The Role of Gender in Social Network Organization

The digital traces we leave behind when engaging with the modern world offer an interesting lens through which we study behavioral patterns as expression of gender. Although gender differentiation has been observed in a number of settings, the majority of studies focus on a single data stream in isolation. Here we use a dataset of high resolution data collected using mobile phones, as well as detailed questionnaires, to study gender differences in a large cohort. We consider mobility behavior and individual personality traits among a group of more than $800$ university students. We also investigate interactions among them expressed via person-to-person contacts, interactions on online social networks, and telecommunication. Thus, we are able to study the differences between male and female behavior captured through a multitude of channels for a single cohort. We find that while the two genders are similar in a number of aspects, there are robust deviations that include multiple facets of social interactions, suggesting the existence of inherent behavioral differences. Finally, we quantify how aspects of an individual's characteristics and social behavior reveals their gender by posing it as a classification problem. We ask: How well can we distinguish between male and female study participants based on behavior alone? Which behavioral features are most predictive

Shyness and Online Social Networking Services

Online social networking services are Internet websites that allow individuals to learn about and communicate with others. This study investigated the association between use of these websites and friendship quality for individuals varying in shyness. Participants (N = 241) completed questionnaires assessing their use of Facebook, an online social networking service, shyness, perceived available social support, loneliness, and friendship quality. Results indicated an interaction between shyness and Facebook usage, such that individuals high in shyness (when compared to less shy individuals) reported stronger associations between Facebook use and friendship quality. Facebook use, however, was unrelated to loneliness among highly shy individuals. Therefore, online social networking services may provide a comfortable environment within which shy individuals can interact with others

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Using Learning Analytics to Implement Evidence-Based Interventions to Support Ethnic Minority and International Student Social Integrations

As universities in the UK become increasingly diverse, one common challenge is how best to socially integrate ethnic minority and international students into the classroom and larger campus. Indeed, research currently demonstrates that students most often form social and learning connections with peers from the same ethnicity or culture, despite the benefits of intergroup connections. However, few studies have looked at student social networks to determine how they influence actual behaviours in group learning activities. In this research, Social Network Analysis and Learning Analytics methods will be used to explore the role of social networks in classroom participation and attainment for ethnic minority and international students, highlighting replicable interventions that can help promote social cohesion in the UK

Measure of Node Similarity in Multilayer Networks

The weight of links in a network is often related to the similarity of the nodes. Here, we introduce a simple tunable measure for analysing the similarity of nodes across different link weights. In particular, we use the measure to analyze homophily in a group of 659 freshman students at a large university. Our analysis is based on data obtained using smartphones equipped with custom data collection software, complemented by questionnaire-based data. The network of social contacts is represented as a weighted multilayer network constructed from different channels of telecommunication as well as data on face-to-face contacts. We find that even strongly connected individuals are not more similar with respect to basic personality traits than randomly chosen pairs of individuals. In contrast, several socio-demographics variables have a significant degree of similarity. We further observe that similarity might be present in one layer of the multilayer network and simultaneously be absent in the other layers. For a variable such as gender, our measure reveals a transition from similarity between nodes connected with links of relatively low weight to dis-similarity for the nodes connected by the strongest links. We finally analyze the overlap between layers in the network for different levels of acquaintanceships.Comment: 12 pages, 4 figure

Internet addiction in students: prevalence and risk factors

The last decade has witnessed a large increase in research on the newly emerging mental health problem of Internet addiction. Rather than looking at Internet addiction per se, this study focused on particular activities on the Internet that might be potentially addictive and linked them to personality traits that might predispose individuals to Internet addiction. The aims of this study were (i) to assess the prevalence of clinically significant levels of Internet addiction, and to (ii) discern the interplay between personality traits and specific Internet uses in increasing the risk for Internet addiction. This cross-sectional online survey used data from 2,257 students of an English university. Results indicated that 3.2% of the students were classified as being addicted to the Internet. The included personality traits and uses of online activities explained 21.5% of the variance in Internet addiction. A combination of online shopping and neuroticism decreased the risk for Internet addiction, whereas a combination of online gaming and openness to experience increased it. In addition to this, frequent usage of online shopping and social online activities, high neuroticism and low agreeableness significantly increased the chances of being addicted to the Internet. Findings and their implications are discussed

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

Toward a Model for Fisheries Social Impact Assessment

This paper presents a model for Fisheries Social Impact Assessment (SIA) that lays the groundwork for development of fisheries-focused, quantitative social assessments with a clear conceptual model. The usefulness of current fisheries SIA’s has been called into question by some as incompatible with approaches taken by fisheries biologists and economists when assessing potential effects of management actions. Our model’s approach is closer to the economists’ and biologists’ assessments and is therefore more useful for Fishery Management Council members. The paper was developed by anthropologists initially brought together in 2004 for an SIA Modeling Workshop by the National Marine Fisheries Service, NOAA. Opinions and conclusions expressed or implied are solely those of the authors and do not necessarily reflect the views or policy of the National Marine Fisheries Service, NOAA