This research investigates the application of computational linguistic techniques to identify linguistic cues indicative of deception in Arabic political discourse. We analyze a corpus of political speeches and interviews, focusing on features such as sentiment polarity, hedging strategies, lexical diversity, and pragmatic markers. We develop and evaluate a machine learning model trained on these features to detect deceptive statements. The results demonstrate the potential of computational linguistics to uncover subtle linguistic patterns associated with deception in Arabic political communication, offering valuable insights for media analysis, political science, and cross-cultural communication research. The study also addresses the unique challenges of Arabic NLP in the context of deception detection, paving the way for future research in this area.
This paper explores the application of computational stylometry to analyze the evolution of authorial voice in 21st-century novels. We investigate how machine learning algorithms can identify and track stylistic markers, revealing subtle shifts in writing style across an author's oeuvre or within a single novel. By examining quantifiable features like word frequency, sentence structure, and punctuation patterns, we aim to understand how authors adapt their voice in response to various factors, including evolving literary trends, reader expectations, and personal stylistic development. Our analysis focuses on a selection of contemporary novels, employing diverse computational methods to uncover patterns and insights that may not be readily apparent through traditional literary criticism. The findings contribute to a deeper understanding of the dynamic nature of authorial voice in the digital age and demonstrate the potential of computational stylometry to enrich literary scholarship.
This study investigates the dynamic interplay between digital discourse and the evolving contours of Ukrainian identity. Employing a corpus-based approach, we analyze a substantial dataset of social media posts, online news articles, and forum discussions to identify key linguistic features and ideological narratives that shape and reflect contemporary Ukrainian self-perception. The research focuses on identifying recurring themes, sentiment patterns, and rhetorical strategies employed in online communication to understand how collective memory, political discourse, and social experiences are articulated and negotiated within the digital sphere. Furthermore, the study examines the influence of geopolitical events, such as the ongoing conflict, on the expression and construction of Ukrainian identity online. Our findings reveal a complex and multifaceted identity that is constantly being redefined through digital interactions, demonstrating the profound impact of online spaces on the formation and dissemination of national consciousness.
This paper explores the semiotic landscape of online discourse within contemporary social media, employing a corpus-based methodology to analyze both linguistic and visual modalities. By examining a large dataset of social media posts, we investigate how meaning is constructed and conveyed through the interplay of text, images, and other visual elements. Our analysis focuses on identifying prevalent semiotic codes and patterns, exploring their relationship to user engagement and sentiment, and ultimately, understanding how these multimodal communicative strategies shape online social interaction. The findings contribute to a deeper understanding of the complexities of digital communication and the evolving role of semiotics in the online environment. We address the limitations of previous research by incorporating a novel, computationally-assisted approach to visual semiotic analysis and by focusing on the emergent properties of multimodal meaning-making.
This research delves into the intricate landscape of deception detection, employing computational linguistic techniques to identify and analyze linguistic cues indicative of deceptive communication. Focusing on multimodal contexts, this study investigates the interplay between language, sentiment, and pragmatic elements in revealing deceptive intent. We propose a novel framework that integrates sentiment analysis, discourse analysis, and machine learning algorithms to detect deception with improved accuracy. The methodology involves analyzing a large corpus of text and audio-visual data, extracting relevant linguistic features, and training machine learning models to classify deceptive and truthful statements. The results demonstrate the effectiveness of our approach in identifying subtle linguistic patterns associated with deception, contributing significantly to the advancement of automated deception detection systems. The study concludes by highlighting the implications of these findings for various fields, including law enforcement, cybersecurity, and human-computer interaction, and outlines future research directions aimed at enhancing the robustness and generalizability of deception detection models.