The goal of the paper is to present a Simplified Offensive Language (SOL) Taxonomy, its application and testing in the Second Annotation Campaign conducted between March-May 2023 on four languages: English, Czech, Lithuanian, and Polish to be verified and located in LLOD. Making reference to the previous Offensive Language taxonomic models proposed mostly by the same COST Action Nexus Linguarum WG 4.1.1 team, the number and variety of the categories underwent the definitional revision, and the present (...) typology was tested in the annotation on the publicly available offensive language datasets of each of the four languages. The results of the annotation are presented and as they are contained within the accepted statistical values on the inter-annotator agreement in the SOL categories and their aspects, we propose this taxonomy as a core ontology which represents the encoding of the supported offensive languages and justify its use on new data in terms of a more universal Linguistic Linked Open Data (LLOD) schema. (shrink)

This paper introduces a streamlined taxonomy for categorizing offensive language in Hebrew, addressing a gap in the literature that has, until now, largely focused on Indo-European languages. Our taxonomy divides offensive language into seven levels (six explicit and one implicit level). We based our work on the simplified offensive language (SOL) taxonomy introduced in (Lewandowska-Tomaszczyk et al. 2021a) hoping that our adjustment of SOL to the Hebrew language will be capable of reflecting the (...) unique linguistic and cultural nuances of Hebrew. The study involves both linguistic and cultural analysis beyond Natural Language Processing (NLP). We employed manual linguistic analysis to understand the nuances of offensive language in Hebrew. An accompanying dataset, gathered on Twitter and manually curated by human annotators, is described in detail. This dataset was constructed to both validate the taxonomy and serve as a foundation for future research on offensive language detection and analysis in Hebrew. Preliminary analysis of the dataset reveals intriguing patterns and distributions, underscoring the complexity and specificity of offensive expressions in the Hebrew language. The aim of our work is to capture the complexity and specificity of offensive expressions in Hebrew beyond what automated NLP methods alone can provide. Our findings highlight the significance of considering linguistic and cultural variations when researching and correcting abusive language online. We believe that our streamlined taxonomy and associated dataset will be crucial in improving research in Hebrew language sociocultural studies, natural language processing, and offensive language detection. Our study also makes a substantial contribution to the study of low-resource languages and can be used as a model for future research on other languages. (shrink)

This paper presents a streamlined taxonomy for categorizing offensive language in Arabic, specifically Modern Standard Arabic (MSA) and the Levantine dialect. Addressing a gap in the existing literature, which has mainly focused on Indo-European languages, our taxonomy divides offensive language into seven levels (six explicit and one implicit). We adapted our framework from the simplified offensive language (SOL) taxonomy by (Lewandowska-Tomaszczyk, Barbara, Slavko Žitnik, Anna Bączkowska, Chaya Liebeskind, Jelena Mitrovic & Giedre Valunaite Oleškeviciente. 2021a. (...) Lod-connected offensive language ontology and tagset enrichment. In Shubert R. Carvalho & Renato R. Souza (eds.), Proceedings of the workshops and tutorials held at ldk 2021 co-located with the 3rd language, data and knowledge conference, Vol. 3064, 135–150. CEUR Workshop Proceedings), customizing it to reflect the unique linguistic and cultural nuances of Arabic. To validate this taxonomy, we created a new dataset from various social media platforms, primarily focusing on Twitter. This dataset was manually curated by human annotators and is described in detail within the paper, serving as both a validation tool for our taxonomy and a foundation for future research on offensive language detection in Arabic. Initial analysis of the dataset reveals complex patterns of offensive expressions in MSA and Levantine Arabic, underscoring the need to account for linguistic and cultural variations in studying online abuse. Our taxonomy and dataset are vital for advancing research in Arabic sociocultural studies, natural language processing, and linguistic analysis, and contribute to the study of low-resource languages. (shrink)

This study intends to contribute to the delimitation of selected offensive language categories based on an analysis of a corpus of contributions to discussion forums in Czech online national newspapers and news platforms called Czech Corpus of Offensive Language (CCOL). It endeavours to study three problematic areas (1) delimitation between the speech acts performed, (ii) lexical realisation of specific properties of the target and (iii) identification and categorisation of implicit offence (e.g. figurative semantic shifts) by exploring (...) contextual cues for the speech act identification, the keywords indicating the properties of the target and the types of semantic shifts in implicit expressions of offence. The findings indicate that annotation systems that do not use context information for the detection of offensive language may face problems with adequate interpretation of the language means under investigation. (shrink)

The aim of this paper is to present a proposal of implicitness typology. The theoretical model we propose is compliant with neo-Gricean pragmatics and is explicitly designed to cover instances of offensive language on social media. The implicitness framework we propound has been empirically verified by means of a corpus-assisted analysis and computational method of word embeddings (Word2Vec and FastText), which, in principle, have supported the schema explicated here. This taxonomy is potentially applicable to the ontology of offensiveness (...) and, thus, to NLP-based research; in particular, it can be useful for automatic detection of implicit offensive language on social media. (shrink)

The automatic detection of implicitly offensive language is a challenge for NLP, as such language is subtle, contextual, and plausibly deniable, but it is becoming increasingly important with the wider use of large language models to generate human-quality texts. This study argues that current difficulties in detecting implicit offence are exacerbated by multiple factors: (a) inadequate definitions of implicit and explicit offense; (b) an insufficient typology of implicit offence; and (c) a dearth of detailed analysis (...) of implicitly offensive linguistic data. In this study, based on a qualitative analysis of an implicitly offensive dataset, a new typology of implicitly offensive language is proposed along with a detailed, example-led account of the new typology, an operational definition of implicitly offensive language, and a thorough analysis of the role of figurative language and humour in each type. Our analyses identify three main issues with previous datasets and typologies used in NLP approaches: (a) conflating content and form in the annotation; (b) treating figurativeness, particularly metaphor, as the main device of implicitness, while ignoring its equally important role in the explicit offence; and (c) an over-focus on form-specific datasets (e.g. focusing only on offensive comparisons), which fails to reflect the full complexity of offensive language use. (shrink)

Harmful and dangerous language is frequent in social media, in particular in spaces which are considered anonymous and/or allow free participation. In this paper, we analyse the language in a Telegram channel populated by followers of Donald Trump, in order to identify the ways in which harmful language is used to create a specific narrative in a group of mostly like-minded discussants. Our research has several aims. First, we create an extended taxonomy of potentially harmful language (...) that includes not only hate speech and direct insults, but also more indirect ways of poisoning online discourse, such as divisive speech and the glorification of violence. We apply this taxonomy to a large portion of the corpus. Our data gives empirical evidence for harmful speech such as in/out-group divisive language and the use of codes within certain communities which have not often been investigated before. Second, we compare our manual annotations to several automatic methods of classifying hate speech and offensive language, namely list based and machine learning based approaches. We find that the Telegram data set still poses particular challenges for these automatic methods. Finally, we argue for the value of studying such naturally occurring, coherent data sets for research on online harm and how to address it in linguistics and philosophy. (shrink)

The use of slang, abusive, and offensive language has become common practice on social media. Even though social media companies have censorship polices for slang, abusive, vulgar, and offensive language, due to limited resources and research in the automatic detection of abusive language mechanisms other than English, this condemnable act is still practiced. This study proposes USAD, a lexicon-based intelligent framework to detect abusive and slang words in Perso-Arabic-scripted Urdu Tweets. Furthermore, due to the (...) nonavailability of the standard dataset, we also design and annotate a dataset of abusive, offensive, and slang word Perso-Arabic-scripted Urdu as our second significant contribution for future research. The results show that our proposed USAD model can identify 72.6% correctly as abusive or nonabusive Tweet. Additionally, we have also identified some key factors that can help the researchers improve their abusive language detection models. (shrink)

The aim of the current research is to investigate the feasibility of identifying offensive language in Lithuanian by utilising the Simplified Offensive Language Taxonomy (SOLT). The key principle behind this taxonomy is its ability to complement existing offensive language ontologies and tagset systems, with the ultimate goal of integrating it into publicly accessible Linguistic Linked Open Data (LLOD) resources. The dataset used in the current study is a publicly available corpus of user-generated comments collected (...) from a Lithuanian portal (Amilevičius et al. 2016). The study identified that offensive language predominantly focuses on collective derogatory language rather than individuals. The most common category of offensive language is related to physical and mental disabilities, followed by ideological offenses, xenophobic and sexist remarks, and less frequent categories like ageism, classism, homophobia, and religious discrimination. These results highlight the diverse range of offensive language online and underscore the need to combat discrimination and promote respectful discourse, particularly concerning marginalised groups. (shrink)

The current study represents an integrated model of explicit and implicit offensive language taxonomy. First, it focuses on a definitional revision and enrichment of the explicit offensive language taxonomy by reviewing the collection of available corpora and comparing tagging schemas applied there. The study relies mainly on the categories originally proposed by Zampieri et al. (2019) in terms of offensive language categorization schemata. After the explanation of semantic differences between particular concepts used in the (...) tagging systems and the analysis of theoretical frameworks, a finite set of classes is presented, which cover aspects of offensive language representation along with linguistically sound explanations (Lewandowska-Tomaszczyk et al. 2021). In the analytic procedure, offensive from non-offensive discourse is first distinguished, with the question of offence Target and the following categorization levels and sublevels. Based on the relevant data generated from Sketch Engine (https://www.sketchengine.eu/ententen-english-corpus/), we propose the concept of offensive language as a superordinate category in our system with a number of hierarchically arranged 17 subcategories. The categories are taxonomically structured into 4 levels and verified with the use of neural-based (lexical) embeddings. Together with a taxonomy of implicit offensive language and its subcategorization levels which has received little scholarly attention until now, the categorization is exemplified in samples of offensive discourses in selected English social media materials, i.e., publicly available 25 web-based hate speech datasets (consult Appendix 1 for a complete list). The offensive category levels (types of offence, targets, etc.) and aspects (offensive language property clusters) as well as the categories of explicitness and implicitness are discussed in the study and the computationally verified integrated explicit and implicit offensive language taxonomy proposed in the study. (shrink)

We provide a new text corpus from the social medium Telegram, which is rich in indirect forms of divisive speech. We scraped all messages from one channel of supporters of Donald Trump, covering a large part of his presidency from late 2016 until January 2021. The discussion among the group members over this long time period includes the spread of disinformation, disparaging of out-group members, and other forms of offensive speech. To encourage research into such practices of poisoning public (...) political discourse, we added automatic annotations of offensive language to all messages. We further added manual annotations of harmful language to a portion of the posts in order to enable the analysis of more implicit forms of online harm. (shrink)

The aim of this paper is to shed some light on the linguistic concept of implicit offensiveness. On the one hand, implicitness will be juxtaposed with indirectness as the two concepts are not conceived of here as synonymous. On the other hand, a typology of offensiveness (vs offensive language and vs offendedness) will be proposed, as well as the overarching term ‘covert meaning’ that will span figurative implicitness and non-figurative implicitness. The gradability of various forms of covert meaning (...) and its overlap with overt meaning (subsuming explicit literal meaning and implicit literal meaning) will also be discussed. In the analysis, two sample implicit concepts will be examined (irony vs sarcasm) based on corpus data (of general English and dedicated offensiveness corpus) and using non-contextual embeddings. Theory-wise, the paper demonstrates that implicitness is a complex term which is fuzzy and gradable; methodology-wise, it shows how computational tools can be used to attest theoretical assumptions related to offensive covert terms. (shrink)

One can attack a philosophical claim by identifying a misuse of the language used to state it. I distinguish between two varieties of this strategy: one belonging to Norman Malcolm and the other to Ludwig Wittgenstein. The former is flawed and easily dismissible as misled linguistic conservatism. It muddies the name of ordinary language philosophy. I argue that the latter avoids this flaw. To make perspicuous the kind of criticism of philosophical claims that the second variety makes available, (...) I draw a comparison between Wittgenstein’s recommendation that philosophers study ordinary language and Alfred Schütz’s recommendation that social scientists study the methods of the agents they study. Both do so in an attempt to sensitise philosophers and social scientists respectively to particular artefacts of method which can be easily mistaken for features of that which is studied. (shrink)

Currently, the Internet information and communication network has become an integral part of human life. People use social networks such as Twitter, VKontakte, Facebook, etc., to establish global contacts, exchange opinions, gain knowledge, etc. The active participation of not only individual users, but also information organizations in the entire world space makes it necessary to develop measures that correspond to modern trends in the development of information and communication technologies to ensure national security, in particular, the organization of events related (...) to countering the strengthening of ideas of extremism and terrorism. Countering the spread of aggressive information on the global network is an urgent problem of society and government agencies, this task is solved by filtering unwanted Internet resources. However, terrorist and extremist groups rationally use web technologies to perform various functions, including information dissemination, propaganda, fundraising and extremist missions. In such a situation, the Internet poses a threat to national security. In this paper, we investigate the issue of creating semantic analysis models to identify extremist messages in the Kazakh language. For the study, a proprietary text corpus was assembled and models based on bigrams and word input methods were proposed. According to the results of experiments, the proposed model shows the highest indicators for evaluating machine learning methods. (shrink)

This paper examines the concept of “terminological gaps” and strives to identify suitable methods for dealing with them during translation. The analysis begins with an investigation of the contended notion of gaps in terminology based on empirical examples drawn from a German-Italian terminological database specifically designed for translation purposes. Two macro categories of gaps are identified, conceptual and linguistic level gaps, which only partially correspond to previous observations in the literature. The paper uses examples to explore the advantages of ontological (...) representations for detecting conceptual terminological gaps and identifying appropriate translation strategies. However, limitations are also observed and an attempt is made to resolve these using a frame-based approach. A frame-based analysis reveals that while certain designations may appear to refer to convergent conceptual units with matching distinctive features, differences also emerge due to the way the two language systems build designations. Examples from the corpus make it evident that a frame-based approach is helpful for identifying both kinds of terminological gaps, and then resolving them during translation. An important presupposition for this approach is that larger units of analysis need to be addressed rather than just terms themselves. There is confirmation of the existing idea that methods embracing entire segments or paragraphs as units of investigation are preferable during translation, and this is also seen to apply in terminological studies. (shrink)

Both language and genes evolve by transmission over generations with opportunity for differential replication of forms. The understanding that gene frequencies change at random by genetic drift, even in the absence of natural selection, was a seminal advance in evolutionary biology. Stochastic drift must also occur in language as a result of randomness in how linguistic forms are copied between speakers. Here we quantify the strength of selection relative to stochastic drift in language evolution. We use time (...) series derived from large corpora of annotated texts dating from the 12th to 21st centuries to analyse three well-known grammatical changes in English: the regularization of past-tense verbs, the introduction of the periphrastic ’do’, and variation in verbal negation. We reject stochastic drift in favour of selection in some cases but not in others. In particular, we infer selection towards the irregular forms of some past-tense verbs, which is likely driven by changing frequencies of rhyming patterns over time. We show that stochastic drift is stronger for rare words, which may explain why rare forms are more prone to replacement than common ones. This work provides a method for testing selective theories of language change against a null model and reveals an underappreciated role for stochasticity in language evolution. (shrink)

Mueller et al. discuss the role of acoustic cues in detecting language structure more generally. Across languages, there are clear links between acoustic cues and syntactic structure. They show that AGL experiments implementing analogous links demonstrate that prosodic cues, as well as various auditory biases, facilitate the learning of structural rules. Some of these biases, e.g. for auditory grouping, are also present in other species.

Most of the existing natural language processing systems for legal texts are developed for the English language. Nevertheless, there are several application domains where multiple versions of the same documents are provided in different languages, especially inside the European Union. One notable example is given by Terms of Service (ToS). In this paper, we compare different approaches to the task of detecting potential unfair clauses in ToS across multiple languages. In particular, after developing an annotated corpus and a (...) machine learning classifier for English, we consider and compare several strategies to extend the system to other languages: building a novel corpus and training a novel machine learning system for each language, from scratch; projecting annotations across documents in different languages, to avoid the creation of novel corpora; translating training documents while keeping the original annotations; translating queries at prediction time and relying on the English system only. An extended experimental evaluation conducted on a large, original dataset indicates that the time-consuming task of re-building a novel annotated corpus for each language can often be avoided with no significant degradation in terms of performance. (shrink)

Can managers detect honest people’s lies in a handwritten message? In this article, I will briefly discuss graphology and a basic model of interpersonal communication. I will then develop a fundamental theoretical framework of eight principles for detecting lies based on the basic communication model, handwriting analyses, and the following assumptions: For most people, it is easier to tell the truth than to tell lies. This applies to handwritings also. When most honest people lie, they try to hide their stressful (...) emotions in the encoding process. As a consequence, they deviate from their own normal writing and violate their own personal moral standards. Interestingly enough, the art or science of detecting a lie in a handwritten sample is to focus not on what they write, but on how they write it. These 24 exhibits (cases) written in 11 languages—used in different parts of the world—help managers apply this important theoretical framework of interpersonal communication, understand the encoding process, pinpoint these sudden emotional changes, decode handwritten messages, unlock the secrets, reveal the message’s true meanings, and detect people’s lies. (shrink)

In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is (...) lacking in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. (shrink)

The offense principle requires that an unpleasant state of mind or offense be produced wrongfully by another party, but not that it be an offense in the strict sense of ordinary language. The legislative problem of determining when offensive conduct is a public or criminal nuisance could be expressed, with equal accuracy, as a problem about determining the extent of personal privacy or autonomy. The former way of describing the matter lends itself to talk of balancing the independent (...) value or reasonableness of the offending conduct against the seriousness of the offense cause, whereas the latter way lends itself to talk of drawing boundaries between the various private domains of persons, and between private domains of a person and the public world. (shrink)

Freedom of information laws promote transparency by permitting individuals and organizations to obtain government documents. However, exemptions from disclosure are necessary to protect privacy and to permit government officials to deliberate freely. Deliberative language is often the most challenging and burdensome exemption to detect, leading to high processing costs and delays in responding to open-records requests. This paper describes a novel deliberative-language detection model trained on a new annotated training set. The deliberative-language detection model is (...) a component of a decision-support system for open-records requests under the US Freedom of Information Act, the FOIA Assistant, that ingests documents responsive to an open-records requests, suggests passages likely to be subject to deliberative language, privacy, or other exemptions, and assists analysts in rapidly redacting suggested passages. The tool’s interface is based on extensive human-factors and usability studies with analysts and is currently in operational testing by multiple US federal agencies. (shrink)

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done (...) with the support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free. (shrink)

Socialist courts are supposed to apply the law, not make it, and socialist legality denies judicial decisions any precedential status. In 2011, the Chinese Supreme People’s Court designated selected decisions as Guiding Cases to be referred to by all judges when adjudicating similar disputes. One decade on, the paucity of citations to Guiding Cases has been taken as demonstrating the incongruity of case-based adjudication and the socialist legal tradition. Citations are, however, an imperfect measure of influence. Reproduction of language (...) uniquely traceable to Guiding Cases can also be evidence of their impact on judicial decision-making. We employ a local alignment tool to detect unattributed text reuse of Guiding Cases in local court decisions. Our findings suggest that Guiding Cases are more consequential than commonly assumed, thereby complicating prevailing narratives about the antagonism of socialist legality to case law. (shrink)

The prevalence of sensationalized headlines and deceptive narratives in online content has prompted the need for effective clickbait detection methods. This study delves into the nuances of clickbait in Hebrew, scrutinizing diverse features such as linguistic and structural features, and exploring various types of clickbait in Hebrew, a language that has received relatively limited attention in this context. Utilizing a range of machine learning models, this research aims to identify linguistic features that are instrumental in accurately classifying Hebrew (...) headlines as either clickbait or non-clickbait. The findings underscore the critical role of linguistic attributes in enhancing the performance of the classification model. Notably, the employment of a machine learning model resulted in an impressive accuracy of 0.87 in clickbait detection. Moving forward, our research plan encompasses dataset expansion through the best machine learning model assisted labelling, with the objective of optimizing deep learning models for even more robust outcomes. This study not only advances clickbait detection in the realm of Hebrew but also emphasizes the fundamental importance of linguistic features in the accurate classification of clickbait. (shrink)

First language attrition in adulthood offers new insight on neuroplasticity and the role of language experience in shaping neurocognitive responses to language. Attriters are multilinguals for whom advancing L2 proficiency comes at the cost of the L1, as they experience a shift in exposure and dominance. To date, the neurocognitive mechanisms underlying L1 attrition are largely unexplored. Using event-related potentials, we examined L1-Italian grammatical processing in 24 attriters and 30 Italian native-controls. We assessed whether attriters differed from (...) non-attriting native speakers in their online detection and re-analysis/repair of number agreement violations, and whether differences in processing were modulated by L1-proficiency. To test both local and non-local agreement violations, we manipulated agreement between three inflected constituents and examined ERP responses on two of these. Our findings revealed group differences in amplitude, scalp distribution, and duration of LAN/N400 + P600 effects. We discuss these differences as reflecting influence of attriters’ L2-English, as well as shallower online sentence repair processes than in non-attriting native speakers. ERP responses were also predicted by L1-Italian proficiency scores, with smaller N400/P600 amplitudes in lower proficiency individuals. Proficiency only modulated P600 amplitude between 650 and 900 ms, whereas the late P600 depended on group membership and amount of L1 exposure within attriters. Our study is the first to show qualitative and quantitative differences in ERP responses in attriters compared to non-attriting native speakers. Our results also emphasize that proficiency predicts language processing profiles, even in native-speakers, and that the P600 should not be considered a monolithic component. (shrink)

Effective methods for evaluating the reliability of statements issued by witnesses and defendants in hearings would be an extremely valuable support to decision-making in court and other legal settings. In recent years, methods relying on stylometric techniques have proven most successful for this task; but few such methods have been tested with language collected in real-life situations of high-stakes deception, and therefore their usefulness outside lab conditions still has to be properly assessed. In this study we report the results (...) obtained by using stylometric techniques to identify deceptive statements in a corpus of hearings collected in Italian courts. The defendants at these hearings were condemned for calumny or false testimony, so the falsity of (some of) their statements is fairly certain. In our experiments we replicated the methods used in previous studies but never before applied to high-stakes data, and tested new methods. We also considered the effect of a number of variables including in particular the homogeneity of the dataset. Our results suggest that accuracy at deception detection clearly above chance level can be obtained with real-life data as well. (shrink)

Research on delinquency reduction often highlights the importance of identifying and sanctioning antisocial and illegal activities so as to reduce the likelihood of future offending. The rise of digital technology complicates the process of detecting cybercrimes and technology enabled offenses, as individuals can use devices from anywhere to engage in various harmful activities that may appear benign to an observer. Despite the growth of cybercrime research, limited studies have examined the extent to which technology enabled offenses are detected, or the (...) behavioral and attitudinal factors associated with being unobserved or caught for one’s actions. The current study addresses this gap in the literature by estimating a multinomial regression model for self-reported computer hacking behavior and the likelihood of those actions being detected in a large international sample of juveniles (N = 51,059). The findings demonstrate significant differences between youth who hack without detection compared to those who are caught. The implications of this analysis for our understanding of cybercrime and its relationship to traditional delinquency are explored in depth. (shrink)

Can conscious awareness be ascertained from physiological responses alone? We evaluate a novel learning-based procedure permitting detection of conscious awareness without reliance on language comprehension or behavioural responses. The method exploits a situation whereby only consciously detected violations of an expectation alter skin conductance responses . Thirty participants listened to sequences of piano notes that, without their being told, predicted a pleasant fanfare or an aversive noise according to an abstract rule. Stimuli were presented without distraction , or (...) while distracted by a visual task to remove awareness of the rule . A test phase included occasional violations of the rule. Only participants attending the sounds reported awareness of violations and only they showed significantly greater SCR for noise occurring in violation, vs. accordance, with the rule. Our results establish theoretically significant dissociations between conscious and unconscious processing and furnish new opportunities for clinical assessment of residual consciousness in patient populations. (shrink)

This paper investigates offensive animal metaphors in blog comments about the management of donations of money and medical relief during the coronavirus pandemic in China. Rather than understanding the metaphorical usage of language as a cognitive process, we consider its situational usage as a social action and invoke insights from Conversation Analysis. Based on data retrieved from Sina Weibo, we show how discussants use animal metaphors to accomplish varying actions and construct intelligibility between themselves and others about the (...) relationship between actions, moral accountability, and description of social events. We therefore argue that CA with its focus on topicality, action and societal membership can complement the cognitive understanding of the proximity between the source and the target domains. (shrink)

An objection frequently brought against critical or satirical expressions, especially when these target religions, is that they are ‘offensive’. In this article, I indicate why the existence of diverse and conflicting beliefs gives people an incentive to formulate their complaints in the language of offence. But I also cast doubt on whether people, in saying they are offended really mean to present that as the foundation of their complaint and, if they do, whether their complaint should weigh with (...) us. These doubts do not apply to everything we might find offensive; in particular, they do not apply to simple cases of ‘sensory offence’; but they do apply to ‘belief-based offence’. Relying on offence also implies, inequitably, that different faiths should be differently protected depending on their susceptibility to offence; and the faithful themselves should worry about the flimsiness of claims based on ‘bare knowledge’ offence. I propose a principle of respect for beliefs as a differently grounded and more plausible reason for curbing our treatment of others’ beliefs. However, that principle has a limited compass and is hemmed in by the claims of free expression. It is also less suited to dictating the content of law than to influencing our conduct within the law. (shrink)

Nowadays, football goalkeepers play an important role in the team's organization, namely, considering the offensive and defensive processes. The purpose of our investigation focuses on the notational and T-pattern analysis of the offensive and defensive actions of elite young football GKs. The participating GKs presented 8 years of experience in the specific position, were internationally selected for the national team of Portugal, and competed in the national U-17 championship of Portugal. Thirty football matches were observed. The observational sample (...) consisted of defensive actions and offensive actions. Two observational instruments were used to codify the actions: the observation system of defensive technical-tactical actions of GKs and the observation system of offensive technical-tactical actions of GKs. Both instruments underwent a validation process, and inter- and intra-observer reliability was tested. The codification of the actions was performed with the LINCE program, and later the data were exported to Microsoft Excel and THEME 5.0. The notational data were analyzed in SPSS, and T-pattern detection analysis was performed in THEME 5.0. The predominant actions of young observed GKs were fundamentally goal defense and participation in the team's offensive process construction through actions performed with the foot and hand. The analysis of T-patterns allowed to identify T-pattern actions in the defensive actions of goal defense and exit of the goal, as well as related to the defensive set pieces. In the offensive process, the analysis of T-patterns reinforced the participation of the GK in the team's first phase of construction and in the execution of goal kicks and actions that start as a result of the actions of the opponent. The GK's defensive actions are mostly focused on the objective of goal defense and offensive actions with the hands and feet are important for their participation in positional attack construction. Our study contributes to a better knowledge of the GK's actions in the competition and is relevant to be considered by the specific position coaches in the training process organization. (shrink)

Michael Pardo and Dennis Patterson have recently put forward several provocative and stimulating criticisms that strike at the heart of much work that has been done at the crossroads of neuroscience and the law. My goal in this essay is to argue that their criticisms of the nascent but growing field of neurolaw are ultimately based on questionable assumptions concerning the nature of the ever evolving relationship between scientific discovery and ordinary language. For while the marriage between ordinary (...) class='Hi'>language and scientific discovery is admittedly not always a happy one, it is an awkward union that nevertheless seems to work itself out with the passage of time. In the following pages, I will try to show that Pardo and Patterson’s primary argumentative strategy ultimately depends on basic assumptions concerning the fixity of language that we should reject. (shrink)

Lying is the deliberate act of conveying false information to deceive others using language. Examining the linguistic and content features of this act has been a point of interest for forensic linguists due to its potential significance in examining various legal contexts such as criminal investigations, accusations, eyewitness testimonies, allegations, and police interrogations. This article aimed to examine the content features of lying in Jordanian Colloquial Arabic and their social constraints. The study draws on the Reality Monitoring (RM) approach, (...) a forensic tool that has proven its effectiveness in lying detection. To achieve this aim, a corpus of 288 narratives, 144 lying and 144 truthful, was collected through face-to-face interviews with 48 speakers stratified by gender, age, and educational attainment. The findings of this study did not conform to the RM criteria. The study revealed that truthful accounts contain more cognitive details while lying accounts contain more spatial, temporal, affective, and perceptual details. (shrink)

In this article we evaluate claims that language structure adapts to sociolinguistic environment. We present the results of two typological case studies examining the effects of the number of native (=L1) speakers and the proportion of adult second language (=L2) learners on language structure. Data from more than 300 languages suggest that testing the effect of population size and proportion of adult L2 learners on features of verbal and nominal complexity produces conflicting results on different grammatical features. (...) The results show that verbal inflectional synthesis adapts to the sociolinguistic environment but the number of genders does not. The results also suggest that modeling population size together with proportion of L2 improves model fit compared to modeling them independently of one another. We thus argue that surveying population size alone may be insufficient to detect possible adaptation of linguistic structure to the sociolinguistic environment. Rather, other features, such as proportion of L2 speakers, prestige and social network density, should be studied, and if demographic numeric data are used, they should not be used in isolation but rather in competition with other sociolinguistic features. We also suggest that not all types of language structures within a given grammatical domain are equally sensitive to the effect of sociolinguistic variables, and that more exploratory studies are needed before we can arrive at a reliable set of grammatical features that may be potentially most (and least) adaptive to social structures. (shrink)

The Hilbert space formalism is a powerful language to express many cognitive phenomena. Here, relevant concepts from signal detection theory are recast in that language, allowing an empirically testable extension of the quantum probability formalism to psychophysical measures, such as detectability and discriminability.

Sentence boundary detection (SBD) represents an important first step in natural language processing since accurately identifying sentence boundaries significantly impacts downstream applications. Nevertheless, detecting sentence boundaries within legal texts poses a unique and challenging problem due to their distinct structural and linguistic features. Our approach utilizes deep learning models to leverage delimiter and surrounding context information as input, enabling precise detection of sentence boundaries in English legal texts. We evaluate various deep learning models, including domain-specific transformer models (...) like LegalBERT and CaseLawBERT. To assess the efficacy of our deep learning models, we compare them with a state-of-the-art domain-specific statistical conditional random field (CRF) model. After considering model size, F1-score, and inference time, we identify the Convolutional Neural Network Model (CNN) as the top-performing deep learning model. To further enhance performance, we integrate the features of the CNN model into the subsequent CRF model, creating a hybrid architecture that combines the strengths of both models. Our experiments demonstrate that the hybrid model outperforms the baseline model, achieving a 4% improvement in the F1-score. Additional experiments showcase the superiority of the hybrid model over SBD open-source libraries when confronted with an out-of-domain test set. These findings underscore the importance of efficient SBD in legal texts and emphasize the advantages of employing deep learning models and hybrid architectures to achieve optimal performance. (shrink)

Fake news detection involves predicting the likelihood that a particular news article is intentionally deceptive. Arabic FND started to receive more attention in the last decade, and many detection approaches demonstrated some ability to detect fake news on multiple datasets. However, most existing approaches do not consider recent advances in natural language processing, i.e., the use of neural networks and transformers. This paper presents a comprehensive comparative study of neural network and transformer-based language models used for (...) Arabic FND. We examine the use of neural networks and transformer-based language models for Arabic FND and show their performance compared to each other. We also conduct an extensive analysis of the possible reasons for the difference in performance results obtained by different approaches. The results demonstrate that transformer-based models outperform the neural network-based solutions, which led to an increase in the F1 score from 0.83 to 0.95, and it boosted the accuracy by 16% compared to the best in neural network-based solutions. Finally, we highlight the main gaps in Arabic FND research and suggest future research directions. (shrink)

Software vulnerabilities are the root cause for a multitude of security problems in computer systems. Owing to their efficiency and tight control over low-level system resources, the C and C++ programming languages are extensively used for a myriad of purposes, from implementing operating system kernels to user-space applications. However, insufficient or improper memory management frequently leads to invalid memory accesses, eventually resulting in memory corruption vulnerabilities. These vulnerabilities are used as a foothold for elaborated attacks that bypass existing defense methods. (...) In this paper, we summarise the main memory safety violation types (i.e. memory errors), and analyse how they are exploited by attackers and the main mitigation methods proposed in the research community. We further systematise the most relevant techniques with regards to memory corruption identification in current programs. (shrink)

To justify attraction to Dominant Traditional Masculinities (DTM) and lack of attraction to non-aggressive men, some women defend opinions such as “there are no frigid women, only inexperienced men”. Such statements generate a large amount of sexual-affective insecurity in oppressed men and contribute to decoupling desire and ethics in sexual-affective relationships, which, in turn, reinforces a model of attraction to traditional masculinities that use coercion, thus perpetuating gender-based violence. New Alternative Masculinities (NAM) represent a type of masculinity that reacts to (...) reverse such consequences with communicative acts, in which they state that women who support such discourses have never met a NAM man or have never experienced a successful sexual-affective relationship where passion, love, desire, and equality are all included. This article presents data analyzing these communicative acts (exclusory and transformative; language employed and consequences) to ultimately find the key to NAM communication that would contribute to changing attraction patterns. The data was collected using communicative daily life stories of three heterosexual white men and one heterosexual white woman, between the ages of 30 and 40. Findings emphasize the importance of self-confidence manifested by NAM men when communicating about sex and facing these offensive mottos in the presence of other men and women. Findings also demonstrate that supportive egalitarian relationships encourage the emergence of self-confidence in NAM men and that NAM men's self-confident communicative acts foster healthy relationships and obliterate coercive ones. (shrink)

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the (...) classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes. (shrink)

The language specificity of modern media texts and the aspects of studying it in the courses ‘Language and style of modern mass media‘ and ‘Modern mediatext‘ are considered in the article. The language specificity of contemporary media texts is connected, on the one hand, with the subjectivization of the text, enforcement of personality, democratization and with the increase of proportion of a foreign word, intertexuality, intellectualization of the text on the other hand. Subjectivization of the text is (...) connected with the personality enforcement, is determined by the influence of social processes - freedom of speech declaration - and demonstrates itself in the actualization of the author, in a high level of evaluation, emotionality, abundance of metatextual means. Democratization in the modern mass media texts shows itself in the joint functioning of means of different styles: bookish, substandard, jargon. Intertextuality is opposed to the excessive subjectivization of the text and demonstrates itself in the increased usage of foreign words, it is realized with the help of precedent phenomena. Intellectualization shows itself in the active usage of the means of indirect communication by the journalists: tropes, wordplay. Unnecessarily liberal usage of language means for the expressive-evaluating and wordplay on purposes is often contiguous to speech aggression. Consequently studying modern media texts allows students to not only detect and characterize the main features of the modern mass media language and style, structural-semantic peculiarities of a modern mediatext, but also to give its socio-ethical evaluation. (shrink)

Social bots are computer programs that act like human users on social media platforms. Social bot detection is a rapidly growing field dominated by machine learning approaches. In this paper, we propose a complementary method to machine learning by exploring bot detection as a model checking problem. We introduce Temporal Network Logic (TNL) which we use to specify social networks where agents can post and follow each other. Using this logic, we formalize different types of social bot behavior (...) with formulas that are satisfied in a model of a network with bots. We also consider an extension of the logic where we explore the expressive power of including elements from hybrid logic in our framework. We give model checking algorithms for TNL and its hybrid extension, and show that the complexity of the former is in p and the latter in pspace. (shrink)