Study of Equities using Signed Social Network

2022 ◽  
Author(s):  
Madhav Agrawal ◽  
Shubham Sharma ◽  
Amit A. Nanavati ◽  
Sougata Mukherjea
Keyword(s):  
Social Network ◽  
Signed Social Network

An Extend SIR Opinion Dynamic Model Base on Empirical Signed Network Mining

2014 ◽  
Vol 513-517 ◽  
pp. 2744-2747
Author(s):  
Wei Li ◽  
Pei Li ◽  
Hui Wang
Keyword(s):  
Social Network ◽  
Web Crawler ◽  
Model Base ◽  
Network Mining ◽  
Web Structure ◽  
Online Social Media ◽  
Signed Social Network ◽  
Spread Model ◽  
Signed Network ◽  
Opinion Dynamic

Relations between users on online social media sites often reflect a mixture of positive and negative interactions. The network composed by those positive and negative relations is called signed social network. We design a web crawler to collect the data base on a special web event of battle between Fang Zhouzi and Han Han. And we construct a signed social network with sentiment weighted relationships base on this empirical data. Under this empirical spread web structure, we construct an extended SIR spread model in such a signed social network with sentiment weighted relationships, and we study influence with the network factors of signed, directed and weighted on opinion spreading. Under this model, we could know the proportion of signed edges is most important factor to the spread result.

Distrust Prediction in Signed Social Network

2019 ◽  
Vol 28 (1) ◽  
pp. 189-194
Author(s):  
Pengfei Shen ◽  
Shufen Liu ◽  
Lu Han
Keyword(s):  
Social Network ◽  
Signed Social Network

scholarly journals Exploiting Social Network Structure for Person-to-Person Sentiment Analysis

2014 ◽  
Vol 2 ◽  
pp. 297-310 ◽  
Author(s):  
Robert West ◽  
Hristo S. Paskov ◽  
Jure Leskovec ◽  
Christopher Potts
Keyword(s):  
Social Network ◽  
Sentiment Analysis ◽  
Social Bonds ◽  
Speech Corpus ◽  
Hinge Loss ◽  
Markov Random ◽  
Social Network Structure ◽  
Signed Social Network ◽  
Signed Social Networks ◽  
The Rich

Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A’s opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

The social network

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

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.

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