Social Network Anomaly Detection for Optimized Decision Development

Abstract

The paper presents the development of systems for improved source selection in a process that creates real-time categorization of events using only posts collected through various sensing applications that use social networks (such as Twitter or other mass dissemination networks) for reporting. The system recognizes critical instances in applications and simply views essential information from users (either by explicit user action or by default, as on Twitter) within the event and provides a textual description. As a result, social networks open up unprecedented possibilities for creating sensing applications by representing a set of tweets generated in a limited timeframe as a weighted network for influence concerning users. Obtaining data from a network of social site users substantiates the quality and dependability of data. It collects many users' dynamic behavior to construct and disseminate related information across the channel. The goal is to find a link between various data sources for event abnormalities.