BERT-Based Detection of Fake Twitter Profiles: A Case Study on the Israel-Palestine War

The increasing number of fake social media accounts poses notable obstacles to the genuineness and legitimacy of virtual conversations, especially during sensitive geopolitical circumstances like the war between Israel and Palestine. In this work, we primarily focus on the activities of fake Twitter profiles during the Israel-Palestine war and use a BERT-based technique for their detection. For text processing and classification, we use BERT, a transformer-based model that has been trained beforehand, by utilizing text data from Twitter. For text processing and classification, we use BERT, a transformer-based model that has been trained beforehand, by utilizing text data from Twitter. To improve classification accuracy, we extract features from textual data using BERT-preprocess and BERT-encoder. This study adds to larger initiatives to counter false information and improve the legitimacy of internet content, especially when it comes to delicate geopolitical situations. Our research provides insights into efficient detection strategies, particularly during crucial global crises, and emphasizes the importance and urgency of tackling the spread of fake profiles on social media platforms.