Jul 2026· Annual International Computer Software and Applications Conference· pp. 2173-2178· 0 citations· 39 references
Abstract
This work investigates whether writing style is associated with stance alignment in large scale online discourse. We construct a Reddit based pipeline that transforms r/AskReddit questions and comments interactions into stance units using large language models (LLMs), stylistic embeddings, and semantic embeddings. The study begins with 169,053 original AskReddit questions, categorizes them into 48 semantic classes, and filters them for “stance suitability”. The full question corpus contains 176,932 rows; in the target stance evaluation stage, 22,238 original questions from 10 selected semantic classes are paired with up to 200 top level comments each and annotated by an LLM, yielding 277,474 comment level model outputs and 352,407 stance instances. Style is represented with answer text embeddings trained to capture writing style, while semantic context is represented with embeddings of question summaries, stance targets, and canonical opinions. We analyze 8,000,000 randomly sampled stance instance pairs by measuring style similarity, topic similarity, and stance label agreement. Across topic similarity thresholds from 0.50 to 0.95, pairs with high stylistic similarity consistently show higher model annotated stance agreement than the topic similar baseline, with lifts of approximately 6% to 12% points. A parallel augmentation analysis shows that synthetic questions produced by the tested models under generalize real class diversity, motivating the use of original questions for the main stance analysis. These findings suggest that writing style carries a weak but measurable signal related to stance alignment under semantically controlled conditions.
Opinionated text - spanning product reviews, hotel feedback, and social posts - captures rich signals about user experiences, preferences, and concerns. However, the scale, redundancy, and imbalance of such corpora make it challenging to analyze opinions effectively, particularly when the goal is to generate summaries that remain faithful to the diversity of viewpoints expressed. This paper presents a framework that preserves semantics in LLM-based opinion summarization while minimizing token usage. We combine multidimensional classification (e.g., sentiment, topics) with a family of stratified sampling strategies to select compact yet representative subsets of opinions before prompting the LLM. Tailored prompts then produce balanced summaries that surface the salient aspects expressed in the opinions (e.g., strengths and weaknesses of products/hotels). Experiments on Amazon product reviews, Tripadvisor hotel reviews, and X/Twitter posts demonstrate that our method significantly reduces token usage and computational cost while consistently outperforming traditional AI-based and standard LLM summarization baselines in terms of content coverage, balance, and semantic preservation.
This study proposes a robust and semantically enriched framework for conversation understanding based on a composite distributed representation that incorporates both temporal adjacency and semantic proximity between utterances, enabling the visualization of key conversational connections.
Large language models (LLMs) are known to be sensitive to prompt and input formulations. However, existing studies have focused on lexical realization and largely ignored constructional choice. This paper studies whether linguistic construction can systematically shift LLM decisions and where these shifts can be causally localized inside the model. We use political stance judgment as a meaning-sensitive case study and extend an English political statements dataset, resulting in six controlled linguistic rewrite types that preserve or invert the meaning of a statement. Experiments on four open-weight models show that stance instability affect both meaning-preserving and meaning-inversing rewrites. Because output shifts reveal that rewrites affect stance, but not where in the model, we apply activation patching, where activations from the original statement are substituted into the forward pass for the rewritten statement and measure which components recover the original stance distribution. The results show that mid-to-late decoder layers, especially block outputs at the final prompt position, provide the strongest restoration signal.
Langchen Huang, Sebastian Padó, Franziska Weeber· 0 citations
Stance detection aims to identify whether a text expresses a favorable or opposing attitude toward a given target, and serves as an important task for various downstream applications. Although existing studies have achieved strong performance in monolingual settings, especially in English, many low-resource languages such as Catalan still lack sufficient annotated data for training effective models. Cross-lingual stance detection alleviates this problem by transferring stance knowledge from resource-rich languages to low-resource languages. However, most existing methods mainly rely on semantic alignment between texts and targets, while ignoring the reasoning process required for reliable stance inference. Although Large Language Models provide strong reasoning ability, their high computational cost and inference latency limit practical deployment. To address these limitations, we propose a rationale-guided knowledge distillation framework for cross-lingual stance detection. Specifically, we use Chain-of-Thought prompting to guide Large Language Models in generating informative rationales, and distill the resulting reasoning knowledge into a compact student model. We further design a dual-path distillation mechanism to align rationale-enhanced and rationale-free representations, together with their prediction distributions. In addition, two contrastive learning strategies are introduced to improve stance discrimination. Experiments on multilingual benchmarks demonstrate that our method consistently outperforms competitive baselines.
Qiuli Zhou, Jingyuan Yao, Shengeng Tang et al.· 0 citations
Stance classification in Natural Language Processing (NLP) is not just an academic exercise but a crucial tool for understanding political discourse and the attitudes underlying political statements. This research addresses the challenge of limited annotated datasets in political science by proposing a practical sentence-level dataset sourced from professional politicians for binary subjective stance classification - support or oppose - using bootstrapping in a SetFit model. The study leverages the Sentence Transformers architecture and incorporates traditional linguistic approaches to enhance explainability. We employ corpus linguistics, tailored lexicons, and lexicogrammatical rules to identify key linguistic features such as positive affect, negative affect, pro polarity, con polarity, certainty, emphatics, doubt, hedges. SHAP analysis quantifies the influence of these features on SetFit model decisions. Our findings demonstrate that iterative bootstrapping significantly enhances the efficacy of few-shot learning in subjective stance classification, and we highlight the importance of linguistic features, particularly pro/con polarity and affective expressions. The StanceSentences dataset and our hybrid analytical approach offer a benchmark for future research, emphasizing the need for nuanced, multi-layered analysis in political discourse.
Juan-Francisco Reyes· Journal for Language Technol...· 0 citations
Although stance detection has gained substantial interest in high-resource languages, it remains largely unexplored for Manipuri, a low-resource Indo-Tibeto-Burman language with limited annotated data and linguistic resources. This paper proposes a framework for stance detection in Manipuri editorial articles. The dataset created for this research is balanced with respect to the stance categories, namely, FOR, AGAINST, and NEUTRAL. Both classical machine learning algorithms and Keyword-Guided Bidirectional Long Short-Term Memory (KG-BiLSTM) are evaluated on the dataset. The KG-BiLSTM architecture proposed in the paper consists of FastText embeddings along with a label-independent keyword-guided attention network constructed from manually curated stance lexicons. The attention mechanism enables the model to focus on potentially stance-bearing words without using ground-truth class information during mask construction. With five-fold stratified cross-validation, the KG-BiLSTM obtains an average accuracy of 71.2% and a Macro-F1 score of 70.1%, outperforming logistic regression and support vector machine baselines. The model reduces confusion between FOR and NEUTRAL editorials, which is one of the most critical problems for stance identification.