Emojis are widely used across social media platforms but are often lost in noisy or garbled text, posing challenges for data analysis and machine learning. Conventional preprocessing approaches recommend removing such text, risking the loss of emojis and their contextual meaning. This paper proposes a three-step reverse-engineering methodology to retrieve emojis from garbled text in social media posts. The methodology also identifies reasons for the generation of such text during social media data mining. To evaluate its effectiveness, the approach was applied to 509,248 Tweets about the Mpox outbreak, a dataset referenced in about 30 prior works that failed to retrieve emojis from garbled text. Our method retrieved 157,748 emojis from 76,914 Tweets. Improvements in text readability and coherence were demonstrated through metrics such as Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, Automated Readability Index, Dale-Chall Readability Score, Text Standard, and Reading Time. Additionally, the frequency of individual emojis and their patterns of usage in these Tweets were analyzed, and the results are presented.

Nonetheless, properly constructed comparisons designed to reconcile the two datasets yield up to 90% agreement (e.g., in South America). The Comment by Hansen et al. (1) provides the opportunity to distinguish our research, which quantifies dynamics in carbon density, from studies focused on the binary classification of changes in forest area (2). We use a multisensor (ICESat/MODIS), multistage approach combined with field measurements to map net change (i.e., losses and gains) in carbon density for the period 2003–2014 for each 463 m 463 m (21.4 ha) pixel in our dataset. Within each pixel, dynamic processes occurring at both the tree and stand level are necessarily considered in aggregate, meaning that losses and gains are happening always and concurrently wherever woody biomass is present. A loss is registered when losses are greater than gains, and vice versa.