Towards robust stance detection: data augmentation, model fine-tuning, and empirical benchmarking

Team LatentAuthors at PAN 2026: DeBERTa with Genre-Aware Fine-Tuning for AI Text Detection

Leveraging LLM for Sentiment Detection in News Headlines

In this study, a longitudinal dataset of more than 23 million news headlines from 47 U.S.-based media outlets is used to investigate the use of large language models (LLMs) for sentiment detection. Recent developments in LLMs offer potential gains in contextual understanding, adaptability, and generalization, even thou...

A Compact CNN–BiLSTM Model for Fake News Detection: Post Hoc Explanations and Cross-Dataset Evaluation

The results support the CNN–BiLSTM as a compact and competitive text-based alternative while highlighting the importance of controlled baselines, overlap analysis, cautious interpretation of post hoc explanations, and strict external evaluation.

The Impact of Synthetic Data Augmentation on Discourse-Pragmatic Function Classification

Synthetic data augmentation has become a common strategy for addressing class imbalance in NLP, but most approaches focus on the quantity and diversity of generated examples rather than their geometric relationship to real training data. We investigate this question in the context of discourse pragmatic function classi...

SentiMatrix: Parameter-Efficient Fine-Tuning of Encoder-Based Transformers for Multidimensional Sentiment Analysis

Transformer-based architectures have established state-of-the-art benchmarks across Natural Language Processing (NLP) tasks; however, the computational overhead of full fine-tuning remains a significant barrier to scalable deployment. This paper presents SentiMatrix, a systematic evaluation of Low-Rank Adaptation (LoRA...

LoveHate: Stance Detection and Generation for Multiple Topics in User-generated Comments in Russian and English

This paper introduces LoveHate, a new multi-topic corpus of user-generated arguments in Russian, collected from the historical data of the debate platform lovehate.ru. The dataset contains nearly 19,000 posts spanning 16 socially and politically relevant topics, each mapped to binary pro and con stances. We test multip...