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Fake news detection models are critical to countering disinformation but canbe manipulated through adversarial attacks. In this position paper, we analyzehow an attacker can compromise the performance of an online learning detectoron specific news content without being able to manipulate the original targetnews. In some contexts, such as social networks, where the attacker cannotexert complete control over all the information, this scenario can indeed bequite plausible. Therefore, we show how an attacker could potentially introducepoisoning data into the training data to manipulate the behavior of an onlinelearning method. Our initial findings reveal varying susceptibility of logisticregression models based on complexity and attack type.

Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It