In pharmacovigilance, post-marketing surveillance is mainly supported by spontaneous reporting systems (SRS) collecting adverse drug events explicitly submitted by physicians alerted by their patients. Nowadays, this activity could leverage on mining opinions and experiences of individuals from social media by monitoring users’ posts citing symptoms, drugs, etc. The most critical problem is the reliability of the information sources. In order to address this challenge, the proposed method tries to cross-relate heterogeneous data sources with correspondingly different levels of trustworthiness. It filters out assertions quoted on social media on the basis of data validated by official information sources. The method adopts the Fuzzy Formal Concept Analysis (Fuzzy FCA) to evaluate the reliability of adverse drug events extracted on Twitter and PubMed. It keeps track of the difference between the co-citation frequencies by calculating a residual threshold $\tau$. The main outcome is that with $\tau$ in the range $[-4,+4]$, 91% of drug and side effect correlations extracted from tweets can be considered reliable, according to the official site (we used http://sideeffects.embl.de).

在药物警戒中，上市后监督主要由自发报告系统（SRS）支持，该系统收集由患者警惕的医师明确提交的不良药物事件。如今，此活动可以通过监视用户通过引用症状，毒品等信息发布的帖子，从社交媒体上挖掘个人的观点和经验。最关键的问题是信息源的可靠性。为了解决这一挑战，所提出的方法试图将异构数据源与相应的不同级别的可信度进行交叉关联。它根据官方信息来源验证的数据过滤掉社交媒体上引用的断言。该方法采用模糊形式概念分析（Fuzzy FCA）来评估在Twitter和PubMed上提取的不良药物事件的可靠性。它通过计算a来跟踪共引频率之间的差异。剩余阈值 $\tau$。主要结果是$\tau$ 范围中 $[-4，+4]$根据官方网站（我们使用http://sideeffects.embl.de），从推文中提取的91％的药物和副作用之间的相关性可以认为是可靠的。