<ul><li>Emojis are often lost in noisy or garbled text on social media, posing challenges for data analysis and machine learning.</li><li>A three-step reverse-engineering methodology is proposed to retrieve emojis from garbled text in social media posts.</li><li>The methodology was applied to 509,248 tweets about the Mpox outbreak, retrieving 157,748 emojis from 76,914 tweets.</li><li>Improvements in text readability and coherence were demonstrated through various metrics, and the usage patterns of individual emojis were analyzed.</li></ul>

Emoji Retrieval from Gibberish or Garbled Social Media Text: A Novel Methodology and A Case Study

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