Gender and Friendship Norms Among Older Adults

The authors examined same- and cross-gender friendship norms in a sample of 135 adults (average age 73 years). Participants evaluated a friend’s behavior, quantitatively and qualitatively, in vignettes in which the friend’s gender was experimentally manipulated. Gender often significantly, though modestly, influenced normative evaluations. Women frequently had higher expectations of friends than men and placed a greater emphasis on intimacy. Women were more disapproving of violations of friendship rules, such as betraying a confidence, paying a surprise visit, and failing to stand up for a friend in public. However, both men and women were less approving of a man than a woman who greets another friend with a kiss or who requests to stay overnight. Respondents’ open-ended comments reflected positive attitudes regarding cross-gender friendships. Most findings demonstrated that men and women across a wide age range held similar cultural norms for close ties, norms of trust, commitment, and respect

Dyads, triads, and tetrads: a multivariate simulation approach to uncovering network motifs in social graphs

Motifs represent local subgraphs that are overrepresented in networks. Several disciplines document multiple instances in which motifs appear in graphs and provide insight into the structure and processes of these networks. In the current paper, we focus on social networks and examine the prevalence of dyad, triad, and symmetric tetrad motifs among 24 networks that represent six types of social interactions: friendship, legislative co-sponsorship, Twitter messages, advice seeking, email communication, and terrorist collusion. Given that the correct control distribution for detecting motifs is a matter of continuous debate, we propose a novel approach that compares the local patterns of observed networks to random graphs simulated from exponential random graph models. Our proposed technique can produce conditional distributions that control for multiple, lower-level structural patterns simultaneously. We find evidence for five motifs using our approach, including the reciprocated dyad, three triads, and one symmetric tetrad. Results highlight the importance of mutuality, hierarchy, and clustering across multiple social interactions, and provide evidence of “structural signatures” within different genres of graph. Similarities also emerge between our findings and those in other disciplines, such as the preponderance of transitive triads

From evolution to revolution: understanding mutability in large and disruptive human groups

Over the last 70 years there has been a major shift in the threats to global peace. While the 1950's and 1960's were characterised by the cold war and the arms race, many security threats are now characterised by group behaviours that are disruptive, subversive or extreme. In many cases such groups are loosely and chaotically organised, but their ideals are sociologically and psychologically embedded in group members to the extent that the group represents a major threat. As a result, insights into how human groups form, emerge and change are critical, but surprisingly limited insights into the mutability of human groups exist. In this paper we argue that important clues to understand the mutability of groups come from examining the evolutionary origins of human behaviour. In particular, groups have been instrumental in human evolution, used as a basis to derive survival advantage, leaving all humans with a basic disposition to navigate the world through social networking and managing their presence in a group. From this analysis we present five critical features of social groups that govern mutability, relating to social norms, individual standing, status rivalry, ingroup bias and cooperation. We argue that understanding how these five dimensions interact and evolve can provide new insights into group mutation and evolution. Importantly, these features lend themselves to digital modeling. Therefore computational simulation can support generative exploration of groups and the discovery of latent factors, relevant to both internal group and external group modelling. Finally we consider the role of online social media in relation to understanding the mutability of groups. This can play an active role in supporting collective behaviour, and analysis of social media in the context of the five dimensions of group mutability provides a fresh basis to interpret the forces affecting groups

Parameterising the dynamics of inter-group conflict from real world data

Generative modelling of inter-group relations enables probabilistic forecasting of possible conflict for scenarios where real-world data is sparse. In order for such models to have relevance and integrity, it is important to ensure that realworld data is used to parameterise the model and verify its characteristics. In this paper we investigate how real-world datasets can be mapped into generative model parameters concerning group structures and behaviours. We highlight the issues involved and present a framework for classifying potential data based on three attributes: (i) inter-group structure, (ii) inter-group actions and (iii) impact of actions. We argue that these attributes are fundamental for benchmarking and developing generative models in the context of limited existing data on inter-group interaction

Understanding the characteristics of COVID-19 misinformation communities through graphlet analysis

Online social networks serve as a convenient way to connect, share, and promote content with others. As a result, these networks can be used with malicious intent, causing disruption and harm to public debate through the sharing of misinformation. However, automatically identifying such content through its use of natural language is a significant challenge compared to our solution which uses less computational resources, language-agnostic and without the need for complex semantic analysis. Consequently alternative and complementary approaches are highly valuable. In this paper, we assess content that has the potential for misinformation and focus on patterns of user association with online social media communities (subreddits) in the popular Reddit social media platform, and generate networks of behaviour capturing user interaction with different subreddits. We examine these networks using both global and local metrics, in particular noting the presence of induced substructures (graphlets) assessing posts from 96,634 users. From subreddits identified as having potential for misinformation, we note that the associated networks have strongly defined local features relating to node degree — these are evident both from analysis of dominant graphlets and degree-related global metrics. We find that these local features support high accuracy classification of subreddits that are categorised as having the potential for misinformation. Consequently we observe that induced local substructures of high degree are fundamental metrics for subreddit classification, and support automatic detection capabilities for online misinformation independent from any particular language