In our paper we explore the definition, and extrapolation of fallacies as they pertain to the automatic detection of manipulation on social media. In particular we explore how these logical fallacies might appear in the real world i.e internet forums. We discovered a prevalence of misinformation / misguided intention in discussion boards specifically centered around the Ukrainian Russian Conflict which serves to narrow the domain of our task. Although automatic fallacy detection has gained attention recently, most datasets use unregulated fallacy taxonomies or are limited to formal linguistic domains like political debates or news reports. Online discourse, however, often features non-standardized and diverse language not captured in these domains. We present Shady Linguistic Utterance Replication-Generation (SLURG) to address these limitations, exploring the feasibility of generating synthetic fallacious forum-style comments using large language models (LLMs), specifically DeepHermes-3-Mistral-24B. Our findings indicate that LLMs can replicate the syntactic patterns of real data} and that high-quality few-shot prompts enhance LLMs' ability to mimic the vocabulary diversity of online forums.

Due to this complexity, research into online hate The rapid integration of the Internet into our daily lives has led to is fragmented throughout numerous disciplines. Despite all these many benefits but also to a number of new, wide-spread threats extensive approaches and methods proposed to analyse online hate such as online hate, trolling, bullying, and generally aggressive [1, 12], limited research has investigated how hateful behaviours behaviours. While research has traditionally explored online hate, and content compare and relate across different online platforms in particular, on one platform, the reality is that such hate is a [8]. It has only recently been recognised within academic literature phenomenon that often makes use of multiple online networks. In that online hate is not simply an issue for a select few platforms, this article, we seek to advance the discussion into online hate by rather networks of hate are often linked across these platforms, harnessing a comparative approach, where we make use of various forming a global'network of networks' dynamic [6]. Natural Language Processing (NLP) techniques to computationally Our study applies various computational methods, including analyse hateful content from Reddit and 4chan relating to the 2020 topic modelling and sentiment analysis, to explore the type of US Presidential Elections. Our findings show how content and content that is promoted on Reddit and 4chan to provide unique posting activity can differ depending on the platform being used.