Mitigation of Diachronic Bias in Fake News Detection Dataset

Murayama, Taichi; Wakamiya, Shoko; Aramaki, Eiji

doi:10.48550/arxiv.2108.12601

Cited by 1 publication

(1 citation statement)

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“…For example, a model learned from a dataset in 2017 is difficult to correctly classify articles including "Donald Trump" or "Joe Biden" in 2021 because the model is not aware of the change in presidents. As a solution to this problem, a method of replacing the proper nouns in the dataset with information from Wikidata is proposed to make the model more robust [147]. Also, NELA-GT [81,82,158] updates its contents every year.…”

Section: Velocitymentioning

confidence: 99%

Dataset of Fake News Detection and Fact Verification: A Survey

Murayama¹

2021

Preprint

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The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news and satire detection. We also describe in detail their utilization tasks and their characteristics. Finally, we highlight the challenges in the fake news dataset construction and some research opportunities that addresses these challenges. Our survey facilitates fake news research by helping researchers find suitable datasets without reinventing the wheel, and thereby, improves fake news studies in depth.

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Section: Velocitymentioning

confidence: 99%