期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 11, 期 1, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3363818
关键词
Fake news; biased news; misleading news; fake news detection; misinformation; disinformation; concept drift; robust machine learning; adversarial machine learning
In this study, we examine the impact of time on state-of-the-art news veracity classifiers. We show that, as time progresses, classification performance for both unreliable and hyper-partisan news classification slowly degrade. While this degradation does happen, it happens slower than expected, illustrating that hand-crafted, content-based features, such as style of writing, are fairly robust to changes in the news cycle. We show that this small degradation can bemitigated using online learning. Last, we examine the impact of adversarial content manipulation by malicious news producers. Specifically, we test three types of attack based on changes in the input space and data availability. We show that static models are susceptible to content manipulation attacks, but online models can recover from such attacks.
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