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Retrieving information from an online search engine, is the first and mostimportant step in many data mining tasks. Most of the search engines currentlyavailable on the web, including all social media platforms, are black-boxes(a.k.a opaque) supporting short keyword queries. In these settings, retrievingall posts and comments discussing a particular news item automatically and atlarge scales is a challenging task. In this paper, we propose a method forgenerating short keyword queries given a prototype document. The proposediterative query selection algorithm (IQS) interacts with the opaque searchengine to iteratively improve the query. It is evaluated on the Twitter TRECMicroblog 2012 and TREC-COVID 2019 datasets showing superior performancecompared to state-of-the-art. IQS is applied to automatically collect alarge-scale fake news dataset of about 70K true and fake news items. Thedataset, publicly available for research, includes more than 22M accounts and61M tweets in Twitter approved format. We demonstrate the usefulness of thedataset for fake news detection task achieving state-of-the-art performance.

Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback