On the Social Influence in Human Behavior: Physical, Homophily, and Social Communities

Abstract

Understanding the forces governing human behavior and social dynamics is a challenging problem. Individuals' decisions and actions are affected by interlaced factors, such as physical location, homophily, and social ties. In this paper, we propose to examine the role that distinct communities, linked to these factors, play as sources of social influence. The ego network is typically used in the social influence analysis. Our hypothesis is that individuals are embedded in communities not only related to their direct social relationships, but that involve different and complex forces. We analyze physical, homophily, and social communities to evaluate their relation with subjects' behavior. We prove that social influence is correlated with these communities, and each one of them is (differently) significant for individuals. We define community-based features, which reflect the subject involvement in these groups, and we use them with a supervised learning algorithm to predict subject participation in social events. Results indicate that both communities and ego network are relevant sources of social influence, confirming that the ego network alone is not sufficient to explain this phenomenon. Moreover, we classify users according to the degree of social influence they experienced with respect to their groups, recognizing classes of behavioral phenotypes. To our knowledge, this is the first work that proves the existence of phenotypes related to the social influence phenomenon.

Analyzing and inferring human real-life behavior through online social networks with social influence deep learning

“…Though the one-hop neighborhood (ego network) appeared as the straightforward solution to detect the set of input (nodes) that affected the prediction related to a given user, results show that the ego network alone is not enough to explain the social influence phenomenon. As we discussed in Luceri et al (2017), mesolevel structures, such as communities, provide a significant contribution to the understanding of this phenomenon. Results show that C-SIDL closely approaches the performance of the general model.…”

“…In the second direction, we will evaluate different solutions to group users in mesolevel structures and employ C-SIDL accordingly. A wide spectrum of possibilities can be explored for this purpose, ranging from homophily community (Luceri et al 2017), which groups together similar individuals with low social distance (Förster et al 2012), to tensor decomposition techniques (Kolda and Bader 2009), which extract sub-network modules composed of nodes with correlated activity.…”

“…In the event main page, a user can see the information related to the event, e.g., date, location, and description, along with the confirmed participants. This information may activate processes of social influence, which can drive user participation in the events (Georgiev et al 2014;Luceri et al 2017).…”

“…In Singla and Richardson (2008) and (Anagnostopoulos et al 2008), the authors propose how to qualitatively measure the existence of social influence, whereas in Crandall et al (2008) the correlation between social similarity and influence is examined. In Luceri et al (2017); Luceri (2016), we introduce a novel interpretation of physical, homophily, and social community, as sources of social influence. Along with these qualitative studies, complementary research efforts have focused on developing predictive models of diffusion processes (Guille et al 2013).…”

VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things

“…The HBDC experiments aim to collect large-scale data of human beings with the objective of understanding and predicting the subjects' behavior and the social dynamics among them. The purpose is to investigate the forces that drive people aggregation in groups (or communities) [42] and to examine the factors that mostly affect individuals' decisions and actions.…”