Detecting the Emerging topic from Social Network

Abstract

Social network is a place where people exchange and share information related to the current events all over the world. 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. A term-frequency-based approach could suffer from the ambiguity caused by synonyms or homonyms. 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.

It may also require complicated pre-processing (e.g., segmentation) depending on the target language. We propose 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. We demonstrate our technique in several real data sets we gathered from social networks.We have combined the proposed mention model with the SDNML change point detection algorithm and Kleinbrg’s burst detection model to pinpoint the emergence of a topic. We have to do is deliver to people the best and freshest most relevant information possible.

Key Words:-Social network; Anomaly Detection; Topic detection