This paper examines the challenges of text classification in social networks, particularly focusing on the issues posed by short text and informal language. The brevity and informal nature of social media posts, filled with slang, abbreviations, emojis, and code-switching, complicate traditional text classification techniques. We explore various approaches, including machine learning models, Natural Language Processing (NLP) techniques, and deep learning architectures designed to address these challenges. Furthermore, we discuss the use of context-aware models and transfer learning to improve the accuracy of classification in social networks.