Volume 20, Issue 2 - Special Issue: Pragmatics, digital content and opinions; Guest Editors: Barbara Lewandowska-Tomaszczyk and Marcin Trojszczak

This paper focuses on the news representation of Liz Truss by the British press, starting from the identification of news values. In addition, our analysis investigates the portrayal of Truss as a gendered social actor. The data were retrieved online through the resource LexisNexis – using the keyword “Truss” in headlines and lead paragraphs in both British broadsheets and tabloids – during three crucial days: 10 July 2022, the day Truss announced her candidacy for the leadership of the Conservative Party and as Prime Minister, 5 September 2022, when she became leader of the Conservative Party and consequently the new Prime Minister, and 20 October 2022, the day she announced her resignation as leader of the Conservative Party and Prime Minister. These data were analysed combining Bednarek’s and Caple’s Discursive News Values Analysis (DNVA) and Multimodal Critical Discourse Analysis (MCDA). Our findings identify the most frequent news values employed by British national newspapers in Truss’ portrayal during the selected timespans and highlight the connection between those values and her gendered representation.

Language technology – starting from the handwriting in early ages of humanity and perfectionated in the age of social media and micro-targeting - is used to gain and maintain power, and to rebel against it. Social media such as Telegram provide a safe space for freedom fighters in authoritarian regimes but also offer a stage for extremist, aggressive and manipulative content. This work uses romantic relationships as an example topic. The article discusses how the language used shapes the interpretation of political reports based on literature. Further, this work analyses metaphorical frames around the concepts related to romantic relationships and family in influential news outlets from three countries in three languages. The analysis shows that conservative political actors use similar metaphorical frames for relationship-related concepts such as LGBT and ‘traditional family’, and with similar, manipulative purposes. The results suggest that anti-democratic discourses build on exploitation of social categories rendered subordinate to men in a similar way across conservative movements. However, changes in political leadership can facilitate societal changes towards more open and more democratic societies, but they can also change cultures and traditions to suit power interests.

This paper examines anger expression in a corpus of 66 customer service complaints posted to the Mexican X/Twitter account of the online cab company, Uber. Specifically, building on work on emotions and (im)politeness as well as work on service-related anger episodes, it focuses on how these complainers signal their anger, and how these emotional expressions are related to rapport management orientation. Adopting a qualitative perspective, we observed complainers in this corpus expressing anger explicitly (using specific lexical choices such as profanities), implicitly (for example, asking rhetorical questions with semantic connotations of anger), or indulging in anger behaviours (threatening retaliatory behaviour). The majority of these strategies would be considered rapport challenging, however, certain more constructive, rapport enhancing strategies were also observed: for example, complainers portrayed themselves as approachable using medium specific in-group slang or provided means to open a dialogue to solve whatever problem they were complaining about.

The present paper aims to investigate the role played by figurative language, in particular metaphor and simile, in expressing negative opinions in reviews of popular music albums. In order to explore this phenomenon at the intersection of cognitive linguistics, discourse analysis, and pragmatics, it makes use of language data gathered from selected critical reviews of music albums from a reputed English-speaking music website Pitchfork.com. More specifically, the paper analyses selected instances of negatively-laden metaphors and similes so as to demonstrate how they are used to portray key objects of criticism such as music album as a whole, artists and their careers, music genre and its production, songs in the album, and their lyrics. Moreover, it analyses figurative imagery used to express negative opinions about these objects of criticism by pointing out the prominent role of personification and objectification.

The process of slur reclamation is a linguistic phenomenon whereby members of a targeted group begin to employ their own slur as a means of self-labelling, expressing pride, fostering camaraderie, and reversing the derogatory perspective of the slur. This action is designed to deprive racist and bigoted institutions of a means of perpetuating social power imbalances. Some authors posit that slur reclamation entails a shift in meaning. The ambiguity thesis (AT) can be developed in a variety of ways depending on the semantic level at which the derogatory content is situated. However, AT is subject to a number of criticisms, including what has been termed Appropriation Worry (AW). This paper seeks to elucidate the semantic mechanisms of slur reclamation without recourse to AT. To do this, an imperative account of slurs is adopted, which will be termed warning theory of slurs (WT). This account proposes that reclaimed uses of slurs are expressions of irony and resilience.

Over one million tokens of comments were collected for the study using data mining methods. The videos under which the comments were dug out were not chosen arbitrarily but according to the current official national YouTube trends in Poland and the Czechia. The comments were collected under the most popular videos in ten categories: cars, comedy, fashion & lifestyle, gaming, music, non-political interview, politics, report, sports, and video blog. The data collection was carried in 2021–2022 and 2023 from under 40 videos per language and period, 160 videos in total. The corpus data should reveal more about the internet register through the part of speech (POS) frequencies, and the syntactical statistics. In addition, the comments are stylistically clustered in R to make dependencies in linguistic usage visible and better understandable. The study aims to expand the term register, using Polish and Czech, to include language on the internet and distinguish between authentic and non-authentic internet registers in comparison to other registers. An additional sociolinguistic aspect of the analysis is the influence of the Russian war against Ukraine on the linguistic behavior of YouTube users.

The global burden of obesity has risen due to various factors, including sociocultural aspects. Social representations (SRs) of obesity could help to understand the problem. Nowadays, social networks activate new social interaction processes and enable the construction of SRs. Tweets can identify mind-sets as cultural reflections of the times. This study aimed to identify widely shared obesity topics on Twitter-Argentina using Natural Language Processing. First, 134,766 Spanish tweets about obesity were collected from August 2021 to July 2022. Next, a geolocation filter removed non-Argentinian messages, leaving 48,149 tweets. The body text was cleaned and prepared for analysis. K-means clustering model was applied and 21 clusters were identified after reaching theoretical saturation. Then, 10 tweets from each cluster were randomly selected to analyse word usage and identify cluster themes. Thus, the main themes were “Gender-related”, “Obesity and family”, “Hate speech and fatphobia”, “Body image, perceptions and feelings”, “Public health actions” and “Obesity as a health-disease process”. These aggregations allowed us to understand obesity as a sociocultural phenomenon, the Argentinian culture-specific discourses surrounding it, and their connections to health. We firmly believe that this valuable information will enlighten the planning of future social-political actions to address this health-disease process.

The paper tests conversational Large Language Models, instructed to produce stance expression types (affective, relational, epistemic, and moral) and their contexts in Opinion (Speech) Events (Lewandowska-Tomaszczyk, Barbara, Chaya Liebeskind, Anna Baczkowska, Jurate Ruzaite, Ardita Dylgjeri, Ledia Kazazi & Erika Lombart 2023. Opinion events: Types and opinion markers in English social media discourse. Lodz Papers in Pragmatics 19(2). 447–481). In the first part an opinion taxonomy proposed in (Lewandowska-Tomaszczyk, Barbara, Chaya Liebeskind, Anna Baczkowska, Jurate Ruzaite, Ardita Dylgjeri, Ledia Kazazi & Erika Lombart 2023. Opinion events: Types and opinion markers in English social media discourse. Lodz Papers in Pragmatics 19(2). 447–481) is discussed in terms of Explicit (direct or indirect) and Implicit opinionated texts, categorized as positive, negative, ambiguous, or balanced. The further part discusses our previous attempts at Explicit (direct/indirect) and Implicit opinion type generation, performed by means of a series of prompts with LLMs (ChatGPT and Gemini) (Liebeskind, Chaya & Barbara Lewandowska-Tomaszczyk. 2024a. Opinion identification using a conversational large language model. In FLAIRS conference Proceedings . Florida, Liebeskind, Chaya & Barbara Lewandowska-Tomaszczyk. F 2024b. Navigating opinion space: A Study of explicit and implicit opinion generation in language models . Santiago de Compostella: EAIS conference publication), while this paper presents further LLM experiments with chatGPT and Gemini as well as their results, based on the analysis of stance expression types , which lead to increased success in opinion context generation.

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.

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.