difusores de información falsa no es tarea fácil. Los trabajos previos que se han propuesto para limitar o estudiar el tema de la detección de información falsa se han centrado en comprender el lenguaje de la información falsa desde una perspectiva lingüística. En el caso de información verificable, estos enfoques se han propuesto en un entorno monolingüe. Además, apenas se ha investigado la detección de las fuentes o los difusores de información falsa en las redes sociales. En esta tesis estudiamos la información falsa desde varias perspectivas. En primer lugar, dado que los trabajos anteriores se centraron en el estudio de la información falsa en un entorno monolingüe, en esta tesis estudiamos la información falsa en un entorno multilingüe. Proponemos diferentes enfoques multilingües y los comparamos con un conjunto de baselines monolingües. Además, proporcionamos estudios sistemáticos para los resultados de la evaluación de nuestros enfoques para una mejor comprensión. En segundo lugar, hemos notado que el papel de la información afectiva no se ha investigado en profundidad. Por lo tanto, la segunda parte de nuestro trabajo de investigación estudia el papel de la información afectiva en la información falsa y muestra cómo los autores de contenido falso la emplean para manipular al lector. Aquí, investigamos varios tipos de información falsa para comprender la correlación entre la información afectiva y cada tipo (Propaganda, Trucos / Engaños, Clickbait y Sátira). Por último, aunque no menos importante, en un intento de limitar su propagación, también abordamos el problema de los difusores de información falsa en las redes sociales. En esta dirección de la investigación, nos enfocamos en explotar varias características basadas en texto extraídas de los mensajes de perfiles en línea de tales difusores. Estudiamos diferentes conjuntos de características que pueden tener el potencial de ayudar a discriminar entre difusores de información falsa y verificadores de hechos.[CA] En temps recents, el desenvolupament de les xarxes socials i de les agències de notícies han portat nous reptes i amenaces a la web. Aquestes amenaces han cridat l'atenció de la comunitat investigadora en Processament de Llenguatge Natural (PLN) ja que estan contaminant les plataformes de xarxes socials. Un exemple d'amenaça serien les notícies falses, en què els usuaris difonen i comparteixen informació falsa, inexacta o enganyosa. La informació falsa no es limita a la informació verificable, sinó que també inclou informació que s'utilitza amb fins nocius. A més, un dels desafiaments als quals s'enfronten els investigadors és la gran quantitat d'usuaris en les plataformes de xarxes socials, on detectar els difusors d'informació falsa no és tasca fàcil. Els treballs previs que s'han proposat per limitar o estudiar el tema de la detecció d'informació falsa s'han centrat en comprendre el llenguatge de la informació falsa des d'una perspectiva lingüística. En el cas d'informació verificable, aquests enfocaments s'han proposat en un entorn monolingüe. A més, gairebé no s'ha investigat la detecció de les fonts o els difusors d'informació falsa a les xarxes socials. En aquesta tesi estudiem la informació falsa des de diverses perspectives. En primer lloc, atès que els treballs anteriors es van centrar en l'estudi de la informació falsa en un entorn monolingüe, en aquesta tesi estudiem la informació falsa en un entorn multilingüe. Proposem diferents enfocaments multilingües i els comparem amb un conjunt de baselines monolingües. A més, proporcionem estudis sistemàtics per als resultats de l'avaluació dels nostres enfocaments per a una millor comprensió. En segon lloc, hem notat que el paper de la informació afectiva no s'ha investigat en profunditat. Per tant, la segona part del nostre treball de recerca estudia el paper de la informació afectiva en la informació falsa i mostra com els autors de contingut fals l'empren per manipular el lector. Aquí, investiguem diversos tipus d'informació falsa per comprendre la correlació entre la informació afectiva i cada tipus (Propaganda, Trucs / Enganys, Clickbait i Sàtira). Finalment, però no menys important, en un intent de limitar la seva propagació, també abordem el problema dels difusors d'informació falsa a les xarxes socials. En aquesta direcció de la investigació, ens enfoquem en explotar diverses característiques basades en text extretes dels missatges de perfils en línia de tals difusors. Estudiem diferents conjunts de característiques que poden tenir el potencial d'ajudar a discriminar entre difusors d'informació falsa i verificadors de fets.[EN] In the recent years, the development of social media and online news agencies has brought several challenges and threats to the Web. These threats have taken the attention of the Natural Language Processing (NLP) research community as they are polluting the online social media platforms. One of the examples of these threats is false information, in which false, inaccurate, or deceptive information is spread and shared by online users. False information is not limited to verifiable information, but it also involves information that is used for harmful purposes. Also, one of the challenges that researchers have to face is the massive number of users in social media platforms, where detecting false information spreaders is not an easy job. Previous work that has been proposed for limiting or studying the issue of detecting false information has focused on understanding the language of false information from a linguistic perspective. In the case of verifiable information, approaches have been proposed in a monolingual setting. Moreover, detecting the sources or the spreaders of false information in social media has not been investigated much. In this thesis we study false information from several aspects. First, since previous work focused on studying false information in a monolingual setting, in this thesis we study false information in a cross-lingual one. We propose different cross-lingual approaches and we compare them to a set of monolingual baselines. Also, we provide systematic studies for the evaluation results of our approaches for better understanding. Second, we noticed that the role of affective information was not investigated in depth. Therefore, the second part of our research work studies the role of the affective information in false information and shows how the authors of false content use it to manipulate the reader. Here, we investigate several types of false information to understand the correlation between affective information and each type (Propaganda, Hoax, Clickbait, Rumor, and Satire). Last but not least, in an attempt to limit its spread, we also address the problem of detecting false information spreaders in social media. In this research direction, we focus on exploiting several text-based features extracted from the online profile messages of those spreaders. We study different feature sets that can have the potential to help to identify false information spreaders from fact checkers.Ghanem, BHH. (2020). On the Detection of False Information: From Rumors to Fake News [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158570TESISCompendi

Computational Stylistics in Poetry, Prose, and Drama

The contributions in this edited volume approach poetry, narrative, and drama from the perspective of Computational Stylistics. They exemplify methods of computational textual analysis and explore the possibility of computational generation of literary texts. The volume presents a range of computational and Natural Language Processing applications to literary studies, such as motif detection, network analysis, machine learning, and deep learning