Sher-Locked: A Hybrid Deep Learning Model Based Mobile Platform for Social Media Fact-Checking

Abstract

In the present context, false news can be easily constructed and circulated through various social media plat-forms. As a result, people on those platforms have difficulty in distinguishing between correct and incorrect information. Therefore, a firm desire appears to develop a fact-checking platform to address this issue. From this research study, the authors present ‘Sher-Locked’ which is a hybrid deep learning model based mobile platform to fact-check information on social media. The process of checking and verifying information is referred to as fact-checking. A hybrid deep learning model which is mainly focused on CNN and RNN-LSTM networks integrated with the mobile application to check and verify information on social media. The high-level characteristics and interdependencies among the input text capture from the hybrid  model. The mobile application consists of several features such as fact-checking, daily news updates, news reporting, social media trends and daily COVID-19 reports. Flutter chose as the mobile application development framework along with Firebase as the backend development framework with REST APIs to develop the entire system. When checking and verifying the information mitigating on social media, the hybrid model  achieved a 92% accuracy by surpassing most of the traditional models today with 91% score rates for Precision, Recall and F1-Score. After delivering the mobile app as a complete system to various users for testing, the authors discovered that the user  satisfaction and usability rates are high when compared to other related software.

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