Viral Marketing

Viral marketing research focuses on understanding and predicting the spread of information or products through online social networks, aiming to optimize strategies for maximizing reach and impact. Current research employs various machine learning models, including transformer-based architectures, graph convolutional networks, and other deep learning approaches, to analyze factors like content features, network structure, and user behavior in predicting virality. These studies have implications for targeted advertising, public health campaigns (e.g., combating misinformation), and a deeper understanding of information diffusion dynamics in online environments. The development of accurate predictive models holds significant practical value for businesses and organizations seeking to leverage the power of viral marketing.