In recent years, misinformation on the Web has become increasingly rampant.
The research community has responded by proposing systems and challenges, which
are beginning to be useful for (various subtasks of) detecting misinformation.
However, most proposed systems are based on deep learning techniques which are
fine-tuned to specific domains, are difficult to interpret and produce results
which are not machine readable. This limits their applicability and adoption as
they can only be used by a select expert audience in very specific settings. In
this paper we propose an architecture based on a core concept of Credibility
Reviews (CRs) that can be used to build networks of distributed bots that
collaborate for misinformation detection. The CRs serve as building blocks to
compose graphs of (i) web content, (ii) existing credibility signals
--fact-checked claims and reputation reviews of websites--, and (iii)
automatically computed reviews. We implement this architecture on top of
lightweight extensions to Schema.org and services providing generic NLP tasks
for semantic similarity and stance detection. Evaluations on existing datasets
of social-media posts, fake news and political speeches demonstrates several
advantages over existing systems: extensibility, domain-independence,
composability, explainability and transparency via provenance. Furthermore, we
obtain competitive results without requiring finetuning and establish a new
state of the art on the Clef'18 CheckThat! Factuality task.

本文提出了一种基于可信度评估的体系架构，用于建立分布式机器人网络协同实现误传信息检测，并且通过使用 Schema.org 扩展和 NLP 技术实现语义相似性和态度检测，有效地提高了可伸缩性、领域独立性、可组合性、可解释性和透明度，同时还在 Clef'18 CheckThat! Factuality 任务上取得了新的明显的效果。