Automatic Detection of Rumor on Social Network

2015 ◽  
pp. 113-122 ◽  
Author(s):  
Qiao Zhang ◽  
Shuiyuan Zhang ◽  
Jian Dong ◽  
Jinhua Xiong ◽  
Xueqi Cheng
Keyword(s):  
Social Network ◽  
Automatic Detection

scholarly journals Retraction: Big Data and Automatic Detection of Topics: Social Network Texts (Journal of Physics: Conference Series 1432 012073)

2020 ◽  
Vol 1432 ◽  
pp. 012102
Author(s):  
Jesús Silva ◽  
Hugo Hernández Palma ◽  
William Niebles Núñez ◽  
Alex Ruiz-Lazaro ◽  
Noel Varela
Keyword(s):  
Big Data ◽  
Social Network ◽  
Automatic Detection ◽  
Conference Series

scholarly journals Big Data and Automatic Detection of Topics: Social Network Texts

2020 ◽  
Vol 1432 ◽  
pp. 012073
Author(s):  
Jesús Silva ◽  
Hugo Hernández Palma ◽  
William Niebles Núñez ◽  
Alex Ruiz-Lazaro ◽  
Noel Varela
Keyword(s):  
Big Data ◽  
Social Network ◽  
Automatic Detection

scholarly journals Automatic Detection of Lupus Butterfly Malar Rash Based on Transfer Learning

2020 ◽  
Author(s):  
Jhonatan Souza ◽  
Tiago De Oliveira ◽  
Claudemir Casa ◽  
André Ortoncelli
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Social Network ◽  
Transfer Learning ◽  
Language Processing ◽  
Automatic Detection ◽  
Malar Rash ◽  
Popular Approach ◽  
Starting Point

This work presents an approach to the automatic detection of Butterfly Malar Rash (BMR) in images. BMR is a Lupus symptom characterized by a reddish facial rash that appears symmetrically in the cheeks and the back of the nose. The proposed approach is based on Transfer Learning, a popular approach in Deep Learning that consists in the use of pre-trained models as the starting point for computer vision and natural language processing tasks. To perform the experiments, a database was created with images manually collected from the Instagram social network, searching for images with #butterflyrash. We evaluated the proposed approach with eight Convolutional Neural Networks (CNN) architecture. The experimental results are good results, with a precision of up to 0.957.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Automatic detection of epileptiform spikes in the electrocorticogram: a comparison of two algorithms

Seizure ◽  
1998 ◽  
Vol 7 (2) ◽  
pp. 145-152 ◽  
Author(s):  
M DUMPELMANN ◽  
C ELGER
Keyword(s):  
Automatic Detection

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Sign in / Sign up

Export Citation Format

Share Document