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\"This book combines traditional theories of narratology with current narrative generation practices using artificial intelligence\"-- Provided by publisher.

This book combines stylistic analysis with corpus linguistics to present an innovative account of the phenomenon of speech, writing and thought presentation - commonly referred to as 'speech reporting' or 'discourse presentation'. This new account is based on an extensive analysis of a quarter-of-a-million word electronic collection of written narrative texts, including both fiction and non-fiction. The book includes detailed discussions of: The construction of this corpus of late twentieth-century written British narratives taken from fiction, newspaper news reports and (auto)biographies The development of a manual annotation system for speech, writing and thought presentation and its application to the corpus. The findings of a quantitive and qualitative analysis of the forms and functions of speech, writing and thought presentation in the three genres represented in the corpus. The findings of the analysis of a range of specific phenomena, including hypothetical speech, writing and thought presentation, embedded speech, writing and thought presentation and ambiguities in speech, writing and thought presentation. Two case studies concentrating on specific texts from the corpus. Corpus Stylistics shows how stylistics, and text/discourse analysis more generally, can benefit from the use of a corpus methodology and the authors' innovative approach results in a more reliable and comprehensive categorisation of the forms of speech, writing and thought presentation than have been suggested so far. This book is essential reading for linguists interested in the areas of stylistics and corpus linguistics. Elena Semino is Senior Lecturer in the Department of Linguistics and Modern English Language at Lancaster University. She is the author of Language and World Creation in Poems and Other Texts (1997), and co-editor (with Jonathan Culpetter) of Cognitive Stylistics: Language and Cognition in Text Analysis (2002). Mick Short is Professor of English Language and Literature at Lancaster University. He has written Exploring the Language of Poems, Plays and Prose (1997) and (with Geoffrey Leech) Style in Fiction (1997). He founded the Poetics and Linguistics Association and was the founding editor of its international journal, Language and Literature. 1. A Corpus-Based Approach to the Study of Discourse Presentation in Written Narratives 2. Methodology: The Construction and Annotation of the Corpus 3. A Revised Model of Speech, Writing and Thought Presentation 4. Speech Presentation in the Corpus: A Quantitative and Qualitative Analysis 5. Writing Presentation in the Corpus: A Quantitative and Qualitative Analysis 6. Thought Presentation in the Corpus: A Quantitative and Qualitative Analysis 7. Specific Phenomena in Speech, Writing Presentation 8. Case Studies of Specific Texts from the Corpus 9. Conclusion

The digital era has not only given us the world of big data, but the tools to deal with this mostly unstructured, mostly textual data: Natural Language Processing (NLP) tools. Yet, most humanists and social scientists do not work with big data. They do not deal with millions of documents. Literary critics’ corpora are the handful of works produced by an author. The historians’ primary source documents number in the tens, perhaps hundreds. Social scientists deal with tens or hundreds of transcripts of focus groups and in-depth interviews, or at most a few thousand media articles. And they analyze these data either qualitatively or quantitatively with a variety of manual or computer-assisted methodologies, from content analysis to frame analysis, discourse analysis, quantitative narrative analysis. But, once developed, at least some of the NLP tools of automatic textual analysis and the data analytics visualization tools, can be applied not just to big data but to small data as well. This paper illustrates how some of these tools can be used by focusing on a short first-person narrative. And the NLP tools reveal patterns of language use perhaps not immediately discernible, thus proving useful in the analysis of even small data. But understanding and interpreting these patterns requires knowledge way beyond the NLP tools themselves. Humanists and social scientists need not fear computer scientists; rather, they need to learn to take advantage of them. NLP tools lay a bridge between quality and quantity, with much to be gained from a constant interaction between distant and close reading.

Overcoming gender inequities is a global priority recognized as essential for improved health and human development. Gender-based violence (GBV) is an extreme manifestation of gender inequities enacted in real-world and internet-based environments. In Nigeria, GBV has come to the forefront of attention since 2020, when a state of emergency was declared due to increased reporting of sexual violence. Understanding GBV-related social narratives is important to design public health interventions. We explore how gender-related internet-based conversations in Nigeria specifically related to sexual consent (actively agreeing to sexual behavior), lack of consent, and slut-shaming (stigmatization in the form of insults based on actual or perceived sexuality and behaviors) manifest themselves and whether they changed between 2017 and 2022. Additionally, we explore what role events or social movements have in shaping gender-related narratives in Nigeria. Social listening was carried out on 12,031 social media posts (Twitter, Facebook, forums, and blogs) and almost 2 million public searches (Google and Yahoo search engines) between April 2017 and May 2022. The data were analyzed using natural language processing to determine the most salient conversation thematic clusters, qualitatively analyze time trends in discourse, and compare data against selected key events. Between 2017 and 2022, internet-based conversation about sexual consent increased 72,633%, from an average 3 to 2182 posts per month, while slut-shaming conversation (perpetrating or condemning) shrunk by 9%, from an average 3560 to 3253 posts per month. Thematic analysis shows conversation revolves around the objectification of women, poor comprehension of elements of sexual consent, and advocacy for public education about sexual consent. Additionally, posters created space for sexual empowerment and expressions of sex positivity, pushing back against others who weaponize posts in support of slut-shaming narrative. Time trend analysis shows a greater sense of empowerment in advocating for education around the legal age of consent for sexual activity, calling out double standards, and rejecting slut-shaming. However, analysis of emotions in social media posts shows anger was most prominent in sexual consent (n=1213, 73%) and slut-shaming (n=226, 64%) posts. Organic social movements and key events (#ArewaMeToo and #ChurchToo, the #SexforGrades scandal, and the #BBNaija television program) played a notable role in sparking discourse related to sexual consent and slut-shaming. Social media narratives are significantly impacted by popular culture events, mass media programs, social movements, and micro influencers speaking out against GBV. Hashtags, media clips, and other content can be leveraged effectively to spread awareness and spark conversation around evolving gender norms. Public health practitioners and other stakeholders including policymakers, researchers, and social advocates should be prepared to capitalize on social media events and discourse to help shape the conversation in support of a normative environment that rejects GBV in all its forms.

The rise of Online Social Networks has reshaped global discourse, enabling real-time conversations on complex issues such as irregular migration. Yet the informal, multilingual, and often noisy nature of content on platforms like X (formerly Twitter) and Telegram presents significant challenges for reliable automated analysis. This study presents an exploratory multilingual natural language processing (NLP) framework for detecting irregular migration discourse across five languages. Conceived as a pilot study addressing extreme data scarcity in sensitive migration contexts, this work evaluates transformer-based models on a curated multilingual corpus. It provides an initial baseline for monitoring informal migration narratives on X and Telegram. We evaluate a broad range of approaches, including traditional machine learning classifiers, SetFit sentence-embedding models, fine-tuned multilingual BERT (mBERT) transformers, and a Large Language Model (GPT-4o). The results show that GPT-4o achieves the highest performance overall (F1-score: 0.84), with scores reaching 0.89 in French and 0.88 in Greek. While mBERT excels in English, SetFit outperforms mBERT in low-resource settings, specifically in Arabic (0.79 vs. 0.70) and Greek (0.88 vs. 0.81). The findings highlight the effectiveness of transformer-based and large-language-model approaches, particularly in low-resource or linguistically heterogeneous environments. Overall, the proposed framework provides an initial, compact benchmark for multilingual detection of irregular migration discourse under extreme, low-resource conditions. The results should be viewed as exploratory indicators of model behavior on this synthetic, small-scale corpus, not as statistically generalizable evidence or deployment-ready tools. In this context, “multilingual” refers to robustness across different linguistic realizations of identical migration narratives under translation, rather than coverage of organically diverse multilingual public discourse.

Attitudinal language plays a crucial role in shaping narratives and character relationships in cinematic discourse, yet little research has explored its function in family-centered films. This study examines the attitudinal language in I Am Sam (2001) and Big Daddy (1999) through the Appraisal Framework (Martin & White, 2005), focusing on the distribution and lexico-grammatical realization of Affect, Judgment, and Appreciation. Using a corpus-assisted qualitative approach, the study analyses how emotional expressions, moral evaluations, and value assessments are embedded in cinematic dialogues. The findings reveal that Affect is the most dominant attitudinal resource, with desire-related expressions shaping the films’ emotional intensity. Judgment is particularly prevalent in evaluations of capacity (competence), with Big Daddy featuring a higher proportion of negative judgments than I Am Sam. Appreciation primarily revolves around quality rather than structural or aesthetic assessments. Additionally, verbs and adjectives serve as primary linguistic carriers of evaluation, while rhetorical questions, repetition, and imperatives contribute to implicit attitudinal meanings. This study sheds light on the linguistic construction in family-focused film and expands the discourse on appraisal in media narratives and offers insights into how attitudinal language functions within cinematic storytelling.

The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party’s narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats’ narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government’s role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the “elusive consensus” between the two parties. Our methodologies may be applied to computationally study narratives in various domains.

Background: Treatment nonadherence in oncology is common. Surveys often miss why patients do not follow recommendations. We synthesised Natural Language Processing (NLP) studies, mainly sentiment analysis, of patient-generated content (social media, forums, blogs, review sites, and survey free text) to identify communication and relationship factors linked to nonadherence and concordance. Methods: We conducted a scoping review (PRISMA-ScR). Searches of PubMed, CINAHL, and Scopus from 2013 to 15 June 2024 identified eligible studies. We included 25 studies. Data were charted by source, cancer type, NLP technique, and adherence/concordance indicators, then synthesised via discourse analysis and narrative synthesis. Results: Four themes emerged: (1) unmet emotional needs; (2) suboptimal information and communication; (3) unclear concordance within person-centred care; and (4) misinformation dynamics and perceived clinician bias. Sentiment analysis helped identify emotions and information gaps that surveys often miss. Conclusions: Patient-voice data suggest practical actions for nursing, including routine distress screening, teach-back, misinformation countermeasures, and explicit concordance checks to improve adherence and shared decision making. Registration: Not registered.

This study investigated which of the three analytic approaches of oral discourse, including linguistically based measures, proposition-based measures, and story grammar, best correlated with aphasia severity and with naïve listeners' ratings on aphasic productions. The predictive power of these analytic approaches to aphasia severity and fluency status of people with aphasia (PWA) was examined. Finally, which approach best discriminated fluent versus nonfluent PWA was determined. Audio files and orthographic transcriptions of the storytelling task \"The Boy Who Cried Wolf\" from 68 PWA and 68 controls were extracted from the Cantonese AphasiaBank. Each transcript was analyzed using these 3 systems. The linguistic approach of discourse analysis best correlated with aphasia severity and naïve listeners' subjective ratings. Although both linguistically based and proposition-based measures significantly predicted aphasia severity, a subset of linguistic measures focusing on the quantity and efficiency of production were particularly useful for clinical estimation of the fluency status of aphasia. The linguistically based measures appeared to be the most clinically effective and powerful in reflecting PWA's performance of spoken discourse.