All your fake detector are belong to us: evaluating adversarial robustness of fake-news detectors under black-box settings

H Ali, MS Khan, A AlGhadhban, M Alazmi… - IEEE …, 2021 - ieeexplore.ieee.org
With the hyperconnectivity and ubiquity of the Internet, the fake news problem now presents
a greater threat than ever before. One promising solution for countering this threat is to …

[PDF][PDF] Are we safe yet? the limitations of distributional features for fake news detection

T Schuster, R Schuster, DJ Shah… - arXiv preprint arXiv …, 2019 - researchgate.net
Automatic detection of fake news—texts that are deceitful and misleading—is a long
outstanding and largely unsolved problem. Worse yet, recent developments in language …

Malcom: Generating malicious comments to attack neural fake news detection models

T Le, S Wang, D Lee - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
In recent years, the proliferation of so-called “fake news” has caused much disruptions in
society and weakened the news ecosystem. Therefore, to mitigate such problems …

Explainable tsetlin machine framework for fake news detection with credibility score assessment

B Bhattarai, OC Granmo, L Jiao - arXiv preprint arXiv:2105.09114, 2021 - arxiv.org
The proliferation of fake news, ie, news intentionally spread for misinformation, poses a
threat to individuals and society. Despite various fact-checking websites such as PolitiFact …

Adversarial training for fake news classification

A Tariq, A Mehmood, M Elhadef, MUG Khan - IEEE Access, 2022 - ieeexplore.ieee.org
News is a source of information to know about progress in the various areas of life all across
the globe. However, the volume of this information is high, and getting benefits from the …

A transformer-based approach to multilingual fake news detection in low-resource languages

A De, D Bandyopadhyay, B Gain, A Ekbal - Transactions on Asian and …, 2021 - dl.acm.org
Fake news classification is one of the most interesting problems that has attracted huge
attention to the researchers of artificial intelligence, natural language processing, and …

Context-aware misinformation detection: A benchmark of deep learning architectures using word embeddings

VI Ilie, CO Truică, ES Apostol, A Paschke - IEEE Access, 2021 - ieeexplore.ieee.org
New mass media paradigms for information distribution have emerged with the digital age.
With new digital-enabled mass media, the communication process is centered around the …

Dafd: Domain adaptation framework for fake news detection

Y Huang, M Gao, J Wang, K Shu - … , Sanur, Bali, Indonesia, December 8–12 …, 2021 - Springer
Nowadays, social media has become the leading platform for news dissemination and
consumption. Due to the convenience of social media platforms, fake news spread at an …

Fake news detectors are biased against texts generated by large language models

J Su, TY Zhuo, J Mansurov, D Wang… - arXiv preprint arXiv …, 2023 - arxiv.org
The spread of fake news has emerged as a critical challenge, undermining trust and posing
threats to society. In the era of Large Language Models (LLMs), the capability to generate …

Fake news detection via NLP is vulnerable to adversarial attacks

Z Zhou, H Guan, MM Bhat, J Hsu - arXiv preprint arXiv:1901.09657, 2019 - arxiv.org
News plays a significant role in shaping people's beliefs and opinions. Fake news has
always been a problem, which wasn't exposed to the mass public until the past election …