Botification of the Twitterary Protest Poetry: @Protestitas' Protestitas

The paper examines the Twitter bot @Protestitas, which algorithmically generates Spanish visual protest poetry called "Protestitas" (TinyProtests). It explores how the bot denounces socio-economic injustices associated with U.S. neoliberal and right-wing policies, foregrounding human rights for marginalized groups (African-Americans, Latinxs, LGBTQ people, women, school children, Internet users during Covid-19 lockdown, among others). The study situates the bot within digital poetry and multimodal distant writing, focusing on its use of twittexts and emojis to convey perceptual emotions (anger, sorrow, fear) and political slogans, and considers whether its emoji-laden, programmatic expression comments on the performative nature of activism.

The discussion contextualizes @Protestitas within emoji-based and Twitterbot literary practices. It references Fred Benenson’s Emoji Dick (2010) and Jamie Rector’s Shakespeare Brand project (2015) as antecedents in emoji summarization and visual literary branding. It compares @Protestitas with other TwitterArt/digital poetry bots, including @tiny_star_field, @petitsmotifs, @infinitedeserts, @tiny_astro_naut, @tiny_cityscapes, @tiny_gardens, @tinyrelations, @tinyneighbor, @tinyspires, and @TinyAdv, highlighting differences in ASCII art versus emoji usage and in complementing versus constituting text. Bibliography includes Flores (2018) on @protestitas and Mertens (2004) on "algo-literature."

The author conducts a critical examination of the bot’s behavior and source code (available via Cheap Bots, Done Quick). Methods include: observing the bot’s automated following behavior; analyzing generation schedules and frequencies on Twitter versus the Taper website; quantifying stanza output per day and per year; inspecting the code to identify thematic lists of slogans; and interpreting the visual rhetoric of emoji arrangements alongside micro-text slogans. The study compares performance across platforms (Twitter vs. Taper) and considers technical dependencies (web browser, data source, device memory) affecting generation intervals.

• @Protestitas generates 8 to 24 unique stanzas daily at intervals of 1 to 3 hours, totaling approximately 2,920 to 8,760 stanzas annually.
• On Taper, a new stanza (English or Spanish) appears about every 2 seconds, indicating faster sequential readability compared to Twitter’s slower schedule.
• The bot automatically follows accounts that mention its name, enabling interaction without formal subscription.
• Each stanza pairs a set of emojis depicting urban and natural scenes and masses of emoji-people with a political slogan and a unique variable micro-text (hetero-metrical monostich).
• Visual rhetoric suggests gated spaces and excluded publics, conveying emotions such as anger, sorrow, and fear.
• The bot’s thematic source code centers on ten slogan categories: gun control (guncontrolslogans), Puerto Ricans (PRslogans), USA (USslogans), women (womenslogans), science (scienceslogans), education (educationslogans), University of Puerto Rico (UPRslogans), daily routines (NORMALshortslogans), Covid-19 responses (respuestacovid), and general responses (respuesta).
• The Spanish version addresses Puerto Ricans; the English version addresses the U.S. government.
• The work fits digital poetry, twitterature, and "algo-literature" and can be read as world literature due to wide translation, accessibility, and affordability.
• Emoji usage may introduce ambiguity, potentially producing ironic readings and commentary on performative activism; the bot is framed as a botification of Puerto Rican urban street protests.

Findings show how algorithmic protest poetry on Twitter can articulate sociopolitical critique through multimodal forms, blending emojis with micro-slogans to make activism legible and widely distributable. The bot’s design foregrounds marginalized voices and post-colonial concerns, while its automated interaction model amplifies reach. Differences in generation frequency across platforms affect reader engagement and sequential reading experiences. The analysis of thematic categories reveals intentionality in targeting specific issues (e.g., gun control, education, Covid-19). Consideration of emoji ambiguity raises questions of sincerity versus irony, suggesting that the bot also comments on the performativity of online activism. Comparisons with other bots delineate @Protestitas’ perceptual uniqueness within TwitterArt and digital poetry.

The study highlights @Protestitas as a distinctive example of Twitterbot protest poetry that leverages emojis and concise slogans to address socio-political issues globally. It contributes to understanding digital, algorithmic literature’s capacity for activism and multimodal expression, and underscores the bot’s readability, code availability, and thematic breadth. The author recommends @Protestitas’ Protestitas to digital literature scholars and students for further exploration and critical code studies. Future research could include comparative analyses with other protest bots, empirical reader-response studies on emoji rhetoric and irony, multilingual translation impacts, and technical performance evaluations across platforms.

The bot’s slow generation intervals on Twitter (1–3 hours) can hinder sequential reading within a single day. Performance depends on browser, data source, and device memory, which may affect consistency. Emoji-based expression introduces ambiguity that can be read as superficial or ironic, complicating interpretation. The study is primarily observational and code-focused without formal user studies or quantitative evaluations of audience impact.