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Quantitative Content Analysis of Cryptocurrency Memes: Bitcoin versus Ethereum

The study aims to explore memes, which serve as a reflection of digital culture, in order to illuminate the distinctions in perception and adoption of the two leading cryptocurrencies, Bitcoin and Ethereum.

By leveraging the Social Construction of Technology (SCOT), Framing, and Visual Framing theories, this study aims to address four key Research Questions in order to conduct a comparative analysis of Bitcoin and Ethereum memes. The research focuses on exploring the following aspects: (1) technological frames, (2) involved actors, (3) media frames, and (4) the overall connotations conveyed through these frames. This approach provides a deeper understanding of these cryptocurrencies and their respective meme cultures.

A manual quantitative content analysis approach was employed to examine a sample of 325 memes on Bitcoin and Ethereum from the Google Image database. The codebook, consisting of 17 categories, was tested for reliability. Various evaluation aspects were examined using descriptive statistics and a one-sided chi-square test at a 5% significance level.

While Bitcoin memes had more media frames due to its wider recognition, both cryptocurrencies were viewed as revolutionary innovation for traditional finance. Both cryptocurrencies utilize similar visual framing techniques to convey their messages. Ethereum memes emphasize its connection to other technologies and tools, while Bitcoin’s association with blockchain has diminished in digital culture. Overall, frames convey optimism in Bitcoin and Ethereum memes, reflecting the crypto industry’s trend and downplaying risks.

This thesis provides a foundation for future research to analyze the digital culture surrounding cryptocurrencies, with potential studies focusing on longitudinal analysis, qualitative content analysis, and examining memes in other languages or cryptocurrencies. This study underscores the importance of cryptocurrency education, due to the prevalence of complexity.