mlsanityData to 7 Oct 2026

Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection

arXiv 2306.14728 ↗First posted 26 Jun 2023cs.CL cs.AI cs.SIcited by 20

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Abstract (arXiv)

Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing on future data. In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data. Specifically, we design an effective framework FTT (Forecasting Temporal Trends), which could forecast the temporal distribution patterns of news data and then guide the detector to fast adapt to future distribution. Experiments on the real-world temporally split dataset demonstrate the superiority of our proposed framework. The code is available at https://github.com/ICTMCG/FTT-ACL23.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 26 Jun 2023.

Builds on

26 of 42 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
  5. 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 762
  6. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  7. 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 281
  8. 2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
  9. 2020FANG: Leveraging Social Context for Fake News Detection Using Graph Representationcited by 98
  10. 2020That is a Known Lie: Detecting Previously Fact-Checked Claimscited by 82
  11. 2022MDFEND: Multi-domain Fake News Detectioncited by 68
  12. 2018All-in-one: Multi-task Learning for Rumour Verificationcited by 54
14 more
  1. 2021Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Datacited by 50
  2. 2019Exploiting Multi-domain Visual Information for Fake News Detectioncited by 48
  3. 2022Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learningcited by 41
  4. 2022Generalizing to the Future: Mitigating Entity Bias in Fake News Detectioncited by 38
  5. 2022Zoom Out and Observe: News Environment Perception for Fake News Detectioncited by 34
  6. 2019Network-based Fake News Detection: A Pattern-driven Approachcited by 33
  7. 2022Memory-Guided Multi-View Multi-Domain Fake News Detectioncited by 31
  8. 2019Learning Hierarchical Discourse-level Structure for Fake News Detectioncited by 28
  9. 2022Domain Adaptive Fake News Detection via Reinforcement Learningcited by 27
  10. 2021Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learningcited by 25
  11. 2021FakeFlow: Fake News Detection by Modeling the Flow of Affective Informationcited by 20
  12. 2022Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfercited by 19
  13. 2023It's about Time: Rethinking Evaluation on Rumor Detection Benchmarks using Chronological Splitscited by 15
  14. 2023Combating Online Misinformation Videos: Characterization, Detection, and Future Directionscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 5; 2025: 8; 2026: 1.

20 papers.

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  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 173
  2. 2023Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detectioncited by 69
  3. 2023Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attackscited by 69
  4. 2024Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Modelscited by 16
  5. 2024ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Taskscited by 11
  6. 2023Exploiting User Comments for Early Detection of Fake News Prior to Users' Commentingcited by 10
  7. 2023Exploring news intent and its application: A theory-driven approachcited by 7
  8. 2024Analyzing Semantic Change through Lexical Replacementscited by 7
  9. 2024LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detectioncited by 4
  10. 2025T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Searchcited by 4
  11. 2024Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Modelscited by 3
  12. 2025Robust Misinformation Detection by Visiting Potential Commonsense Conflictcited by 3
8 more
  1. 2025Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inferencecited by 3
  2. 2023Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasetscited by 2
  3. 2025Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detectioncited by 2
  4. 2025Towards Robust and Realible Multimodal Misinformation Recognition with Incomplete Modalitycited by 2
  5. 2025E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detectioncited by 1
  6. 2025Unveiling Dual Quality in Product Reviews: An NLP-Based Approachcited by 0
  7. 2025Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detectioncited by 0
  8. 2026Large language models for spreading dynamics in complex systemscited by 0