This study investigates the task of clickbait detection in social media posts. These posts often employ deceptive headlines or thumbnails to mislead readers into clicking on irrelevant or undesirable content, thus enabling widespread dissemination and generating commercial benefits such as increased clicks. To evade detection, malicious creators frequently disguise clickbait posts as legitimate ones, using techniques such as adding irrelevant or misleading content to deceive the detector. Detecting such posts requires a detailed analysis and complex multi-step reasoning using commonsense knowledge to identify inconsistencies. However, existing methods typically treat a post as a simple text span and feed it into a neural network for classification, neglecting the analysis of inherent false details, which leads to misjudgments. Moreover, these black-box models lack explainability. To address this issue, a new question-guided detector is proposed, which systematically analyzes the details through a doubt-then-verify approach to uncover potential inconsistencies and falsehoods. Specifically, a multi-modal retrieval-augmented technique is used to extract detailed clues from the content of the post, followed by questioning each clue. To ensure thorough verification of facts and their complex relationships, both simple matching questions and deep commonsense reasoning questions with varying levels of complexity are employed. Each question yields a plausible answer from the post, but the answer may be fabricated or inaccurate. Therefore, an open-domain QA model is utilized for cross-verification, leveraging external knowledge to derive a more reliable answer. When discrepancies are found between answers, the post is likely to contain false content. This inconsistency serves as a valuable feature and can be combined with other multi-modal features indicative of clickbait, improving the discriminative power of the detection model. By breaking down the complex clickbait detection task into a series of question-guided verification steps, inconspicuous inconsistencies can be identified to explain the underlying reasons for clickbait. Extensive experiments on three popular datasets demonstrate the effectiveness of the proposed approach.