Processing power limits social group size: computational evidence for the cognitive costs of sociality

Dávid-Barrett, Tamás; Dunbar, Robin

doi:10.1098/rspb.2013.1151

Cited by 83 publications

(60 citation statements)

References 46 publications

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“…In this work, we focus on 1.5 ego-networks as they capture a reasonable level of social context (me, my friends, and the relationships between them) while still keeping the data requirements and computational complexity low. This is in congruence with the human processing limits and the social brain hypothesis proposed by Dunbar et al [5] and also the recent results that have shown the value of 1.5 ego networks in identifying network phenomena [1].…”

Section: Characterizing Social Structure Surrounding Bullying Messagessupporting

confidence: 84%

Cyber Bullying Detection Using Social and Textual Analysis

Huang

Singh

Atrey

2014

Proceedings of the 3rd International Workshop on Socially-Aware Multimedia

176

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Cyber Bullying, which often has a deeply negative impact on the victim, has grown as a serious issue among adolescents. To understand the phenomenon of cyber bullying, experts in social science have focused on personality, social relationships and psychological factors involving both the bully and the victim. Recently computer science researchers have also come up with automated methods to identify cyber bullying messages by identifying bullying-related keywords in cyber conversations. However, the accuracy of these textual feature based methods remains limited. In this work, we investigate whether analyzing social network features can improve the accuracy of cyber bullying detection. By analyzing the social network structure between users and deriving features such as number of friends, network embeddedness, and relationship centrality, we find that the detection of cyber bullying can be significantly improved by integrating the textual features with social network features.

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Section: Characterizing Social Structure Surrounding Bullying Messagessupporting

confidence: 84%

Cyber Bullying Detection Using Social and Textual Analysis

Huang

Singh

Atrey

2014

Proceedings of the 3rd International Workshop on Socially-Aware Multimedia

176

View full text Add to dashboard Cite

show abstract

“…In this work, we focus on building 1.5 ego networks as they capture a reasonable level of social context (me, my friends, and the relationships between them) while still keeping the data requirements and computational complexity low. This is in congruence with the human processing limits and the social brain hypothesis proposed by [63] and also the recent results that have shown the value of 1.5 ego networks in identifying network phenomena [62].…”

Section: Characterizing Social Structure Surrounding Bullying Messagessupporting

confidence: 72%

On cyberbullying incidents and underlying online social relationships

2018

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Cyberbullying is an important social challenge that takes place over a technical substrate. Thus, it has attracted research interest across both computational and social science research communities. While the social science studies conducted via careful participant selection have shown the effect of personality, social relationships, and psychological factors on cyberbullying, they are often limited in scale due to manual survey or ethnographic study components. Computational approaches on the other hand have defined multiple automated approaches for detecting cyberbullying at scale, and have largely focused only on the textual content of the messages exchanged. There are no existing efforts aimed at testing, validating, and potentially refining the findings from traditional bullying literature as obtained via surveys and ethnographic studies at scale over online environments. By analyzing the social relationship graph between users in an online social network and deriving features such as out-degree centrality and the number of common friends, we find that multiple social characteristics are statistically different between the cyberbullying and nonbullying groups, thus supporting many, but not all, of the results found in previous survey-based bullying studies. The results pave way for better understanding of the cyberbullying phenomena at scale.

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“…In contrast, we focus on the extent to which such decisions affect network cohesion and the indirect effects that this has on individual fitness. This approach shares with the models developed by Dávid-Barrett & Dunbar [23,46,47] a focus on achieving behavioural synchrony as an intermediate goal necessary for maximizing fitness, in line with Hamilton's conception of neighbourhood modulated fitness [29,30]. More importantly, it bypasses the conventional collective action dilemma created by free-riders because it is not possible to free-ride: one is either in the group or one is not, and one benefits accordingly [46].…”

Section: Introductionmentioning

confidence: 80%

Effects of deception in social networks

et al. 2014

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Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies ('white' lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agentbased model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these grouplevel effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole.

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Processing power limits social group size: computational evidence for the cognitive costs of sociality

Cited by 83 publications

References 46 publications

Cyber Bullying Detection Using Social and Textual Analysis

Cyber Bullying Detection Using Social and Textual Analysis

On cyberbullying incidents and underlying online social relationships

Effects of deception in social networks

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