Key point extraction is an important task in argument summarization which involves extracting high-level short summaries from arguments. Existing approaches for KP extraction have been mostly evaluated on the popular ArgKP21 dataset. In this paper, we highlight some of the major limitations of the ArgKP21 dataset and demonstrate the need for new benchmarks that are more representative of actual human conversations. Using SoTA large language models (LLMs), we curate a new argument key point extraction dataset called ArgCMV comprising of around 12K arguments from actual online human debates spread across over 3K topics. Our dataset exhibits higher complexity such as longer, co-referencing arguments, higher presence of subjective discourse units, and a larger range of topics over ArgKP21. We show that existing methods do not adapt well to ArgCMV and provide extensive benchmark results by experimenting with existing baselines and latest open source models. This work introduces a novel KP extraction dataset for long-context online discussions, setting the stage for the next generation of LLM-driven summarization research.

These authors contributed equally to this work. Abstract --As Large Language Models (LLMs) become increasingly integrated into online platforms and digital communication spaces, their potential to influence public discourse--particularly in contentious domains like climate change--demands systematic investigation. This study examines how LLMs naturally moderate climate change conversations through their distinct communicative behaviors, offering insights into their role as facilitators of civil discourse. We conducted a comparative analysis of conversational patterns between LLMs and human participants in climate change discussions across social media platforms. Our investigation employed five state-of-the-art models: three open-source LLMs (Gemma, Llama 3, and Llama 3.3) and two commercial systems (GPT -4o by OpenAI and Claude 3.5 by Anthropic). Through sentiment analysis, we assessed the emotional characteristics and discourse patterns exhibited by both LLMs and human users. Our findings reveal two key mechanisms through which LLMs moderate climate change conversations: First, LLMs consistently demonstrate emotional neutrality, with their responses significantly dominated by neutral sentiment compared to human participants who exhibit more polarized emotional expressions. Second, LLMs maintain notably lower emotional intensity across all interaction contexts, creating a stabilizing effect on conversational dynamics. These results suggest that LLMs possess inherent moderating capabilities that could enhance the quality of public discourse on controversial topics. By maintaining emotional equilibrium and reducing inflammatory rhetoric, LLMs may serve as valuable tools for fostering more constructive and civil climate change conversations online. This research contributes to our understanding of AI's potential role in improving digital discourse and offers implications for the design of AI-mediated communication platforms.

Political online participation in the form of discussing political issues and exchanging opinions among citizens is gaining importance with more and more formats being held digitally. To come to a decision, a careful discussion and consideration of opinions and a civil exchange of arguments, which is defined as the act of deliberation, is desirable. The quality of discussions and participation processes in terms of their deliberativeness highly depends on the design of platforms and processes. To facilitate online communication for both participants and initiators, machine learning methods offer a lot of potential. In this work we want to showcase which issues occur in political online discussions and how machine learning can be used to counteract these issues and enhance deliberation.

We present a survey of methods for assessing and enhancing the quality of online discussions, focusing on the potential of Large Language Models (LLMs). While online discourses aim, at least in theory, to foster mutual understanding, they often devolve into harmful exchanges, such as hate speech, threatening social cohesion and democratic values. Recent advancements in LLMs enable facilitation agents that not only moderate content, but also actively improve the quality of interactions. Our survey synthesizes ideas from Natural Language Processing (NLP) and Social Sciences to provide (a) a new taxonomy on discussion quality evaluation, (b) an overview of intervention and facilitation strategies, along with a new taxonomy on conversation facilitation datasets, (c) an LLM-oriented roadmap of good practices and future research directions, from technological and societal perspectives.

Online spaces allow people to discuss important issues and make joint decisions, regardless of their location or time zone. However, without proper support and thoughtful design, these discussions often lack structure and politeness during the exchanges of opinions. Artificial intelligence (AI) represents an opportunity to support both participants and organizers of large-scale online participation processes. In this paper, we present an extension of adhocracy+, a large-scale open source participation platform, that provides two additional debate modules that are supported by AI to enhance the discussion quality and participant interaction.

Social media conversations frequently suffer from toxicity, creating significant issues for users, moderators, and entire communities. Events in the real world, like elections or conflicts, can initiate and escalate toxic behavior online. Our study investigates how real-world events influence the origin and spread of toxicity in online discussions across various languages and regions. We gathered Reddit data comprising 4.5 million comments from 31 thousand posts in six different languages (Dutch, English, German, Arabic, Turkish and Spanish). We target fifteen major social and political world events that occurred between 2020 and 2023. We observe significant variations in toxicity, negative sentiment, and emotion expressions across different events and language communities, showing that toxicity is a complex phenomenon in which many different factors interact and still need to be investigated. We will release the data for further research along with our code.

Modern democracies face a critical issue of declining citizen participation in decision-making. Online discussion forums are an important avenue for enhancing citizen participation. This thesis proposal 1) identifies the challenges involved in facilitating large-scale online discussions with Natural Language Processing (NLP), 2) suggests solutions to these challenges by incorporating hybrid human-AI technologies, and 3) investigates what these technologies can reveal about individual perspectives in online discussions. We propose a three-layered hierarchy for representing perspectives that can be obtained by a mixture of human intelligence and large language models. We illustrate how these representations can draw insights into the diversity of perspectives and allow us to investigate interactions in online discussions.

Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, automating these measures has become feasible. While some studies focus on analyzing specific quality indicators, a comprehensive quality score incorporating various deliberative aspects is often preferred. In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post. Unlike other singular scores, AQuA preserves information on the deliberative aspects present in comments, enhancing model transparency. We develop adapter models for 20 deliberative indices, and calculate correlation coefficients between experts' annotations and the perceived deliberativeness by non-experts to weigh the individual indices into a single deliberative score. We demonstrate that the AQuA score can be computed easily from pre-trained adapters and aligns well with annotations on other datasets that have not be seen during training. The analysis of experts' vs. non-experts' annotations confirms theoretical findings in the social science literature.

Disagreements are common in online discussions. Disagreement may foster collaboration and improve the quality of a discussion under some conditions. Although there exist methods for recognizing disagreement, a deeper understanding of factors that influence disagreement is lacking in the literature. We investigate a hypothesis that differences in personal values are indicative of disagreement in online discussions. We show how state-of-the-art models can be used for estimating values in online discussions and how the estimated values can be aggregated into value profiles. We evaluate the estimated value profiles based on human-annotated agreement labels. We find that the dissimilarity of value profiles correlates with disagreement in specific cases. We also find that including value information in agreement prediction improves performance.

Asynchronous online discussions are a common fundamental tools to facilitate social interaction in hybrid and online courses. However, instructors lack the tools to accomplish the overwhelming task of evaluating asynchronous online discussion activities. In this paper we present an approach that uses Latent Dirichlet Analysis (LDA) and the instructor's keywords to automatically extract codes from a relatively small dataset. We use the generated codes to build an Epistemic Network Analysis (ENA) model and compare this model with a previous ENA model built by human coders. The results show that there is no statistical difference between the two models. We present an analysis of these models and discuss the potential use of ENA as a visualization to help instructors evaluating asynchronous online discussions.