Mastodon Content Warnings: Inappropriate Contents in a Microblogging Platform

Abstract

Our social communications and the expression of our beliefs and thoughts are becoming increasingly mediated and diffused by online social media. Beyond countless other advantages, this democratization and freedom of expression is also entailing the transfer of unpleasant offline behaviors to the online life, such as cyberbullying, sexting, hate speech and, in general, any behavior not suitable for the online community people belong to. To mitigate or even remove these threats from their platforms, most of the social media providers are implementing solutions for the automatic detection and filtering of such inappropriate contents. However, the data they use to train their tools are not publicly available.In this context, we release a dataset gathered from Mastodon, a distribute online social network which is formed by communities that impose the rules of publication, and which allows its users to mark their posts inappropriate if they perceived them not suitable for the community they belong to. The dataset consists of all the posts with public visibility published by users hosted on servers which support the English language. These data have been collected by implementing an ad-hoc tool for downloading the public timelines of the servers, namely instances, that form the Mastodon platform, along with the meta-data associated to them. The overall corpus contains over 5 million posts, spanning the entire life of Mastodon. We associate to each post a label indicating whether or not its content is inappropriate, as perceived by the user who wrote it. Moreover, we also provide the full description of each instance. Finally, we present some basic statistics about the production of inappropriate posts and the characteristics of their associated textual content.

Shifting your research from X to Mastodon? Here’s what you need to know

“…For example, in 2019 researchers published a paper called “Mastodon Content Warnings: Inappropriate Contents on a Microblogging Platform” that studied the use of the “content warning” feature of Mastodon, where users can add a text describing the nature of their post. 6 The study relied on the collection of a large sample of posts that had been accessed through instance APIs and was released as part of the paper. After reading the study, members of the fediverse, both technically savvy and academically trained, wrote a scathing open letter to the university arguing for the retraction of the study based on several flaws with the study.…”

The Pushshift Reddit Dataset

“…For example, (Fair and Wesslen 2019) released a dataset that includes 37M posts and 24M comments covering August 2016 through December 2018 from Gab, a Twitter-like social media platform that after being de-platformed by major service providers ported their codebase to use the federated social network protocol from the Mastodon project. As it turns out, (Zignani et al 2019) released a dataset focused around Mastodon itself. Their dataset contains 5M posts, along with a crowdsourced (by Mastodon users) label that indicates whether or not the post contains inappropriate content.…”

The MeLa BitChute Dataset

“…Similar platformwide datasets are available for other platforms, most notably the Pushshift.io Reddit dataset (Baumgartner et al 2020). Unlike Reddit, BitChute is part of the alt-tech space and fits into a constellation of recent work studying these alternative spaces, including releases from Parler (Aliapoulios et al 2021), Gab (Fair and Wesslen 2019), and Mastodon (Zignani et al 2019). Paralleling YouTube's centrality in the mainstream information ecosystem, BitChute and its videohosting is likewise a core element of the alt-tech space and is often a highly shared domain in other fringe platforms and Telegram channels.…”