Copertina

Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Donabauer, Gregor, Kruschwitz, Udo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Reproducibility Study of Graph-Based Legal Case Retrieval
di: Donabauer, Gregor, et al.
Pubblicazione: (2025)

Token-Level Graphs for Short Text Classification
di: Donabauer, Gregor, et al.
Pubblicazione: (2024)

Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
di: Her, Wan-Hua, et al.
Pubblicazione: (2024)

Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks
di: Ateia, Samy, et al.
Pubblicazione: (2024)

Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
di: Fehle, Jakob, et al.
Pubblicazione: (2026)

Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025
di: Ateia, Samy, et al.
Pubblicazione: (2025)

Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach
di: Guidotti, Sabrina, et al.
Pubblicazione: (2024)

BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
di: Ateia, Samy, et al.
Pubblicazione: (2024)

LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction
di: Hellwig, Nils Constantin, et al.
Pubblicazione: (2026)

AnnoABSA: A Web-Based Annotation Tool for Aspect-Based Sentiment Analysis with Retrieval-Augmented Suggestions
di: Hellwig, Nils Constantin, et al.
Pubblicazione: (2026)

nchellwig at SemEval-2026 Task 3: Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis using Large Language Models
di: Hellwig, Nils Constantin, et al.
Pubblicazione: (2026)

Advanced Text Analytics -- Graph Neural Network for Fake News Detection in Social Media
di: Patel, Anantram, et al.
Pubblicazione: (2025)

Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development
di: Ognibene, Dimitri, et al.
Pubblicazione: (2025)

Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
di: Hellwig, Nils Constantin, et al.
Pubblicazione: (2026)

Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction
di: Hellwig, Nils Constantin, et al.
Pubblicazione: (2025)

Context-Based Fake News Detection using Graph Based Approach: ACOVID-19 Use-case
di: Muniyappa, Chandrashekar, et al.
Pubblicazione: (2025)

Many Ways to Be Fake: Benchmarking Fake News Detection Under Strategy-Driven AI Generation
di: Wang, Xinyu, et al.
Pubblicazione: (2026)

Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
di: Donhauser, Niklas, et al.
Pubblicazione: (2026)

Neighborhood-Order Learning Graph Attention Network for Fake News Detection
di: Lakzaei, Batool, et al.
Pubblicazione: (2025)

Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
di: Wang, Zhengjia, et al.
Pubblicazione: (2025)

Experiments in News Bias Detection with Pre-Trained Neural Transformers
di: Menzner, Tim, et al.
Pubblicazione: (2024)

Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection
di: Yin, Junwei, et al.
Pubblicazione: (2024)

From Scarcity to Capability: Empowering Fake News Detection in Low-Resource Languages with LLMs
di: Shibu, Hrithik Majumdar, et al.
Pubblicazione: (2025)

Fake News Detection: It's All in the Data!
di: Kuntur, Soveatin, et al.
Pubblicazione: (2024)

FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
di: Raza, Shaina, et al.
Pubblicazione: (2024)

FakeGPT: Fake News Generation, Explanation and Detection of Large Language Models
di: Huang, Yue, et al.
Pubblicazione: (2023)

Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions
di: Yi, Jingyuan, et al.
Pubblicazione: (2025)

Seek and You Shall Find: Design & Evaluation of a Context-Aware Interactive Search Companion
di: Bink, Markus, et al.
Pubblicazione: (2026)

Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines
di: Koyuturk, Cansu, et al.
Pubblicazione: (2025)

MVAN: Multi-View Attention Networks for Fake News Detection on Social Media
di: Ni, Shiwen, et al.
Pubblicazione: (2025)

Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks
di: Wu, Jiaying, et al.
Pubblicazione: (2023)

Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI
di: Yigezu, Mesay Gemeda, et al.
Pubblicazione: (2024)

From Veracity to Diffusion: Adressing Operational Challenges in Moving From Fake-News Detection to Information Disorders
di: Savatteri, Francesco Paolo, et al.
Pubblicazione: (2025)

A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
di: Wang, Bo, et al.
Pubblicazione: (2026)

Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection
di: Guo, Hao, et al.
Pubblicazione: (2024)

Trust Oriented Explainable AI for Fake News Detection
di: Siwek, Krzysztof, et al.
Pubblicazione: (2026)

Bangla Fake News Detection Based On Multichannel Combined CNN-LSTM
di: George, Md. Zahin Hossain, et al.
Pubblicazione: (2025)

On the Limits of Model Merging for Multilinguality in Pre-Training
di: Aycock, Seth, et al.
Pubblicazione: (2026)

Long Context Pre-Training with Lighthouse Attention
di: Peng, Bowen, et al.
Pubblicazione: (2026)

Heterogeneous Subgraph Transformer for Fake News Detection
di: Zhang, Yuchen, et al.
Pubblicazione: (2024)