Do Sentence Interactions Matter? Leveraging Sentence Level Representations for Fake News Classification (D19-53)
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| Challenge: | Existing methods to distinguish between trusted and fake news articles lack feature engineering . et al. (2009) define fake news as the one which deliberately exposes real-world individuals, organisations and events to ridicule. |
| Approach: | They propose a graph neural network-based model which captures sentence interactions within a document. |
| Outcome: | The proposed model beats baselines and achieves state-of-the-art accuracy on existing datasets. |
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Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022.acl-long)
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| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
| Approach: | They propose to use inference operators to analyze social media for fake news spread to uncover unobserved interactions between documents and users' engagement patterns. |
| Outcome: | The proposed algorithms improve the performance of two fake news detection tasks. |
Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (2022.coling-1)
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| Challenge: | Recent advances to neural fake news generators have made it difficult to understand how misinformation generated by these models may best be confronted. |
| Approach: | They conduct feature-based analysis to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most effectively exploit. |
| Outcome: | The proposed models are compared with models trained on subsets of features and confronted with increasingly advanced neural fake news. |
KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection (2023.findings-emnlp)
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| Challenge: | Existing methods of fake news detection focus on news entity information and ignore structured knowledge among news entities. |
| Approach: | They propose a model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs) they identify entities in news content and link them to entities in KGs. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and is competitive in the few-shot scenario. |
Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)
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| Challenge: | Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge. |
| Approach: | They propose a graph neural model which compares news to knowledge base through entities for fake news detection. |
| Outcome: | The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets. |
Adapting Fake News Detection to the Era of Large Language Models (2024.findings-naacl)
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| Challenge: | a gap exists in understanding the interplay between machine-paraphrased real news, machine-generated fake news, and human-written real news . false information is easier to generate but harder to detect due to the bias of detectors against machine-generated texts . |
| Approach: | They propose a strategy to adapt fake news detectors to the era of large language models and AI-driven content creation . |
| Outcome: | The proposed detectors perform well on human-written articles but not vice versa . the proposed detector should be trained on datasets with lower machine-generated news ratio than the test set . |
GREENER: Graph Neural Networks for News Media Profiling (2022.emnlp-main)
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| Challenge: | a new method for profiling news media on the Web addresses the factuality of reporting and bias problem . a recent study has focused on text features but has focused primarily on text . |
| Approach: | They propose a model that models the similarity between media outlets based on their audience overlap . they propose GREENER, which builds a graph of inter-media connections based upon audience overlap. |
| Outcome: | The proposed model improves on state-of-the-art models on two datasets. |
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations (2024.lrec-main)
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| Challenge: | Graph Neural Networks (GNNs) are used to train neural networks to detect fake news based on context-based methods. |
| Approach: | They propose to combine the two by applying pre-training of Graph Neural Networks (GNNs) in the domain of context-based fake news detection. |
| Outcome: | The proposed methods show that transfer learning does not lead to significant improvements over training a model from scratch in the domain of context-based fake news detection. |
An Interactive Framework for Profiling News Media Sources (2024.naacl-long)
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| Challenge: | Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased? |
| Approach: | They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media. |
| Outcome: | The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source . |
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
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| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
| Approach: | They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection. |
| Outcome: | The proposed model is based on linguistic features and will be extended in the future . it will be used to improve the existing model and improve the tools in the field of fake news detection . |
On Fake News Detection with LLM Enhanced Semantics Mining (2024.emnlp-main)
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| Challenge: | Existing methods for detecting fake news use only news embeddings to capture the lexical semantics between tokens. |
| Approach: | They propose a topic-based model with prompts to extract news embeddings from LLMs and a generalized page-rank model to extract local and global semantics. |
| Outcome: | The proposed model shows superior performance on five benchmark datasets over seven baseline methods. |