What is a flag for? Social media reporting tools and the vocabulary of complaint

By Kate Crawford, Tarleton Gillespie. New Media & Society, 2016

387 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/96e8d4d6f325b9173fba4d17aadf1c5c6f48edbe

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Citations in the corpus

Challenges and frontiers in abusive content detection (2019, background): “However, the intention of speakers is difficult to discern using socially-generated data and may not directly correspond with their actions (Crawford & Gillespie, 2016; Margetts, John, Hale, & Yasseri, 2015).”
Disproportionate Removals and Differing Content Moderation Experiences for Conservative, Transgender, and Black Social Media Users: Marginalization and Moderation Gray Areas (2021, background): “Removals based on user reporting and fagging can act to favor majority norms and against marginalized groups, and harm the latter disproportionately [19, 35, 102].”
Like Trainer, Like Bot? Inheritance of Bias in Algorithmic Content Moderation (2017, background): “Large platforms often have teams dedicated to identifying and removing offensive content, while also relying on users flagging content, both as means of detection and as ‘rhetorical justification’ for censorship [11].”
The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support (2021, background): “Flagging mechanisms utilize volunteers to assess which content should be taken down [34, 103].”
Expanding the debate about content moderation: Scholarly research agendas for the coming policy debates (2020, neutral): “Without the push from civil society, academics, and reporters to take up their revelations and make content moderation more transparent (Crawford & Gillespie, 2016), the impact of such decisions would have never been rendered so clearly.”
What do we mean when we talk about transparency? Towards meaningful transparency in commercial content moderation (2019, background): “Some platforms have granted additional powers to particular trusted groups to allow them to more easily flag a larger volume of posts or access expedited moderation processes (Crawford & Gillespie, 2014).”
Online Harassment and Content Moderation (2018, background): “ese mechanisms include using a small number of human moderators who manually remove abusive posts [25], moderating through a voting mechanism where registered users up-vote or down-vote each submission [27], and allowing users to ag abusive content [11].”
Instagrammatics and digital methods: studying visual social media, from selfies and GIFs to memes and emoji (2016, background): “…users posting it, who view it as mundane or artistic, but by the platforms and other users (and thus the media might be ﬂ agged and taken down; see Crawford & Gillespie, 2014): this includes content featuring breastfeeding and menstruation, and which have generated their own visual responses and…”
Human-Machine Collaboration for Content Regulation: The Case of Reddit Automoderator (2019, background): “Finally, platforms rely on regular users to flag content that offends readers or violates the community rules [26].”
But Who Protects the Moderators? The Case of Crowdsourced Image Moderation (2018, background): “While all potentially problematic content ﬂagged by users or algorithms could be removed, this would also remove some acceptable content and could be further manipulated (Crawford and Gillespie 2016; Rojas-Galeano 2017).”
When Are Search Completion Suggestions Problematic? (2020, methodology): “For the remaining of our analysis, we consider a <prefix> [<suffix>] pair problematic if at least one judge marked it as such—following a reporting model used in applications where user complaint reports are reviewed irrespective of how many users submit them [14, 48].”
Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms (2018, neutral): “At the moment, users remain critical to the functioning of content moderation: social media companies rely on users flagging content they deem objectionable in order to identify what needs to be removed (Crawford and Gillespie, 2016).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, methodology): “Taking into account the iterative nature of software development, we may also question how the system has changed over time, why it changed, under what circumstances, and how extensive those changes were [36, 43, 110, 136].”