Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media

By Tarleton Gillespie. 2018

171 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Explainable machine learning in deployment (2019, background): “Though there are aspects of these use cases that are clearly in users’ interest, there are others where that is much less clear, with potential for adverse eﬀects both when these systems work correctly and when they err [25, 38, 60].”
Towards Transparency by Design for Artificial Intelligence (2020, background): “An example of transparency struggles when it comes to ADM is the domain of content moderation on social media (Gillespie 2018; Roberts 2019).”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “Main-stream platforms have made efforts in recent times to ‘clean up’ content and so the most overt and aggressive forms of abuse, such as direct threats, are likely to be taken down [89].”
Crossmod: A Cross-Community Learning-based System to Assist Reddit Moderators (2019, background): “Moderators are critical to platforms that rely heavily on human moderation, acting as digital gatekeepers who decide what content gets posted on the platform and what content gets taken down [29].”
Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator (2019, background): “In recent years, researchers have begun asking questions about the democratic accountability of platform companies and their role in the realization of important public values like freedom of expression, transparency, diversity, and socio-economic equality [39, 43, 46, 52, 103, 109].”
What do we mean when we talk about transparency? Towards meaningful transparency in commercial content moderation (2019, background): “The concept of transparency, however, is often used in a general sense; Gillespie (2018) goes so far as to say that “calls for greater transparency in the critique of social media are so common as to be nearly vacant” (p. 212).”
Detecting weak and strong Islamophobic hate speech on social media (2018, background): “It could also enable platforms and governments to better regulate and monitor social media and develop more nuanced content moderation and intervention strategies (Gillespie, 2018).”
ORES (2019, background): “heir users and those who are affected. 8.1 Participatory machine learning In a world increasingly dominated by for-profit content platforms — often marketed by their corporate owners as “communities” [16] — Wikipedia is an anomaly. While the non-profit Wikimedia Foundation has only a fraction of the”