Event Details

Detecting Misleading Information on COVID-19

Presenter: Mohamed Elhaddad
Supervisor:

Date: Thu, December 23, 2021
Time: 14:00:00 - 15:00:00
Place: ZOOM - Please see below.

ABSTRACT

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Meeting ID: 580 349 6177

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                                   Meeting ID: 580 349 6177

 

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Abstract: Social networks play a vital and effective role not only in spreading misleading information related to COVID-19 but in various crises and conflicts around the world. With the presence of a new virus, whose characteristics and details are not fully known yet, and with a state of fear and panic among the general public, the spread and circulation of misleading information about this virus and its impact are ubiquitous. We propose a misleading-information detection model that relies on the World Health Organization, UNICEF, and the United Nations as sources of information, as well as epidemiological material collected from a range of fact-checking websites. Obtaining data from reliable sources should assure their validity. We use this collected ground-truth data to build a detection system that uses machine learning to identify misleading information. Ten machine learning algorithms, with seven feature extraction techniques, are used to construct a voting ensemble machine learning classifier. We perform 5-fold cross-validation to check the validity of the collected data and report the evaluation of twelve performance metrics. The evaluation results indicate the quality and validity of the collected ground-truth data and their effectiveness in constructing models to detect misleading information.