A Unified Training Process for Fake News Detection Based on Finetuned Bidirectional Encoder Representation from Transformers Model

Tida, Vijay Srinivas; Hsu, Sonya; Hei, Xiali

doi:10.1089/big.2022.0050

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…The majority of recent work in disinformation analysis has been conducted with Computational Linguistic methods (Ruffo, Semeraro, Giachanou, & Rosso, 2023). Some recent (and straightforward) approaches put together an NLP pipeline (preprocessing, feature extraction, model building) to exploit traditional text analysis techniques (Asaad & Erascu, 2018;Koloski, Pollak, & Škrlj, 2020) or to train neural networks (Reddy, Suman, Saha, & Bhattacharyya, 2020;Umer et al, 2020;Eldesoky & Moussa, 2021;Qazi, Khan, & Ali, 2020;Tida, Hsu, & Hei, 2022;Dun, Tu, Chen, Hou, & Yuan, 2021) for fake news classification.…”

Section: Natural Language Processing For Stylistic Characterizationmentioning

confidence: 99%

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

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This project has received funding from the European Commission under the call CEF-TC-2020-2 (European Digital Media Observatory)Reference: 2020-EU-IA-0252 Consortium DisclaimerAll information provided reflects the status of the IBERIFIER project at the time of writing and may be subject to change.Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium warrant that the information contained in this document is capable of use, nor that the use of such information is free from risk. Neither the IBERIFIER Consortium as a whole, nor any single party within the IBERIFIER Consortium accepts any liability for loss or damage suffered by any person using the information.This document does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of its content.

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Section: Natural Language Processing For Stylistic Characterizationmentioning

confidence: 99%

IBERIFIER Reports: Is the ‘AI toolbox for disinformation’ ready?

Montoro Montarroso,

Camacho,

Martín

et al. 2023

View full text Add to dashboard Cite

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“…The question characteristic extraction module primarily employs language encoding models such as LSTM (Long Short-Term Memory) [12], GRU (Gate Recurrent Unit) [13], Transformer [14], and BERT (Bidirectional Encoder Representation from Transformers) [15] to extract question characteristics.…”

Section: Related Workmentioning

confidence: 99%

Image to English translation and comprehension: INT2-VQA method based on inter-modality and intra-modality collaborations

Sheng

2023

PLoS ONE

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Existing visual question answering methods typically concentrate only on visual targets in images, ignoring the key textual content in the images, thereby limiting the depth and accuracy of image content comprehension. Inspired by this, we pay attention to the task of text-based visual question answering, address the performance bottleneck issue caused by over-fitting risk in existing self-attention-based models, and propose a scenario text visual question answering method called INT2-VQA that fuses knowledge manifestation based on inter-modality and intra-modality collaborations. Specifically, we model the complementary priori knowledge of locational collaboration between visual targets and textual targets across modalities and the contextual semantical collaboration among textual word targets within a modality. Based on this, a universal knowledge-reinforced attention module is designed to achieve a unified encoding manifestation of both relations. Extensive ablation experiments, contrast experiments, and visual analyses demonstrate the effectiveness of the proposed method and prove its superiority over the other state-of-the-art methods.

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