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

TL;DR

This paper introduces an iterative query selection method to retrieve and classify fake news from opaque search engines, resulting in a large, publicly available dataset that advances fake news detection research.

Contribution

The paper presents a novel iterative query selection algorithm (IQS) that improves search results from black-box engines and enables large-scale fake news data collection.

Findings

IQS outperforms state-of-the-art retrieval methods.

The collected dataset contains 70K news items with 22M accounts.

The dataset improves fake news detection accuracy.

Abstract

Retrieving information from an online search engine, is the first and most important step in many data mining tasks. Most of the search engines currently available on the web, including all social media platforms, are black-boxes (a.k.a opaque) supporting short keyword queries. In these settings, retrieving all posts and comments discussing a particular news item automatically and at large scales is a challenging task. In this paper, we propose a method for generating short keyword queries given a prototype document. The proposed iterative query selection algorithm (IQS) interacts with the opaque search engine to iteratively improve the query. It is evaluated on the Twitter TREC Microblog 2012 and TREC-COVID 2019 datasets showing superior performance compared to state-of-the-art. IQS is applied to automatically collect a large-scale fake news dataset of about 70K true and fake news items. The dataset, publicly available for research, includes more than 22M accounts and 61M tweets in Twitter approved format. We demonstrate the usefulness of the dataset for fake news detection task achieving state-of-the-art performance.