Framework for Efficient Spam Detection in Online Social Network

Abstract

Online Social Networks (OSNs) are perpetually evolving and used in plenteous applications such as communication, news, entertainment, businesses, gaming, marketing and advertisement, live-streaming, job search, dating, education, healthcare, etc. Simultaneously, cybercriminals and botnets with groups of fake/bot accounts use OSNs to disseminate spam, misleading facts, fake news, hate speech, and malicious links to targeted users or masses to perform cyber-crimes, earn money, polarize sentiments, and impact users online interaction time. Moreover, prevalent spam degrades available information quality, network bandwidth, computing power, and speed. Recently, AI-enabled Deepfakes have exacerbated these issues at large. Thus, to detect and eliminate social spam and spammers from OSNs, it is necessary to review recent research on these topics. This doctoral thesis thoroughly reviews existing solutions for social spam and spammer detection techniques. Initially, background related to social spam, the spamming process, and social spam taxonomy is discussed. Later, the extensive review reveals various essentials and critical challenges to detect and combat social spam. The thesis uncovers important information about features used, dimensionality reduction techniques used for feature selection/extraction, existing datasets, and various machine learning and deep learning methodologies used for social spam and spammer detection, along with their strengths and limitations. Also, the thesis explores information related to recent AI-enabled Deepfake (text, image, and video) spam and its countermeasures. The doctoral thesis aims to advance the field of spam detection in OSN by developing Machine Learning (ML) and Deep Learning (DL) based approaches to address the most pressing issues in social spam detection, thereby improving the performance of spam detection. Most previous research relied on small datasets and witnessed class imbalance issues, resulting in biased outcomes towards the majority class. This study uses