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Wordle, a popular daily puzzle offered by The New York Times, challenges users to guess a five-letter word within six attempts, with feedback provided through a color-coded system. This study explores the dynamic patterns of player engagement and result reporting on social media platforms such as Twitter, where users frequently post their game outcomes. The primary aim of our research is to understand and predict the variations in the distribution of these reports using a robust modeling approach that incorporates elements from epidemiological modeling. To address the fluctuations in reporting frequency and simulate the influences of game mechanics on player behavior, we employed the SIR (Susceptible, Infected, Recovered) model, commonly used to describe the spread of infectious diseases. This model helped us to conceptualize the spread of player engagement as analogous to an infectious disease, where interest peaks and then diminishes over time. Our adapted "Zombie Model" further refined this approach by including categories such as susceptible humans, infected zombies, and recovered individuals, along with a novel category of 'serum carriers' who exhibit mild infections and recover quickly, offering a nuanced understanding of the engagement lifecycle. Through rigorous data normalization and analysis, we observed patterns consistent with infectious disease spread—a sharp rise in activity followed by a gradual decline. The results from the model not only provided insights into how game design and external factors such as social media influence player interactions but also predicted future trends in player engagement. Our findings contribute to the broader understanding of digital media interaction and can inform future game design to enhance user engagement and retention.
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