Fake identity & fake activity detection in online social networks based on transfer learning

Overview & Synopsis

While Social Media has connected more people around the world and has increased the easeof access to free content, but is dealing with critical phenomena such as fake content, fakeidentities, and fake activities. Fake content detection on social media has recently becomeemerging research that is attracting tremendous attention. In this area, fake identities areplaying an important role in the production and propagation of fake content in Online SocialNetworks such as Meta (Facebook), Twitter, and Instagram. The main reason behind thisis that social media encourages impersonators, malicious accounts, trolls, and social bots toproduce content and interact with humans or other bots without considering the credibilityof the content and entice users to click and share them.In this thesis, I primarily concentrate on impersonators as one of the concerning vari-eties of fake identities. These entities are nefarious fake accounts that intend to disguise alegitimate account by making similar profiles and then striking social media with fake con-tent, which makes it considerably harder to understand which posts are genuinely produced.The recent advancements in Natural Language Processing (NLP), and Transformer-basedLanguage Models (LM) can be adapted to develop automatic methods for many relatedNLP downstream tasks in this area. Language Models and their flexibility to cope withany corpus delivering great results has made this approach very popular. The fake contentclassification can be handled using Pretrained Language Models (PLM) and accurate deeplearning models.The aim of this thesis is to investigate the problem of fake identities, fake activities, andtheir generated ingenuine content in social media and propose algorithms to classify fakecontent. We define fake content as a verifiably false piece of information shared intentionallyto mislead the readers. I propose different approaches in which I adapt advanced TransferLearning (TL) models and NLP techniques to detect fake identities and classify fake contentautomatically.