The emerging science of content labeling: Contextualizing social media content moderation

Abstract

In the online information ecosystem, a content label is an attachment to a piece of content intended to contextualize that content for the viewer. Content labels are information about information, such as fact‐checks or sensitive content warnings. Research into content labeling is nascent, but growing; researchers have made strides toward understanding labeling best practices to deal with issues such as disinformation, and misleading content that may affect everything from voting to health. To make this review tractable, we focus on compiling the literature that can contextualize labeling effects and consequences. This review summarizes the central labeling literature, highlights gaps for future research, discusses considerations for social media, and explores definitions toward a taxonomy. Specifically, this article discusses the particulars of content labels, their presentation, and the effects of various labels. The current literature can guide the usage of labels on social media platforms and inform public debate over platform moderation.

Fake news reminders and veracity labels differentially benefit memory and belief accuracy for news headlines

“…Characterizing veracity label effects on belief accuracy is a focus of the nascent content labeling literature 4 . Prior work has shown that veracity labels are more effective at improving belief accuracy when they appear after rather than during or before fake news exposure 23 .…”

“…Reminding people of real-world fake news before correcting it can substantially enhance memory and belief accuracy 3 . Additionally, veracity labels about the ground truth of news headlines may reduce false beliefs and sharing behaviors 4 . However, we know virtually nothing about how updating memory and beliefs for factual information compares for correction methods using fake news reminders and veracity labels.…”

Content takedowns and activist organizing: Impact of social media content moderation on activists and organizing

“…With it, content moderation practices have also evolved (Morrow et al, 2022). However, social media companies, users, and regulators have different and sometimes competing motivations when it comes to content moderation (Morrow et al, 2022). As such, these practices have been criticized as inconsistent and lacking transparency (Ananny & Gillespie, 2016; Morrow et al, 2022) as they tend to produce significant errors that directly impact users (West, 2018).…”

“…The secondary goal of this paper is to make a case for social media companies to develop features for individuals who are wrongfully marginalized on their platforms to be notified about and to appeal incidences of censorship and content removal. To do so, this paper will begin by discussing common techniques of content moderation including algorithmic content moderation, which various social media platforms have used to moderate content on their sites (Morrow et al, 2022). This paper will then address the impacts of algorithmic content moderation on its users generally, on individuals identifying with marginalized communities, and on activists by reviewing recent literature in this area and by citing recent examples of social movements impacted by social media censorship.…”

Trust in COVID‐19 public health information

“…According to Pluye et al ( 2019 ), the internet has become a common first source for healthcare information for individuals, yet the complexity of health topics and vast number of unfamiliar sources make it challenging to find reliable information (Chi et al, 2020 ). In general, the quality of online health information about COVID‐19 has remained low on account of the novelty of COVID‐19 and the consequent lack of journalistic expertise on the disease (Fan et al, 2020 ), delayed removal of health misinformation because of the large volume of posts and fear of backlash against censoring of user posts (Gisondi et al, 2022 ; Morrow et al, 2022 ), inadequate content moderation and labeling of inaccurate information on social media (Morrow et al, 2022 ), and the algorithmic bias toward incentivizing sensationalist content about COVID‐19 vaccines (Burki, 2020 ). In addition, the speed with which information is created and spread online makes detecting misinformation practically impossible (Singh et al, 2021 ).…”