TruthStance: An Annotated Dataset of Conversations on Truth Social
- URL: http://arxiv.org/abs/2602.14406v1
- Date: Mon, 16 Feb 2026 02:25:02 GMT
- Title: TruthStance: An Annotated Dataset of Conversations on Truth Social
- Abstract summary: We introduce TruthStance, a large-scale dataset of Truth Social conversation threads spanning 2023-2025.<n>We provide a human-annotated benchmark of 1,500 instances across argument mining and claim-based stance detection.<n>Using the best-performing configuration, we release additional labels for 24,352 posts (argument presence) and 107,873 comments (stance to parent)
- Score: 0.5249805590164902
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Argument mining and stance detection are central to understanding how opinions are formed and contested in online discourse. However, most publicly available resources focus on mainstream platforms such as Twitter and Reddit, leaving conversational structure on alt-tech platforms comparatively under-studied. We introduce TruthStance, a large-scale dataset of Truth Social conversation threads spanning 2023-2025, consisting of 24,378 posts and 523,360 comments with reply-tree structure preserved. We provide a human-annotated benchmark of 1,500 instances across argument mining and claim-based stance detection, including inter-annotator agreement, and use it to evaluate large language model (LLM) prompting strategies. Using the best-performing configuration, we release additional LLM-generated labels for 24,352 posts (argument presence) and 107,873 comments (stance to parent), enabling analysis of stance and argumentation patterns across depth, topics, and users. All code and data are released publicly.
Related papers
- TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations [4.101075054019962]
We present TikStance, a multimodal and context-aware dataset comprising 161 videos and 13,876 comments from TikTok.<n>The dataset covers three major political figures in the 2024 U.S. election cycle--Donald Trump, Joe Biden, and Kamala Harris--with content collected between September 2023 and January 2025.<n>Each discussion unit links a host video and its metadata to a parent-linked comment tree, enabling stance analysis within both audiovisual and conversational context.
arXiv Detail & Related papers (2026-07-16T17:37:19Z) - SAD: A Large-Scale Strategic Argumentative Dialogue Dataset [60.33125467375306]
In practice, argumentation is often realized as multi-turn dialogue.<n>We present the first large-scale textbfStrategic textbfArgumentative textbfDialogue dataset, consisting of 392,822 examples.
arXiv Detail & Related papers (2026-01-12T11:11:37Z) - A Decade of News Forum Interactions: Threaded Conversations, Signed Votes, and Topical Tags [3.443622476405787]
We present a large-scale, longitudinal dataset capturing user activity on the online platform of DerStandard, a major Austrian newspaper.<n>The dataset spans ten years (2013-2022) and includes over 75 million user comments, more than 400 million votes, and detailed metadata on articles and user interactions.
arXiv Detail & Related papers (2025-06-27T13:40:20Z) - A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations [112.81207927088117]
PersonaConvBench is a benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs)<n>We benchmark several commercial and open-source LLMs under a unified prompting setup and observe that incorporating personalized history yields substantial performance improvements.
arXiv Detail & Related papers (2025-05-20T09:13:22Z) - A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media [12.479554210753664]
Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics.
Existing benchmarks often come from a single domain or focus on a limited set of topics.
We propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation.
arXiv Detail & Related papers (2024-10-11T15:20:11Z) - TACO -- Twitter Arguments from COnversations [3.887532996081327]
Argument mining aims to identify the structural elements of arguments, denoted as information and inference.
We contribute TACO, the first dataset of Twitter Arguments utilizing 1,814 tweets covering 200 entire conversations.
Our data reveals that Twitter users tend to engage in discussions involving informed inferences and information.
arXiv Detail & Related papers (2024-03-30T16:14:46Z) - Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews [51.453135368388686]
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM)
Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level.
arXiv Detail & Related papers (2024-03-11T21:51:39Z) - SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents [70.08842857515141]
SpokenWOZ is a large-scale speech-text dataset for spoken TOD.<n>Cross-turn slot and reasoning slot detection are new challenges for SpokenWOZ.
arXiv Detail & Related papers (2023-05-22T13:47:51Z) - OpenAssistant Conversations -- Democratizing Large Language Model
Alignment [37.617080996433586]
We release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus.
The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
arXiv Detail & Related papers (2023-04-14T18:01:29Z) - ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive
Summarization with Argument Mining [61.82562838486632]
We crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads.
We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data.
arXiv Detail & Related papers (2021-06-01T22:17:13Z) - Weakly-Supervised Aspect-Based Sentiment Analysis via Joint
Aspect-Sentiment Topic Embedding [71.2260967797055]
We propose a weakly-supervised approach for aspect-based sentiment analysis.
We learn sentiment, aspect> joint topic embeddings in the word embedding space.
We then use neural models to generalize the word-level discriminative information.
arXiv Detail & Related papers (2020-10-13T21:33:24Z) - WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection [0.0]
We propose an original framework, based on the Wikipedia Comment corpus, with comment-level annotations of different types.
This large corpus of more than 380k annotated messages opens perspectives for online abuse detection and especially for context-based approaches.
We also propose, in addition to this corpus, a complete benchmarking platform to stimulate and fairly compare scientific works around the problem of content abuse detection.
arXiv Detail & Related papers (2020-03-13T10:26:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.