Social dynamics in conferences: analyses of data from the Live Social Semantics application

Popularity and spread of online social networking in recent years has given a great momentum to the study of dynamics and patterns of social interactions. However, these studies have often been confined to the online world, neglecting its interdependencies with the offline world. This is mainly due to the lack of real data that spans across this divide. The Live Social Semantics application is a novel platform that dissolves this divide, by collecting and integrating data about people from (a) their online social networks and tagging activities from popular social networking sites, (b) their publications and co-authorship networks from semantic repositories, and (c) their real-world face-to-face contacts with other attendees collected via a network of wearable active sensors. This paper investigates the data collected by this application during its deployment at three major conferences, where it was used by more than 400 people. Our analyses show the robustness of the patterns of contacts at various conferences, and the influence of various personal properties (e.g. seniority, conference attendance) on social networking patterns

Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach

Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition

The Dynamics of Health Behavior Sentiments on a Large Online Social Network

Modifiable health behaviors, a leading cause of illness and death in many countries, are often driven by individual beliefs and sentiments about health and disease. Individual behaviors affecting health outcomes are increasingly modulated by social networks, for example through the associations of like-minded individuals - homophily - or through peer influence effects. Using a statistical approach to measure the individual temporal effects of a large number of variables pertaining to social network statistics, we investigate the spread of a health sentiment towards a new vaccine on Twitter, a large online social network. We find that the effects of neighborhood size and exposure intensity are qualitatively very different depending on the type of sentiment. Generally, we find that larger numbers of opinionated neighbors inhibit the expression of sentiments. We also find that exposure to negative sentiment is contagious - by which we merely mean predictive of future negative sentiment expression - while exposure to positive sentiments is generally not. In fact, exposure to positive sentiments can even predict increased negative sentiment expression. Our results suggest that the effects of peer influence and social contagion on the dynamics of behavioral spread on social networks are strongly content-dependent

Statistical Proof and Theories of Discrimination

We live in a tightly knit world. Our emotions, desires, perceptions and decisions are interlinked in our interactions with others. We are constantly influencing our surroundings and being influenced by others. In this thesis, we unfold some aspects of social and economical interactions by studying empirical datasets. We project these interactions into a network representation to gain insights on how socio-economic systems form and function and how they change over time. Specifically, this thesis is centered on four main questions: How do the means of communication shape our social network structures? How can we uncover the underlying network of interests from massive observational data? How does a crisis spread in a real financial network? How do the dynamics of interaction influence spreading processes in networks? We use a variety of methods from physics, psychology, sociology, and economics as well as computational, mathematical and statistical analysis to address these questions

Social Network Analysis: From Graph Theory to Applications with Python

Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the fields of social psychology, statistics and graph theory. This talk will covers the theory of social network analysis, with a short introduction to graph theory and information spread. Then we will deep dive into Python code with NetworkX to get a better understanding of the network components, followed-up by constructing and implying social networks from real Pandas and textual datasets. Finally we will go over code examples of practical use-cases such as visualization with matplotlib, social-centrality analysis and influence maximization for information spread.Comment: Presented at PyCon'19 - Israeli Python Conference 201