r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

Nakamura, Kai; Levy, Sharon; Wang, William Yang

doi:10.48550/arxiv.1911.03854

Cited by 23 publications

(42 citation statements)

References 15 publications

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“…A major challenge with many of the approaches mentioned above is the need for more inclusive large-scale public datasets to allow NLP researchers to explore and work with low-resource languages and alternative modalities. There are promising endeavors to curate datasets of misinformation such as the Fakeddit dataset [26], Poynter and COVIDLies [17], as well as as large community modeling efforts such as the FastAI Model Zoo, which could be expanded to cover a wider range of regions and languages. Similarly, efforts to build datasets from a wider range of sources -e.g., via the creation of radio transcription pipelines -could encourage researchers to work with data from alternative modalities.…”

Section: Discussionmentioning

confidence: 99%

Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19

Luccioni¹,

Pham²,

Lam³

et al. 2021

Preprint

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Natural language processing (NLP) plays a significant role in tools for the COVID-19 pandemic response, from detecting misinformation on social media to helping to provide accurate clinical information or summarizing scientific research. However, the approaches developed thus far have not benefited all populations, regions or languages equally. We discuss ways in which current and future NLP approaches can be made more inclusive by covering lowresource languages, including alternative modalities, leveraging out-of-the-box tools and forming meaningful partnerships. We suggest several future directions for researchers interested in maximizing the positive societal impacts of NLP.

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Section: Discussionmentioning

confidence: 99%

Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19

Luccioni¹,

Pham²,

Lam³

et al. 2021

Preprint

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“…analysis is beneficial in contrast to monomodal processing. Our evaluation, conducted on a large-scale multimodal real-world dataset from Reddit [2], shows that multimodal processing strongly improves detection results. This leads us to two conclusions: (i) all modalities can provide useful clues for the detection of fake news and (ii) the proposed multilevel hierarchical information fusion allows to successfully capture information from all modalities.…”

Section: Introductionmentioning

confidence: 93%

“…Table I lists different approaches developed for information disorder detection, including approaches for mis-and disinformation detection, rumor verification, and fake news detection. The related literature can be split into two groups, monomodal approaches [3]- [5] and multimodal approaches [2], [6]- [12].…”

Section: Related Workmentioning

confidence: 99%

Multimodal Detection of Information Disorder from Social Media

Kirchknopf

Slijepčević

Zeppelzauer

2021

2021 International Conference on Content-Based Multimedia Indexing (CBMI)

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Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the problem of multimodal fake news detection on a largescale multimodal dataset. We propose a multimodal network architecture that enables different levels and types of information fusion. In addition to the textual and visual content of a posting, we further leverage secondary information, i.e. user comments and metadata. We fuse information at multiple levels to account for the specific intrinsic structure of the modalities. Our results show that multimodal analysis is highly effective for the task and all modalities contribute positively when fused properly.

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“…Parallel and sequential ensembling techniques have been widely studied and shown to improve performance of models in a variety of tasks [9]- [16]. Some works combine latent embeddings from different input modalities (feature fusion) and train the entire model together in a joint fashion [47]. Boosting algorithms such as Adaptive Boost [12], Gradient Boosting [13], and XG Boost [14] are common sequential ensembling techniques.…”

Section: B Ensemble Techniquesmentioning

confidence: 99%

Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings

Sarawgi¹,

Khincha²,

Zulfikar³

et al. 2021

Preprint

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Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of ML systems in deployment. Sequential and parallel ensemble techniques have shown improved performance of ML systems in multi-modal settings by leveraging the feature sets together. We propose an uncertainty-aware boosting technique for multi-modal ensembling in order to focus on the data points with higher associated uncertainty estimates, rather than the ones with higher loss values. We evaluate this method on healthcare tasks related to Dementia and Parkinson's disease which involve real-world multi-modal speech and text data, wherein our method shows an improved performance. Additional analysis suggests that introducing uncertainty-awareness into the boosted ensembles decreases the overall entropy of the system, making it more robust to heteroscedasticity in the data, as well as better calibrating each of the modalities along with high quality prediction intervals. We open-source our entire codebase at https://github.com/usarawgi911/Uncertainty-aware-boosting.

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r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection

Cited by 23 publications

References 15 publications

Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19

Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19

Multimodal Detection of Information Disorder from Social Media

Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings

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