Kellin Pelrine

$\texttt{BluePrint}$: A Social Media User Dataset for LLM Persona Evaluation and Training

Aur'elien Buck-Kaeffer

Je Qin Chooi

Dan Zhao

Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethi… (voir plus)cally or logistically challenging with human subjects. However, the field lacks standardized data resources for fine-tuning and evaluating LLMs as realistic social media agents. We address this gap by introducing SIMPACT, the SIMulation-oriented Persona and Action Capture Toolkit, a privacy respecting framework for constructing behaviorally-grounded social media datasets suitable for training agent models. We formulate next-action prediction as a task for training and evaluating LLM-based agents and introduce metrics at both the cluster and population levels to assess behavioral fidelity and stylistic realism. As a concrete implementation, we release BluePrint, a large-scale dataset built from public Bluesky data focused on political discourse. BluePrint clusters anonymized users into personas of aggregated behaviours, capturing authentic engagement patterns while safeguarding privacy through pseudonymization and removal of personally identifiable information. The dataset includes a sizable action set of 12 social media interaction types (likes, replies, reposts, etc.), each instance tied to the posting activity preceding it. This supports the development of agents that use context-dependence, not only in the language, but also in the interaction behaviours of social media to model social media users. By standardizing data and evaluation protocols, SIMPACT provides a foundation for advancing rigorous, ethically responsible social media simulations. BluePrint serves as both an evaluation benchmark for political discourse modeling and a template for building domain specific datasets to study challenges such as misinformation and polarization.

2025-09-27

ArXiv (prépublication)

$\texttt{BluePrint}$: A Social Media User Dataset for LLM Persona Evaluation and Training

Aur'elien Buck-Kaeffer

Je Qin Chooi

Dan Zhao

Large language models (LLMs) offer promising capabilities for simulating social media dynamics at scale, enabling studies that would be ethi… (voir plus)cally or logistically challenging with human subjects. However, the field lacks standardized data resources for fine-tuning and evaluating LLMs as realistic social media agents. We address this gap by introducing SIMPACT, the SIMulation-oriented Persona and Action Capture Toolkit, a privacy respecting framework for constructing behaviorally-grounded social media datasets suitable for training agent models. We formulate next-action prediction as a task for training and evaluating LLM-based agents and introduce metrics at both the cluster and population levels to assess behavioral fidelity and stylistic realism. As a concrete implementation, we release BluePrint, a large-scale dataset built from public Bluesky data focused on political discourse. BluePrint clusters anonymized users into personas of aggregated behaviours, capturing authentic engagement patterns while safeguarding privacy through pseudonymization and removal of personally identifiable information. The dataset includes a sizable action set of 12 social media interaction types (likes, replies, reposts, etc.), each instance tied to the posting activity preceding it. This supports the development of agents that use context-dependence, not only in the language, but also in the interaction behaviours of social media to model social media users. By standardizing data and evaluation protocols, SIMPACT provides a foundation for advancing rigorous, ethically responsible social media simulations. BluePrint serves as both an evaluation benchmark for political discourse modeling and a template for building domain specific datasets to study challenges such as misinformation and polarization.

2025-09-27

ArXiv (prépublication)

CrediBench: Building Web-Scale Network Datasets for Information Integrity

James Zhou

Michael M. Bronstein

Shenyang Huang

Online misinformation poses an escalating threat, amplified by the Internet's open nature and increasingly capable LLMs that generate persua… (voir plus)sive yet deceptive content. Existing misinformation detection methods typically focus on either textual content or network structure in isolation, failing to leverage the rich, dynamic interplay between website content and hyperlink relationships that characterizes real-world misinformation ecosystems. We introduce CrediBench: a large-scale data processing pipeline for constructing temporal web graphs that jointly model textual content and hyperlink structure for misinformation detection. Unlike prior work, our approach captures the dynamic evolution of general misinformation domains, including changes in both content and inter-site references over time. Our processed one-month snapshot extracted from the Common Crawl archive in December 2024 contains 45 million nodes and 1 billion edges, representing the largest web graph dataset made publicly available for misinformation research to date. From our experiments on this graph snapshot, we demonstrate the strength of both structural and webpage content signals for learning credibility scores, which measure source reliability. The pipeline and experimentation code are all available here, and the dataset is in this folder.

2025-09-22

NeurIPS.cc/2025/Workshop/NPGML (poster)

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

Sneheel Sarangi

Gayatri Krishnakumar

Busra Tugce Gurbuz

Austin Welch

Andreea Musulan

Hao Yu

Ethan Kosak-Hine

Tom Gibbs

Dan Zhao

2025-08-16

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)

SandboxSocial: A Sandbox for Social Media Using Multimodal AI Agents

Sneheel Sarangi

Gayatri K

Busra Tugce Gurbuz

Austin Welch

Andreea Musulan

Hao Yu

Ethan Kosak-Hine

Tom Gibbs

Dan Zhao

The online information ecosystem enables influence campaigns of unprecedented scale and impact. We urgently need empirically grounded approa… (voir plus)ches to counter the growing threat of malicious campaigns, now amplified by generative AI. But, developing defenses in real-world settings is impractical. Social system simulations with agents modelled using Large Language Models (LLMs) are a promising alternative approach and a growing area of research. However, existing simulators lack features needed to capture the complex information-sharing dynamics of platform-based social networks. To bridge this gap, we present SandboxSocial, a new simulator that includes several key innovations, mainly: (1) a virtual social media platform (modelled as Mastodon and mirrored in an actual Mastodon server) that enables a realistic setting in which agents interact; (2) an adapter that uses real-world user data to create more grounded agents and social media content; and (3) multi-modal capabilities that enable our agents to interact using both text and images---just as humans do on social media. We make the simulator more useful to researchers by providing measurement and analysis tools that track simulation dynamics and compute evaluation metrics to compare experimental results.

2025-08-16

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)

Veracity: An Open-Source AI Fact-Checking System.

William Garneau

Manon Gruaz

Mike Pinder

Li Wei Wang

Sukanya Krishna

Luda Cohen

2025-08-16

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)

Veracity: An Open-Source AI Fact-Checking System

William Garneau

Manon Gruaz

Mike Pinder

Li Wei Wang

Sukanya Krishna

Luda Cohen

The proliferation of misinformation poses a significant threat to society, exacerbated by the capabilities of generative AI. This demo paper… (voir plus) introduces Veracity, an open-source AI system designed to empower individuals to combat misinformation through transparent and accessible fact-checking. Veracity leverages the synergy between Large Language Models (LLMs) and web retrieval agents to analyze user-submitted claims and provide grounded veracity assessments with intuitive explanations. Key features include multilingual support, numerical scoring of claim veracity, and an interactive interface inspired by familiar messaging applications. This paper will showcase Veracity's ability to not only detect misinformation but also explain its reasoning, fostering media literacy and promoting a more informed society.

2025-08-16

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (publié)

A Guide to Misinformation Detection Data and Evaluation

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

James Zhou

Florence Laflamme

Luke Yuxiang Guan

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

A Guide to Misinformation Detection Data and Evaluation

Jacob-Junqi Tian

Gabrielle Péloquin-Skulski

James Zhou

Florence Laflamme

Yuxiang Guan

Misinformation is a complex societal issue, and mitigating solutions are difficult to create due to data deficiencies. To address this probl… (voir plus)em, we have curated the largest collection of (mis)information datasets in the literature, totaling 75. From these, we evaluated the quality of all of the 36 datasets that consist of statements or claims, as well as the 9 datasets that consists of data in purely paragraph form. We assess these datasets to identify those with solid foundations for empirical work and those with flaws that could result in misleading and non-generalizable results, such as insufficient label quality, spurious correlations. We further provide state-of-the-art baselines on all these datasets, but show that regardless of label quality, categorical labels may no longer give an accurate evaluation of detection model performance. We discuss alternatives to mitigate this problem. Overall, this guide aims to provide a roadmap for obtaining higher quality data and conducting more effective evaluations, ultimately improving research in misinformation detection. All datasets and other artifacts are available at [anonymized].

2025-08-03

Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (publié)

TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection

Hao Yu

Shenyang Huang

2025-07-25

colmweb.org/COLM/2025/Workshop/SoLaR (poster)

TRUTH: Teaching LLMs to Rerank for Truth in Misinformation Detection

Hao Yu

Shenyang Huang

Misinformation detection presents a significant challenge due to its knowledge-intensive and reasoning-intensive nature. While Retrieval-Aug… (voir plus)mented Generation (RAG) systems offer a promising direction, the effectiveness of their retrieval and reranking components is crucial. This paper introduces TRUTH, a novel reranking approach designed for domain adaptation, specifically for misinformation detection, which employs a two-stage training methodology: Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). We demonstrate that our 1B parameter TRUTH model achieves strong performance comparable to 7B models on established misinformation benchmarks such as FEVER and Canadian bilingual news datasets, improving retrieval quality and positively impacting downstream task accuracy. Our findings highlight the efficacy of combining SFT for broad knowledge acquisition and domain adaptation with DPO for nuanced reasoning alignment in developing efficient and effective rerankers for complex, knowledge-intensive tasks. Datasets and code will be available with the camera-ready version of the paper.

2025-07-25

colmweb.org/COLM/2025/Workshop/SoLaR (poster)