Smart Edge-based Fake News Detection using Pre-trained BERT Model

Today, online media applications are an important source of information. People are creating and sharing more information than ever before around the world. Being provided by unreliable sources, some news can be misleading. In fact, the assessment of the correctness of the news can be region related. In other words, news can be true in a specific region while fake in another. Existing proposed solutions for fake news detection developed in centralized platforms are not considering the location from where the news gets announced, but they are focused more on the news content. In this paper, a region-based distributed fake news detection framework is proposed. The framework is deployed in a mobile crowdsensing (MCS) environment where a set of workers responsible for collecting news are selected based on their availability in a specific region. The selected workers share the news to the nearest edge node, where the pre-processing and detection of fake news are executed locally. The detection process uses a pre-trained BERT model where it achieved an accuracy of 91 %.