TY - GEN
T1 - Smart Edge-based Fake News Detection using Pre-trained BERT Model
AU - Guo, Yuhang
AU - Lamaazi, Hanane
AU - Mizouni, Rabeb
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 %.
AB - 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 %.
KW - BERT
KW - Deep Learning
KW - Distributed Architecture
KW - Edge Computing
KW - Fake News
KW - Fine-Tuning
KW - Text Classification
UR - https://www.scopus.com/pages/publications/85142760145
UR - https://www.scopus.com/pages/publications/85142760145#tab=citedBy
U2 - 10.1109/WiMob55322.2022.9941689
DO - 10.1109/WiMob55322.2022.9941689
M3 - Conference contribution
AN - SCOPUS:85142760145
T3 - International Conference on Wireless and Mobile Computing, Networking and Communications
SP - 437
EP - 442
BT - 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2022
PB - IEEE Computer Society
T2 - 18th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2022
Y2 - 10 October 2022 through 12 October 2022
ER -