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)

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)

The paper investigates various definitions of the concept of opinion as opposed to factual or evidence-based statements and proposes a taxonomy of opinions expressed in English as identified in selected social media. A discussion situates opinions in the realm of pragmatics and reaches to philosophy of language and cognitive science. The research methodology combines a thorough linguistic analysis of opinions, proposing their multifaceted taxonomy with the automatically generated lexical embeddings of positive and negative lexicon acquired from the analysed opinionated texts. (...) As proposed, the definition of the concept of opinion is best apprehended when looked upon in terms of an opinion event, with a number of necessary conditions on the one hand, and those that are characteristic of an explicit opinion prototype on the other. Lists of opinion discourse markers show their preferential uses either in positive or negative opinionated texts; however, no sets of necessary and/or sufficient opinion markers properties have been acquired from the analysed texts. The conclusions indicate a polysemous understanding of naturally occurring social media opinionated texts and a definitional flexibility of the boundaries around lexical positive and negative types of opinion markers. (shrink)

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)