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.

scholarly journals Social cohesion and integration in schools reduces suicidal behaviour rate

2019 ◽  
Keyword(s):  
Social Network ◽  
Social Integration ◽  
Social Cohesion ◽  
Network Structure ◽  
Suicidal Behaviour ◽  
Social Bonds ◽  
Social Network Structure

Emerging data suggest that strengthening positive social bonds and improving social integration might reduce suicidal behaviours in youth to date; little research has studied the effect of social integration, on suicide behaviours, with reference to a young person’s social network structure — namely, an individual’s position within their network and the patterns of relationships among members of the network.

Evolutionary dynamics modeling of symbolic social network structure equilibrium

2020 ◽  
Vol 17 (10) ◽  
pp. 229-240
Author(s):  
Weijin Jiang ◽  
Sijian Lv ◽  
Yirong Jiang ◽  
Jiahui Chen ◽  
Fang Ye ◽  
...  
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Evolutionary Dynamics ◽  
Dynamics Modeling ◽  
Social Network Structure

Online Communities and Social Network Structure

2019 ◽  
Author(s):  
Yanhao Wei ◽  
Wensi Zhang ◽  
Sha Yang ◽  
Xi Chen
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Online Communities ◽  
Social Network Structure

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

scholarly journals How social network structure affects decision-making in Drosophila melanogaster

2016 ◽  
Vol 283 (1826) ◽  
pp. 20152954 ◽  
Author(s):  
Cristian Pasquaretta ◽  
Marine Battesti ◽  
Elizabeth Klenschi ◽  
Christophe A. H. Bousquet ◽  
Cedric Sueur ◽  
...  
Keyword(s):  
Decision Making ◽  
Drosophila Melanogaster ◽  
Social Network ◽  
Individual Decision ◽  
Social Network Structure ◽  
The Social ◽  
Survival And Reproduction ◽  
Crucial Information ◽  
Social Encounters ◽  
Selective Information

Animals use a number of different mechanisms to acquire crucial information. During social encounters, animals can pass information from one to another but, ideally, they would only use information that benefits survival and reproduction. Therefore, individuals need to be able to determine the value of the information they receive. One cue can come from the behaviour of other individuals that are already using the information. Using a previous extended dataset, we studied how individual decision-making is influenced by the behaviour of conspecifics in Drosophila melanogaster . We analysed how uninformed flies acquire and later use information about oviposition site choice they learn from informed flies. Our results suggest that uninformed flies adjust their future choices based on how coordinated the behaviours of the informed individuals they encounter are. Following social interaction, uninformed flies tended either to collectively follow the choice of the informed flies or to avoid it. Using social network analysis, we show that this selective information use seems to be based on the level of homogeneity of the social network. In particular, we found that the variance of individual centrality parameters among informed flies was lower in the case of a ‘follow’ outcome compared with the case of an ‘avoid’ outcome.

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