scholarly journals CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams

2022 ◽  
Author(s):  
Deepak Kumar Jain ◽  
Akshi Kumar ◽  
Akshat Shrivastava
Keyword(s):  
Data Streams ◽  
Neural Model ◽  
Social Data

scholarly journals Social Media Multidimensional Analysis for Intelligent Health Surveillance

2020 ◽  
Vol 17 (7) ◽  
pp. 2289
Author(s):  
María José Aramburu ◽  
Rafael Berlanga ◽  
Indira Lanza
Keyword(s):  
Social Networks ◽  
Data Streams ◽  
Public Health Surveillance ◽  
Quality Metrics ◽  
Health Surveillance ◽  
Multidimensional Analysis ◽  
User Profiles ◽  
Multidimensional Model ◽  
Social Data

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

2019 ◽  
Vol 93 ◽  
pp. 42-64
Author(s):  
Irene Kilanioti ◽  
Alejandro Fernández-Montes ◽  
Damián Fernández-Cerero ◽  
Christos Mettouris ◽  
Valentina Nejkovic ◽  
...  
Keyword(s):  
Data Streams ◽  
Energy Efficient ◽  
Cost Effective ◽  
Context Aware ◽  
Social Data ◽  
Efficient Data ◽  
Data Centres

Recursive Ground Truth Estimator for Social Data Streams

2016 ◽  
Author(s):  
Shuochao Yao ◽  
Md. Tanvir Amin ◽  
Lu Su ◽  
Shaohan Hu ◽  
Shen Li ◽  
...  
Keyword(s):  
Data Streams ◽  
Ground Truth ◽  
Social Data

Stochastic stability of a neural model for binocular rivalry

2014 ◽  
Vol 1 ◽  
pp. 739-742
Author(s):  
Tetsuya Shimokawa ◽  
Kenji Leibnitz ◽  
Ferdinand Peper
Keyword(s):  
Binocular Rivalry ◽  
Stochastic Stability ◽  
Neural Model
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