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PhD Project Proposal: How AI and Algorithms Are Shaping the Evolution of Digital English Through Memes and Slang

Gezim Joseph Hazizolli April 11, 2025


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Abstract This research explores how artificial intelligence (AI), memes, and algorithms are reshaping the English language in the digital era. By examining AI-generated phrases, social media-driven slang, and the emergence of hybrid English forms, the study investigates how linguistic norms evolve through digital interaction. Employing a multidisciplinary methodology that includes corpus analysis, social media observation, and case studies, this project uncovers the dynamic interplay between human creativity and machine-mediated communication. Key findings reveal the growing influence of AI-generated content on digital vernacular, the linguistic consequences of algorithmic censorship, and the cultural significance of memes in the spread of language trends.

Introduction Language is a living entity, constantly evolving with cultural, technological, and societal shifts. With the rise of artificial intelligence and algorithmic platforms, English is undergoing rapid transformation. This research investigates how memes, AI, and social media algorithms shape the evolution of English, particularly through slang, neologisms, and internet dialects.

Research Problem and Objectives The central problem addressed is the linguistic impact of digital technologies on contemporary English. Objectives include:

  • To examine how AI-generated language influences digital discourse.

  • To identify how memes contribute to linguistic diffusion.

  • To explore how algorithmic moderation fosters new internet dialects.

Research Questions

  • How does AI affect the development of new phrases and digital speech patterns?

  • What role do memes play in spreading and standardizing slang?

  • How do algorithm-driven platforms shape or restrict online language use?

Justification and Relevance to the Field As language scholars grapple with the implications of digital communication, understanding how AI and algorithms influence language is vital. This research offers a contemporary perspective on language change, contributing to fields such as sociolinguistics, digital media studies, and AI ethics.

Literature Review The theoretical foundation includes David Crystal’s work on Internet linguistics and Jean Aitchison’s model of language change. Prior studies have explored the mechanics of meme culture, the formal patterns of AI-generated text, and the sociolinguistics of online communities. However, gaps remain in understanding how these phenomena intersect.

Key Studies Reviewed

  • Crystal (2004): Language and the Internet
  • McCulloch (2019): Internet linguistics in everyday use
  • AI and GPT-era research on computational language patterns

Gaps in Existing Research There is limited empirical data on how AI-generated speech patterns influence real-time human communication or how algorithmic censorship shapes new slang. This study addresses those gaps by focusing on linguistic adaptation in the face of AI moderation and trend amplification.

Research Aims and Objectives

  • To map the most common AI-influenced phrases in digital English.

  • To analyze the role of algorithm-driven censorship in linguistic innovation.

  • To document meme-based linguistic evolution across platforms like TikTok and X.

  • To compare generational differences in the adoption and adaptation of slang.

Hypotheses or Guiding Questions

  • AI contributes to the formalization and uniformity of digital language.

  • Algorithmic censorship encourages the invention of euphemisms and coded speech.

  • Meme culture facilitates linguistic change through humor and virality.

Methodology A mixed-method approach is used:

  • Corpus analysis: Examining AI-generated texts and trending memes.

  • Social media observation: Tracking hashtags, viral content, and linguistic workarounds.

  • Case studies: In-depth look at ChatGPT speech patterns and TikTok censorship effects.

Data Collection Methods

  • Sampling AI outputs from ChatGPT and similar tools

  • Collecting social media posts using trend-specific tags

  • Archiving linguistic memes and workaround terms

Data Analysis Techniques

  • Qualitative coding of neologisms and meme speech

  • Quantitative frequency analysis of AI-phrases

  • Thematic analysis of censorship-induced dialects

Ethical Considerations

  • Ensuring anonymity of public users on social media

  • Avoiding the amplification of harmful or coded speech

  • Compliance with ethical standards for digital research

Findings and Discussion

The AI Effect: How Language is Evolving AI has introduced new linguistic templates into public discourse. Phrases like "As an AI language model…" and "It is worth noting that…" reflect a stylized, formal tone that now appears frequently in digital communication, especially in corporate and academic contexts.

Memes and AI-Driven Linguistic Spread Memes act as cultural containers, accelerating the adoption of slang and AI-modulated language. Templates such as "I am once again asking…" evolve into remixable formats, often mimicked by AI and re-shared across platforms.

Algorithmic Censorship and Linguistic Workarounds Social platforms rely on AI moderation, prompting users to invent euphemisms like "unalive" or "seggs" to bypass detection. These workarounds highlight how users creatively adapt to technological limitations, and how such adaptations often become normalized.

Digital Vernacular and Conversational AI Users now emulate AI tone in both ironic and sincere ways. Common phrases like “According to my calculations…” or meta-comments like “User has entered the chat” reflect a new, hybrid mode of expression.

Generational Shifts in Language Millennials and Gen Z adapt to internet dialects faster, while Gen Alpha grows up with AI-moderated slang as the norm. Differences in slang usage and acceptance reflect generational comfort levels with digital spaces.

Hybrid English and Translation AI Tools like Google Translate and DeepL create recognizable variants of English. These “machine dialects” influence how non-native speakers express themselves online, often leading to odd phrasing that blends formal structure with colloquial tone.

Conclusion and Future Research Language change is inevitable, but in the age of AI, it is more rapid and more influenced by non-human agents than ever before. This research has shown that:

  • AI introduces new styles of communication.

  • Algorithmic moderation fosters creativity in censorship circumvention.

  • Meme culture amplifies and normalizes these trends.

Limitations

  • Limited scope in non-English languages.

  • Temporal nature of trends makes longitudinal analysis difficult.

  • AI outputs vary based on prompt engineering.

Future Research Recommendations

  • Expand into multilingual digital dialects.

  • Examine the long-term effects of AI-modulated education tools.

  • Investigate how AI influences spoken language in addition to written forms.

Digital English, shaped by AI, memes, and algorithmic systems, is evolving into a form that reflects both machine logic and human creativity. This transformation marks the rise of English 2.0—a blend of structured automation and playful innovation, offering a glimpse into the future of human expression in a tech-mediated world.

Primary Texts

  • Tagliamonte, S. A. (2011). Variationist Sociolinguistics: Change, Observation, Interpretation. Wiley-Blackwell.

  • Tilleczek, K., & Campbell, V. (2019). Youth in the Digital Age: Paradox, Promise, Predicament. Routledge.

  • Jaspers, J., & van de Weerd, P. (2021). Sociolinguistic Approaches to Language and Youth. In B. Svendsen & R. Jonsson (Eds.), The Routledge Handbook of Language and Youth Culture. Routledge.

  • Seargeant, P., & Tagg, C. (2014). The Language of Social Media: Identity and Community on the Internet. Palgrave Macmillan.

  • Munro, M. (2007). Chambers Pardon My English! An Exploration of Slang and Informal Language. Chambers Harrap Pub Ltd.

  • Coleman, J. (2012). The Life of Slang. Oxford University Press.

  • Décharné, M. (2017). Vulgar Tongues: An Alternative History of English Slang. Pegasus Books.

  • Eble, C. (2006). Slang and Sociability: In-Group Language Among College Students. University of North Carolina Press.

  • Dalzell, T. (2018). The Routledge Dictionary of Modern American Slang and Unconventional English. Routledge.

  • Coleman, J. (2014). Global English Slang: Methodologies and Perspectives. Routledge.

  • Viljanen, L. (2021). "YOU THE REAL MVP" A Study on 10 English Slang Words and How They Are Used to Describe People on Social Media.

  • McCulloch, G. (2019). Because Internet: Understanding the New Rules of Language. Riverhead Books.

  • Crystal, D. (2008). Txtng: The Gr8 Db8. Oxford University Press.

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Gezim Joseph Hazizolli

Posted by Gezim Joseph Hazizolli

I’m Joe, a children’s book author with a master’s in languages and literature. A proud polyglot—Albanian, English, Italian, and Spanish—I’m also a movie buff and retro game nerd with a soft spot for fantasy and a habit of writing my way through magical realms and pixelated quests.

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