Detection of Fake News on COVID-19 on Web Search Engines

Abstract

In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which is misleading or even harmful, which has widely spread around the globe. Although social media are increasingly being used as the information source, web search engines, such as Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through search engines. By using a real-world dataset associated with recent COVID-19 pandemic, we first apply re-sampling techniques for class imbalance, and then we use existing machine learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated uniform resource locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URL detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URL features can improve the effectiveness of fake news detection methods.

A Framework of AI-Based Approaches to Improving eHealth Literacy and Combating Infodemic

“…Recently, the topic of detecting fake news about COVID-19 on the Web has also received intense interest, partly due to the fact that many people still prefer to use search engines to find needed information on the Web ( 82 ). During the COVID-19 pandemic, people have many questions about the origin of the disease, transmission patterns, prevention measures, treatment options, and cure possibilities, and information sources related to these questions are distributed across the whole Web.…”

“…During the COVID-19 pandemic, people have many questions about the origin of the disease, transmission patterns, prevention measures, treatment options, and cure possibilities, and information sources related to these questions are distributed across the whole Web. Despite that popular search engines such as Google and Bing already applied various mechanisms to filter and rank Web pages and documents before they present the results to end users, there are still many Web misinformation and disinformation sources ( 82 ). Mazzeo et al ( 82 ) proposed a new direction for Web fake news classification by the integration of the most commonly used features in fake news detection and features that play an important role in malicious URL detection.…”

“…Despite that popular search engines such as Google and Bing already applied various mechanisms to filter and rank Web pages and documents before they present the results to end users, there are still many Web misinformation and disinformation sources ( 82 ). Mazzeo et al ( 82 ) proposed a new direction for Web fake news classification by the integration of the most commonly used features in fake news detection and features that play an important role in malicious URL detection. The authors argued that this feature engineering aimed to feed the original data and provided new and meaningful feature representations to improve AI algorithms for classification.…”

COVID-19 Rumor Detection on Social Networks Based on Content Information and User Response

“…Since information is easier to spread on social networks, rumor detection on social networks is more complex than general fake news detection. For detecting fake news, text features, source URL, and source website credit can be considered [2]. The source of information is more complex on the social network, and information spreading is much faster and wider.…”

“…Rumor detection methods based on content information focuses on language style, emotional polarity, text and picture content features [1]. Rumor detection methods based on rumor source focuses on web address (e.g., the source URLs of rumors), website credit, and webpage metadata [2]. Rumor detection methods based on propagation focus on the propagation structural features during rumor propagation, such as the retweeting and commenting behavior by social platform users [3].…”