Social Entity Embeddings

Social entity embeddings represent entities (e.g., users, organizations) as low-dimensional vectors capturing their social context and relationships within online networks. Current research focuses on developing methods to learn these embeddings, often leveraging co-occurrence patterns in follower networks or incorporating contextual information from text data (e.g., tweets) using techniques like language models and convolutional neural networks. These embeddings are proving valuable for various applications, including sentiment analysis, political bias detection, and even improving the performance of downstream tasks like speaker separation and market trend prediction.