Ladies First: Analyzing Gender Roles and Behaviors in Pinterest

By Raphael Ottoni, João Paulo Pesce, D. Casas, Geraldo Franciscani, Wagner Meira Jr, Ponnurangam Kumaraguru, Virgílio A. F. Almeida. 2013

109 citations recorded by the source corpus.

Online social networks (OSNs) have become popular platforms for people to connect and interact with each other. Among those networks, Pinterest has recently become noteworthy for its growth and promotion of visual over textual content. The purpose of this study is to analyze this image-based network in a gender-sensitive fashion, in order to un- derstand (i) user motivation and usage pattern in the network, (ii) how communications and social interactions happen and (iii) how users describe themselves to others. This work is based on more than 220 million items generated by 683,273 users. We were able to find significant differences w.r.t. all mentioned aspects. We observed that, although the network does not encourage direct social communication, females make more use of lightweight interactions than males. Moreover, females invest more effort in reciprocating social links, are more active and generalist in content generation, and describe themselves using words of affection and positive emotions. Males, on the other hand, are more likely to be specialists and tend to describe themselves in an assertive way. We also observed that each gender has different interests in the network, females tend to make more use of the network's commercial capabilities, while males are more prone to the role of curators of items that reflect their personal taste. It is important to understand gender differences in online social networks, so one can design services and applications that leverage human social interactions and provide more targeted and relevant user experiences.

Subfields: Clinical Psychology, Museology, Urban Studies

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In Clinical Psychology

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “For instance, Mislove et al. (2011) found that Twitter users signiﬁcantly over-represent men and urban populations, while women tend to be over-represented on Pinterest (Ottoni et al., 2013).”
Of Pins and Tweets: Investigating How Users Behave Across Image- and Text-Based Social Networks (2014, background): “Other work (Ottoni et al. 2013) has shown that behavior on Pinterest differs significantly by gender.”
Open challenges for data stream mining research (2014, methodology): “Recently, model selection methods have been proposed that do not require internal crossvalidation, but rather use the Bayesian machinery to design regularizers with data dependent priors [20].”
Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2016, background): “…that on many social media platforms men and women continue to differ in their interpersonal communication styles (Kivran-Swaine et al. 2012), what they present or conceal about themselves (Quercia et al. 2012), and their online behavior (Kim, Sin, and He 2013; Thelwall 2008; Ottoni et al. 2013).”