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

By Mehmet Fatih Çömlekçi. 2019

1,488 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

The Gradient of Generative AI Release: Methods and Considerations (2023, background): “A base generative AI system is capable of many types of content, making content moderation complex [36].”
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].”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “Mainstream 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].”
ORES (2019, background): “In a world increasingly dominated by for-profit content platforms — often marketed by their corporate owners as “communities” [16] — Wikipedia is an anomaly.”
Quantifying the Invisible Labor in Crowd Work (2021, background): “For instance, crowd workers might label content so that Facebook’s News Feed algorithm will not recommend posts that are filled with hate speech or pedophilia [17, 46].”
Content moderation, AI, and the question of scale (2020, background): “For the now dominant social media platforms, community-scale techniques have become increasingly untenable and unconvincing (Gillespie, 2018).”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “Mainstream 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 [48].”
Nathaniel Persily e Joshua A. Tucker (a cura di). Social Media and Democracy. The State of the Field, Prospects for Reform (2021, background): “…are shared, widely accessible systems that are used by a wide range of actors for many different purposes) (Plantin et al. 2016), so far in ways primarily governed by inherited legal and regulatory frameworks and individual companies’ own sense of mission and terms of service (Gillespie 2018).”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “If these systems are trained on biased data, they may disproportionately censor or suppress the voices of certain groups, while allowing harmful content or misinformation from other sources to proliferate [114, 115].”
Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray Areas (2021, background): “However, moderation becomes more visible for some users who face frequent removals [43, 115].”
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective (2020, background): “Traditionally, harmful content has been detected by human moderators or flagged by users (Gillespie, 2018).”
Algorithmic content moderation: Technical and political challenges in the automation of platform governance (2020, background): “…in the Global South); democratic legitimacy concerns (about global speech rules being set by a relatively homogenous group of Silicon Valley elites); and process concerns about the overall lack of transparency and accountability (see Gillespie, 2018; Kaye, 2019; Roberts, 2018; Suzor et al., 2019).”
Expanding the debate about content moderation: Scholarly research agendas for the coming policy debates (2020, neutral): “In the past few years, scholars, activists, and journalists have done invaluable work that has helped us further understand the complex infrastructures of content moderation (Gillespie, 2018; Roberts, 2019; Suzor, 2019).”
The Society of Algorithms (2021, background): “The smooth functioning of on-demand apps, search engines, mapping sites, social media websites, and even autonomous vehicles and many other products all depend on the collective intelligence of armies of humans performing ghost work (Gray & Suri 2019; also see Gillespie 2018,Roberts 2019).”
Platformisation (2019, background): “On the basis of these terms and guidelines, platforms moderate what end-users and complementors can share and how they interact with each other (Gillespie, 2018).”
The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support (2021, background): “While moderation work is relatively unskilled, it is simultaneously ”one of the most crucial jobs created by the internet economy” [106] and lies at the very essence of what social media platforms offer today [59, 129], yet it is largely taken for granted.”
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (2019, background): “Constant exposure to abusive content has been found to negatively and substantially affect the mental health of moderators and users (Roberts, 2014; Gillespie, 2018; Saha et al., 2019).”
What do we mean when we talk about transparency? Towards meaningful transparency in commercial content moderation (2019, background): “Particularly for social media platforms, the decisions that providers make are increasingly the subject of public interest, and a number of major controversies have erupted over the last decade as different stakeholders try to understand and influence how content is moderated online (Gillespie, 2018”