Online Library

Because social media sites are self-regulating, each site has developed its own community standards, which serve as regulatory tools. However, the processes of content moderation are often unclear, subjective, and discriminatory. Drawing from a series of interviews with individuals in the “Men Are Scum” movement, this article describes the experiences of women who have been censored on Facebook and explores whether self-regulatory processes on this platform are distinctly gendered. It asserts that both explicit censorship (e.g., limited displays of the body) and implicit censorship (e.g., rampant and unchecked hate speech silencing women’s voices) are operatives on Facebook, limiting women’s expressive potentiality. Thus, this article proposes the term “gender-based censorship” as a lens through which to understand women’s experiences on Facebook. These findings help reveal the pitfalls of industry self-regulation in which profit motives are prioritized over the protection of users (especially those who may be marginalized offline).

VOX-Pol Blog Post.

Ephemeral digital trace data can decrease the completeness, reproducibility, and reliability of social media datasets. Systematic post deletions thus potentially bias the results of computational methods used to map actors, content, and online information diffusion. Therefore, the aim of this study was to assess the extent and distribution of message deletion across different data types using data from the hybrid messenger service Telegram, which has experienced an influx of deplatformed users from mainstream social media platforms. A repeatedly scraped sample of messages from public Telegram groups and channels was used to investigate the effect of message ephemerality on the consistency of Telegram datasets. The findings revealed that message deletion introduces biases to the computational collection and analysis of Telegram data. Further, message ephemerality reduces dataset consistency, the quality of social network analyses, and the results of computational content analysis methods, such as topic modeling or dictionaries. The implications of these findings for scholars aiming to use Telegram data for computational research, possible solutions, and contributions to the methodological advancement of studying online political communication are discussed further in this article.

Mobilization is central to the emergence, survival and success of armed groups challenging the state, and has lately expanded to new arenas with the rise of social media. Using a new dataset of rebel group Twitter use, we examined the topics contained in rebel group social media communications to understand how different messaging strategies impact civilian engagement with rebel messages. Rather than benefiting solely from direct calls to action, we found that rebel groups also increased civilian engagement through indirect messages of self-promotion. While direct appeals received more engagement than indirect appeals, their effects were tempered by audience fatigue when relied on too heavily. We additionally found that including images further enhanced the impact of a mobilizing message. These findings expanded our understanding of rebel communications and mobilization, with important implications for combating the use of social media as a recruitment tool for violent extremism.

VOX-Pol Blog post.

VOX-Pol Blog post.