Anomaly Detection in Social Streams to Discover Emerging Topics

Detection of emerging topics is now receiving renewed interest motivated by the rapid growth of social networks. Conventional-term-frequency-based approaches may not be appropriate in this context, because the information exchanged in social-network posts include not only text but also images, URLs, and videos. This project focuses on emergence of topics signalled by social aspects of these networks. Specifically, this project focuses on mentions of user links between users that are generated dynamically through replies, mentions, and retweets. This project implements a probability model of the mentioning behaviour of a social network user, and propose to detect the emergence of a new topic from the anomalies measured through the model. Aggregating anomaly scores from users, it can be seen that we can detect emerging topics only based on the reply/mention relationships in social-network posts. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as text-anomaly-based approaches, and in some cases much earlier when the topic is poorly identified by the textual contents in posts.
Chcking.