Understanding the Psycho-Sociological Facets of Homophily in Social Network Communities

Abstract

Community" in social networks is a nebulous concept. A community is generally assumed to be formed by people who possess similar attributes or characteristics, also known as "homophily". Although there has been a lot of research on community detection based on network topology, the semantic interpretation of communities is rarely studied. The present work aims to understand the behavioural similarity of users present in their personal neighbourhood communities formed by friends, relatives, or colleagues, and addresses two fundamental questions: (i) Are communities formed by users who possess similar behavioural traits? If so, does this apply to all those sub-networks, i.e., friends, relatives, and colleagues? (ii) Does adding behavioural node-specific attributes/features to the nodes in a network lead to better community detection? To better understand the psycho-sociological homophilic nature of personal networks, the personalities and values of Twitter users were analysed using the well-established "Big-5 personality model" and "Schwartz sociological behaviour model". Empirical results based on the psycho-sociological behaviour show that friends networks exhibit homophily, whereas relatives and colleagues networks do not exhibit such homophilic behaviour. It can also be observed that neurotic people tend to behave heterogeneously with people of various personality traits. In addition, it is shown that such empirical evidence can be used as features for the tasks of community detection and link prediction.

Making sense of tweets using sentiment analysis on closely related topics

“…Generally, a friend follower network is selected because community detection (Solomon et al. 2019 ; Luo et al. 2020 ), in general, used to detect closely related groups.…”

“…To apply a community detection algorithm, we need to model tweets as mathematical graphs. Generally, a friend follower network is selected because community detection (Solomon et al 2019;Luo et al 2020), in general, used to detect closely related groups. We are more interested in closely related topics than groups; hence, we use hashtags to model our network (Xiao et al 2014).…”

Making Sense of Tweets Using Sentiment Analysis on Closely Related Topics

“…To apply a community detection algorithm, we need to model tweets as mathematical graphs. Generally, a friend follower network is selected because community detection [62,43], in general, is used to detect closely related groups. We are more interested in closely related topics than groups; hence we use hashtags to model our network [68].…”

A Survey on Trust Prediction in Online Social Networks

“…Gerris et al [136] studied the influence of the Big Five personality traits of couples on their marriages. Solomon et al [137] studied Twitter users based on the Big Five personality model [132] and the Schwartz sociological behaviour model [138] to understand the psychosociological homophilic nature of personal networks. We proposed a pattern-based word embedding technique, person-ality2vec [139] as a novel data analytics pipeline that enables analysis of users' personality patterns and behavioural disorders, based on their activities in OSNs.…”