  In the last decade, an increasing number of users have started reporting
Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.
Given the large volume of reports, pharmacovigilance has focused on ways to use
Natural Language Processing (NLP) techniques to rapidly examine these large
collections of text, detecting mentions of drug-related adverse reactions to
trigger medical investigations. However, despite the growing interest in the
task and the advances in NLP, the robustness of these models in face of
linguistic phenomena such as negations and speculations is an open research
question. Negations and speculations are pervasive phenomena in natural
language, and can severely hamper the ability of an automated system to
discriminate between factual and nonfactual statements in text. In this paper
we take into consideration four state-of-the-art systems for ADE detection on
social media texts. We introduce SNAX, a benchmark to test their performance
against samples containing negated and speculated ADEs, showing their fragility
against these phenomena. We then introduce two possible strategies to increase
the robustness of these models, showing that both of them bring significant
increases in performance, lowering the number of spurious entities predicted by
the models by 60% for negation and 80% for speculations.
