The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations, has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most fake news detection methods depend on textual analysis of entire articles. However, these methods face challenges including large training data requirements, sensitivity to topic changes, and the difficulty of encoding lengthy articles. This paper investigates whether effective fake news detection is feasible with limited textual information, focusing only on article source domains rather than full content.
@article{cao2025isless,
title = {Is Less Really More? Fake News Detection with Limited Information},
author = {Zhaoyang Cao and John Nguyen and Reza Zafarani},
year = {2025},
keywords = {journal},
journal = {ACM SIGKDD Explorations Newsletter},
volume = {27},
number = {1},
pages = {20--31},
abstract = {The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations, has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most fake news detection methods depend on textual analysis of entire articles. However, these methods face challenges including large training data requirements, sensitivity to topic changes, and the difficulty of encoding lengthy articles. This paper investigates whether effective fake news detection is feasible with limited textual information, focusing only on article source domains rather than full content.},
url = {https://doi.org/10.1145/3748239.3748243},
}