Specialization, homophily, and gender in a social curation site

Abstract

Pinterest is a popular social curation site where people collect, organize, and share pictures of items. We studied a fundamental issue for such sites: what patterns of activity attract attention (audience and content reposting)? We organized our studies around two key factors: the extent to which users specialize in particular topics, and homophily among users. We also considered the existence of differences between female and male users. We found: (a) women and men differed in the types of content they collected and the degree to which they specialized; male Pinterest users were not particularly interested in stereotypically male topics; (b) sharing diverse types of content increases your following, but only up to a certain point; (c) homophily drives repinning: people repin content from other users who share their interests; homophily also affects following, but to a lesser extent. Our findings suggest strategies both for users (e.g., strategies to attract an audience) and maintainers (e.g., content recommendation methods) of social curation sites.

Mobile app-etite: Consumer attitudes towards and use of mobile technology in the context of eating behaviour

“…19,20 Research is needed that describes consumers' attitudes towards and use of food-related mobile technology and apps, such as apps that support users in planning, purchasing and socially sharing meals and snacks. Food and drinks are popular topics on social media and are the top content shared on Pinterest, [21][22][23] which can be accessed via mobile applications. In 2012, 60 per cent of adults reported tracking their weight, diet or exercise routines, but less than 10 per cent of adults reported using their mobile device to do so, 6 suggesting that this category of app has market expansion potential.…”

Understanding How GitHub Supports Curation Repositories

“…Conversely, social media sites, such as Twitter and Pinterest, have a very general user base comprising users that curate for "Entertainment & Hobbies" [5], or "Food & Drink", "DIY & Craft", "Home Decor", and "Women's Fashion" [9]. Second, GitHub concentrates on software practice, where features for collaboration are emphasized [1,2,8], while curation in other social media sites focuses on individuals and does not emphasize collaboration among its users [6,9]. The comparison leaves an interesting question concerning the characteristics of GitHub as an entirely different environment for curation.…”

“…Duh et al found that entertainment and hobbies together with serious topics, such as society, politics, and economics, are among the most favored topics when curating tweets. Change et al (2014) discovered that the most popular categories of pins are "food and drink", "DIY crafts", and "home decoration" [9]. We are interested in identifying the objectives of curation repositories in GitHub, which offers a different context for this behavior than Twitter or Pinterest.…”

Mining user behavior in location-based social networks

“…Homophily is the principle that a contact between similar people occurs at a higher rate than among dissimilar people, and such rate can be measured by cultural, behavioral, genetic, or material information flowing through networks (MCPHERSON; SMITH-LOVIN; COOK, 2001;CHANG et al, 2014;FARALLI;STILO;VELARDI, 2015). Aiello et al (2012) observed a strong correlation between the social connectivity and the intensity of explicit user activities like tagging and participation in groups.…”

“…Several link prediction methods use a variety of information sources available in OSNs. Some of the information sources commonly used are: i) homophily (MCPHERSON;SMITH-LOVIN;COOK, 2001;CHANG et al, 2014;FARALLI;STILO;VELARDI, 2015), ii) social ties (LÜ; ZHOU, 2010; SOCIEVOLE; RANGO; MARANO, 2013;KLEINBERG, 2014;XU et al, 2017) and iii) communities (ZHELEVA et al, 2008;SOUNDARAJAN;HOPCROFT, 2012;LOPES, 2012a;HOSEINI;HASHEMI;HAMZEH, 2012; CANNIS-TRACI; ALANIS-LOBATO; RAVASI, 2013;KEMAL;TSUYOSHI et al, 2014;MALLEK et al, 2015;DAMINELLI et al, 2015;DING et al, 2016;MA et al, 2016;KUANG;YU, 2016;CAIYAN;BISWAS;BISWAS, 2017). Because some researches have shown that community information can drastically improve the link prediction accuracy XU, 2012;, most of the research efforts have been directed to better explore this information source.…”