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 multiple approaches to stance detection and stance generation across Russian and English data, including translated variants, using both classifier-based (Roberta, RuRoberta) and instruction-tuned generative (Llama, Qwen) models. Results demonstrate that language-specific pretraining yields the strongest performance for stance classification (F1 = 0.892 with RuRoberta), while multilingual generative models – when fine-tuned on sufficient data – can effectively generate stance in Russian without explicit Russian pretraining. Cross-domain experiments show that English datasets generalize better across corpora, whereas Russian data capture language- and culture-specific argumentation but are less effective for generalizable models. Generating topics remains a more challenging task for both Russian and English data. The dataset and accompanying results contribute to multilingual stance research and provide a valuable new resource for argument mining in Russian.

Definition generation models trained on dictionary data are generally expected to produce neutral and unbiased output while capturing the contextual nuances. However, previous studies have shown that generated definitions can inherit biases from both the underlying models and the input context. This paper examines the extent to which stance-related bias in argumentative data influences the generated definitions. In particular, we train a model on a slang-based dictionary to explore the feasibility of generating persuasive definitions that concisely reflect opposing parties’ understandings of contested terms. Through this study, we provide new insights into bias propagation in definition generation and its implications for definition generation applications and argument mining.

While persuasion has been extensively examined in the context of politicians’ speeches, there exists a notable gap in the understanding of the pathos role in user-generated argumentation. This paper presents an exploratory study into the pathos dimension of user-generated arguments and formulates ideas on how pathos could be incorporated in argument mining. Using existing sentiment and emotion detection tools, this research aims to obtain insights into the role of emotion in argumentative public discussion on controversial topics, explores the connection between sentiment and stance, and detects frequent emotion-related words for a given topic.