The analysis of Wordle under data mining

Xingguo Xu; Yiqiang Xia

doi:10.1117/12.3011847

7 December 2023 The analysis of Wordle under data mining

Xingguo Xu, Yiqiang Xia

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129411T (2023) https://doi.org/10.1117/12.3011847
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The New York Times presents Wordle, a word game where players guess a 5-letter word in six attempts, receiving feedback after each guess. This paper presents a comprehensive study using an ARIMA time series prediction model to forecast the number of reports in the Wordle game, leveraging autocorrelation, lag, and averaging of data to make accurate predictions. Additionally, four-word attributes potentially influencing report numbers are extracted and correlated. OLS regression models and Pearson correlation coefficients are employed to analyze the impact of these attributes, highlighting the frequency of solution words and the number of vowels as significant factors in predicting the results, while other effects were found to be negligible. The research findings are validated through statistical tests, offering valuable insights into Wordle game dynamics.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingguo Xu and Yiqiang Xia "The analysis of Wordle under data mining", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129411T (7 December 2023); https://doi.org/10.1117/12.3011847

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