Title
Information Dissemination From Social Network for Extreme Weather Scenario
Abstract
The recent trend of dependence on the social network for information abstraction and propagation has a cumulative effect on critical response. The content and reliability of data are substantiated by acquiring data from a network of social site users. It captures the engaged multiple user behavior to formulate and diffuse the connected information across the channel. The objective is to identify a bridge between different data sources for event anomalies. This article proposes a novel approach toward identifying the sublevel anomalies and predictive investigation toward the use of Twitter’s social data during extreme weather scenarios. We performed qualitative analyses by gathering data from social media and weather data websites. Various analysis methods are proposed to aggregate the diffused information from the social network to generate influencing data. The analyses results further identify the connected user acknowledgment for dominant information in the public domain. This information is mapped by applying a convolutional neural network for a physical sensor data set to detect weather anomalies. Moreover, we exploited the reinforcement learning technique to determine smart policy on the influencing data. The results show that our proposed method can predict critical events with high precision during extreme weather emergency scenarios.
Year
DOI
Venue
2020
10.1109/TCSS.2020.2964253
IEEE Transactions on Computational Social Systems
Keywords
DocType
Volume
Sensors,Meteorology,Real-time systems,Data analysis,Twitter,Correlation
Journal
7
Issue
ISSN
Citations 
2
2329-924X
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Harshit Srivastava100.34
Ravi Sankar265655.66