  Prior work has extensively studied misinformation related to news, politics,
and health, however, misinformation can also be about technological topics.
While less controversial, such misinformation can severely impact companies'
reputations and revenues, and users' online experiences. Recently, social media
has also been increasingly used as a novel source of knowledgebase for
extracting timely and relevant security threats, which are fed to the threat
intelligence systems for better performance. However, with possible campaigns
spreading false security threats, these systems can become vulnerable to
poisoning attacks. In this work, we proposed novel approaches for detecting
misinformation about cybersecurity and privacy threats on social media,
focusing on two topics with different types of misinformation: phishing
websites and Zoom's security & privacy threats. We developed a framework for
detecting inaccurate phishing claims on Twitter. Using this framework, we could
label about 9% of URLs and 22% of phishing reports as misinformation. We also
proposed another framework for detecting misinformation related to Zoom's
security and privacy threats on multiple platforms. Our classifiers showed
great performance with more than 98% accuracy. Employing these classifiers on
the posts from Facebook, Instagram, Reddit, and Twitter, we found respectively
that about 18%, 3%, 4%, and 3% of posts were misinformation. In addition, we
studied the characteristics of misinformation posts, their authors, and their
timelines, which helped us identify campaigns.
