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As a social media platform, Instagram has a strong influence on youth culture, identity, and perceptions of the world, with the application serving not only for youth to follow accounts that are aspirational but also for entertainment and identity building through memes. Meme accounts that are explicitly conservative and that espouse white supremacist, hateful ideology and subsequently, identity, are incredibly prevalent. Media serve as powerful institutions for the socialization of youth, and content on the platform reveals that memes are serving as building blocks of ideological meaning. This study conducted a discourse analysis of the memes and content circulated by the alt-right affiliate movement the ‘Proud Boys,’ which is being sold to young men as a fraternity-like organization to celebrate ‘Western ideals’. Proud Boys operate on an ideology that consists of both symbolic and physical violence, and the popularity of these groups is growing. Using Bourdieu’s work on language as a framework, this article is an exploration to their recruitment and world-building practices on Instagram using memes and will be necessary to understand the movement, and to gain further insight into how memes are being used as propaganda.

The far right is increasingly relying on visual and less extreme online communication, for instance by using memes, to strategically mainstream their ideology. The use of humor in particular renders their communication more relatable to a mainstream audience. However, little is known about the actual impacts of the different content characteristics they employ to become more appealing, in particular on less moderated platforms that function as safe online spaces for extremist ideology and contents. To fill this gap, we conducted a manual quantitative content analysis of 1,200 memes distributed within German-language far-right Telegram channels in 2020 and 2021, concentrating on humor and several content-related factors to analyze their impact on meme reach. The results demonstrate that memes with extreme far-right narratives and memes with humor received fewer views than others, but that memes with both far-right narratives and humor had a significantly increased reach. The findings highlight the mainstreaming potential of humor, particularly when used to mask extreme content that would otherwise be less appealing.

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.

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.