Not everyone deserves their own Wikipedia page; that’s why Wikipedia’s notability guidelines exist. But definitions of notability are unevenly applied across race and gender lines:
Wikipedia’s editors are less likely to consider you “notable” if you’re not a white man.
Using a combination of qualitative and statistical analysis of Wikipedia pages nominated for deletion, Mackenzie Emily Lemieux and Rebecca Zhang join with Francesca Tripodi to explore how Wikipedia’s notability considerations are applied for female and BIPOC academics. They examined two key metrics used in the process of establishing notability on Wikipedia: the Search Engine Test and the “Too Soon” metric. The search engine test determines if a person’s online presence is well covered by reputable, independent sources. In their analysis, Lemieux, Zhang, and Tripodi found that this test predicts whether or not white male academics’ pages will be kept or deleted. But academics who are women and people of color are more likely to have their Wikipedia page deleted—even if they have equivalent or greater online presence than their white male peers.
The second metric, “too soon,” is a label applied to Wikipedia pages when a Wikipedian thinks there aren’t enough independent, high quality news sources about the page’s subject. Women of all races are more likely than men to be considered not yet notable (i.e., “too soon” to be on Wikipedia). The online encyclopedia’s editors were more likely to justify this label applied to women based on their career stages (e.g., “she’s an assistant professor” and therefore not yet notable). But this tag was applied to women on average further in their careers than men who received the tag. Individual bias continues to disadvantage women and people of color on Wikipedia; and Wikipedia continues to allow these hidden biases to influence processes of determining notability.
Right-leaning outlets reach more people - even within the confines of online activist networks built to enact change and oppose dominant ideologies
We analyze social media activity during one of the largest protest mobilizations in US history to examine ideological asymmetries in the posting of news content. Using an unprecedented combination of four datasets (tracking offline protests, social media activity, web browsing, and the reliability of news sources), we show that there is no evidence of unreliable sources having any prominent visibility during the protest period, but we do identify asymmetries in the ideological slant of the sources shared on social media, with a clear bias towards right-leaning domains. These results support the “amplification of the right” thesis, which points to the structural conditions (social and technological) that lead to higher visibility of content with a partisan bent towards the right. Our findings provide evidence that right-leaning sources gain more visibility on social media and reveal that ideological asymmetries manifest themselves even in the context of movements with progressive goals.
Deploying visual "Asian-ness" can create racial solidarity - sometimes at the expense of cross-racial solidarity
This article examines how uses of ‘Asian-ness’ as racial presence becomes used discursively and visually to form affective racial counterpublics around #Asians4BlackLives/#Justice4AkaiGurley and #SavePeterLiang/#Justice4Liang. Specifically, Rachel Kuo looks at how Asian American racial positioning becomes deployed to produce feelings of solidarity. Approaching hashtags as both indexical signifiers of solidarity and as an indexing system that archives together an array of media objects, she tracks media objects across multiple sites to examine visual modes of storytelling that affectively mobilize publics and investigate solidarity as discursively mediated, embodied, and affective phenomena. Kuo closely examines how #SavePeterLiang protestors create narratives of victimization in response to the singularity of Liang’s racial body and how the #Asians4BlackLives selfie project uses representational visibility to activate affective politics.