# GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multicolumn{6}{c}{**Practice ID (F1)**} & \multirow{2}{*}{\(\mathbf{F1}_{\text{avg}_{\text{1-avg}}}\)**} & \multirow{2}{*}{\(\mathbf{F1}_{\text{avg}_{\text{1-avg}}}\)**} \\  & & & & & & & & & & & & \\ \multicolumn{11}{c}{Original dataset} \\ \hline Multi-label & **87.28** & 81.24 & 77.87 & 83.68 & 85.01 & 91.48 & 80.14 & **86.73** & 49.93 & 80.37 & 79.51 & 83.74 \\ One-versus-rest & 86.79 & 80.24 & 74.01 & 91.31 & 84.51 & 92.51 & 86.36 & 84.81 & 49.93 & 81.16 & 80.46 & 84.82 \\ One-versus-rest + w & 86.10 & 81.26 & 76.73 & 93.91 & 82.81 & 92.08 & 87.33 & 84.74 & 49.93 & **81.65** & 81.00 & 85.55 \\ \hline \multicolumn{11}{c}{Augmented dataset} \\ \hline Simple duplication & - & 82.14 & 77.24 & 90.16 & 83.56 & 92.35 & 86.24 & 85.52 & 74.96 & - & 84.02\(\uparrow\) & 85.32 \\ Simple duplication + w & - & 82.14 & 78.23 & 95.50 & 84.27 & 90.44 & 86.92 & 84.48 & 74.96 & - & 84.62\(\uparrow\) & 86.00\(\uparrow\) \\ \hline Backtranslation & - & 81.14 & 75.66 & 91.61 & 84.58 & 92.22 & 85.30 & 85.08 & 78.50 & - & 84.26\(\uparrow\) & 85.08 \\ Backtranslation + w & - & **83.06** & 76.83 & 91.31 & 83.21 & 92.51 & 86.24 & 85.56 & 83.29 & - & 85.25\(\uparrow\) & 85.53 \\ \hline RuGPT3 & - & 75.21 & 76.68 & 95.50 & 79.24 & 88.35 & 84.62 & 83.08 & 49.93 & - & 79.08 & 83.24 \\ RuGPT3 + w & - & 77.48 & 76.58 & 92.17 & 77.64 & 88.91 & 86.33 & 81.63 & 49.93 & - & 78.83 & 82.96 \\ \hline RuT5 & - & 79.39 & 75.71 & 92.41 & **86.75** & 91.51 & 85.11 & 85.19 & 75.96 & - & 84.00\(\uparrow\) & 85.15 \\ RuT5 + w & - & 80.24 & 76.48 & **96.46** & 85.79 & **92.94** & 84.21 & 86.36 & 75.96 & - & 84.81\(\uparrow\) & **86.07**\(\uparrow\) \\ \hline ChatGPT & - & 82.14 & 77.74 & 94.51 & 84.99 & 90.99 & 86.36 & 85.44 & **89.98** & - & **86.52**\(\uparrow\) & 86.02\(\uparrow\) \\ ChatGPT + w & - & 80.43 & **78.96** & 93.70 & 84.14 & 91.80 & **87.82** & 85.01 & 69.93 & - & 83.97\(\uparrow\) &  
amounts of generated texts may improve the current results for minority practices. In addition, the models trained on the current version of GreenRu might be used to search for posts containing mentions of rare green practices. These posts can be checked by experts and subsequently utilized to train classification models. Another possible direction for further improvement is the use of the context of the sentence as additional information. Some potential issues could limit the applicability of GreenRu. The dataset only contains the texts posted in the online green communities of Tyumen, Russia. The recent works on deep learning show that the generalization ability of the fine-tuned transformer-based models can be influenced by domain bias [52; 53; 54]. Thus, the performance of the models for detecting the mentions of green practices may deteriorate for the posts of green communities of other regions and the posts of other domains. This issue is typical for most domain-specific text corpora; in particular, this limitation is discussed by [55; 56; 57]. The currently used domain adaptation methods can be broadly classified into those employing deep architectures and rule-based techniques, such as instance-based and feature-based approaches to align the domain distributions [52]. Applying these techniques for the detection of green practices would be a fruitful area for further work. Another potential limitation of this research is related to the set of practices reflected in this study. GreenRu contains mentions of green practices only in waste management as one of the key aspects of harmonizing the relationship between people and the natural environment. The further extension of the dataset may include other green practices, such as caring for stray animals, cleaning and landscaping land, planting greenery, and some others.