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Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media. 

Algospeak refers to social media users intentionally altering or substituting words when creating or sharing online content, for example, using ‘le$bean’ for ‘lesbian’. This study discusses the characteristics of algospeak as a computer-mediated language phenomenon on TikTok with regards to users’ algorithmic literacy and their awareness of how the platform’s algorithms work. We then present results from an interview study with TikTok creators on their motivations to utilize algospeak. Our results indicate that algospeak is used to oppose TikTok’s algorithmic moderation system in order to prevent unjust content violations and shadowbanning when posting about benign yet seemingly unwanted subjects on TikTok. In this, we find that although algospeak helps to prevent consequences, it often impedes the creation of quality content. We provide an adapted definition of algospeak and new insights into user-platform interactions in the context of algorithmic systems and algorithm awareness. 

Online toxicity is ubiquitous across the internet and its negative impact on the people and that online communities that it effects has been well documented. However, toxicity manifests differently on various platforms and toxicity in open source communities, while frequently discussed, is not well understood. We take a first stride at understanding the characteristics of open source toxicity to better inform future work on designing effective intervention and detection methods. To this end, we curate a sample of 100 toxic GitHub issue discussions combining multiple search and sampling strategies. We then qualitatively analyze the sample to gain an understanding of the characteristics of open-source toxicity. We find that the pervasive forms of toxicity in open source differ from those observed on other platforms like Reddit or Wikipedia. In our sample, some of the most prevalent forms of toxicity are entitled, demanding, and arrogant comments from project users as well as insults arising from technical disagreements. In addition, not all toxicity was written by people external to the projects; project members were also common authors of toxicity. We also discuss the implications of our findings. Among others we hope that our findings will be useful for future detection work. 

This paper presents the results of an interview study with twelve TikTok users to explore user awareness, perception, and experiences with the app’s algorithm in the context of privacy. The social media entertainment app TikTok collects user data to cater individualized video feeds based on users’ engagement with presented content which is regulated in a complex and overly long privacy policy. Our results demonstrate that participants generally have very little knowledge of the actual privacy regulations which is argued for with the benefit of receiving free entertaining content. However, participants experienced privacy-related downsides when algorithmically catered video content increasingly adapted to their biography, interests, or location and they in turn realized the detail of personal data that TikTok had access to. This illustrates the tradeoff users have to make between allowing TikTok to access their personal data and having favorable video consumption experiences on the app.