Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility

Abstract

Social media is a new public sphere where people can, in principle, communicate with each other regardless of their status. However, social categories like gender may still bias online communication, replicating offline disparities. Examining over 94,000 Twitter users, we investigate the association between perceived gender and measures of online visibility: how often Twitter users are followed, assigned to lists, and retweeted. Our analysis shows that users perceived as female experience a 'glass ceiling,' similar to the barrier women face in attaining higher positions in companies. For users in lower quartiles of visibility, being perceived as female is associated with more visibility; however, this tendency flips among the most visible users where being perceived as male is strongly associated with more visibility. Our results suggest that gender presented in social media profiles likely frame interactions as well as perpetuates old inequalities online.

The Effect of Homophily on Disparate Visibility of Minorities in People Recommender Systems

“…In contrast, in our study we consider more properties of the nodes (such as the group they belong to and the visibility they obtain), in addition to characteristics such as node degree that have been previously studied. Nilizadeh et al (2016) were able to prove a glass ceiling effect in social networks. They investigated how perceived gender and online visibility can be linked, showing that users perceived as female experience a "glass ceiling" effect, similar to the one that makes it harder for women to reach higher positions in companies.…”

“…Homophily is a well-known phenomenon in network science and can be expressed as the tendency of people to connect to similar people, or in our case, of people in a group to connect to people in the same group. We measure homophily with respect to a random configuration, inspired by work analyzing dyadicity in signed networks (Park and Barabási 2007):…”

“…Similar findings were reported by Daly, Geyer, and Millen (2010), who conducted a large-scale user study on IBM's Social-Blue social network site. While these two works focus on the inequalities at the level of individual users, some authors have analysed a glass ceiling 1 effect for women in social networks (Nilizadeh et al 2016). For instance, Stoica, Riederer, and Chaintreau (2018) investigate the role of gender in organic and artificial growth of social networks, using a large social graph from Instagram, where women are the majority.…”

FairLP: Towards Fair Link Prediction on Social Network Graphs

“…(Fabbri et al 2020) also investigate the effect of homophily on visibility of minorities in people recommender systems, and find that homophily plays a key role in the disparate visibility of different groups. (Nilizadeh et al 2016;Stoica and Chaintreau 2018) show that biased recommendations can bring the glass ceiling effect, which affects female groups negatively.…”

“…To our best knowledge, our work presents the first comprehensive study of accuracy disparity in existing link prediction algorithms. Furthermore, while existing fairness-aware link prediction methods (Masrour et al 2020;Rahman et al 2019;Nilizadeh et al 2016;Lee et al 2019;Karimi et al 2018) are limited to specific link prediction algorithms, we design the first bias mitigation algorithm that is compatible with most of existing link prediction algorithms. Our contributions are summarized as follows.…”