# GreenRu: A Russian Dataset for Detecting Mentions of Green Practices in Social Media Posts
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studying the product labeling, waste recycling, and signing petitions. Researchers study green practices to offer citizens, eco-activists, and government agencies new ways to scale these practices [9; 10]. At present, there is insufficient knowledge regarding the existing green waste practices in Russian society. Only a few studies focus on some green practices, such as waste sorting, and describe them by eliciting data from questionnaires and interviews [11; 12]. Meanwhile, the content of online environmental community posts on social media is a valuable source of information about the characteristics and activities of online communities [13; 14]. Social networks have a significant impact on changing social practices [15; 16]. Internet communities perform the functions of recruitment, attribution, aggregation of interests, and mobilization of resources [17; 18; 19]. In addition, virtual communities promote participation in real activities, which can be both constructive and protesting in nature [20; 21; 22]. For further research, it is necessary to collect and systematize a large amount of social information about the green waste practices prevalent in Russian society, and the important source of such information is social networks using different methods [14; 23; 24; 25]. Since the manual identification of information in posts published in online communities is a time-consuming and long-term process, it requires the use of the automated analysis of such posts. However, to date, there are very few studies on environmental practices in the context of their risks and opportunities based on big data analytics, for example, deep learning or content analysis methods [13; 26]. This paper presents the GreenRu dataset for detecting the mentions of green practices in social media posts. The dataset consists of Russian social media posts from the VKontakte network, the most popular social network in Russia [27]. The dataset contains the mentions of nine green practices (Figure 1), which were manually annotated. GreenRu is marked up at the sentence level; in other words, each sentence is labeled in terms of the practices it contains. Several machine learning models in the form of both multi-label and binary classifications of sentences are applied to the present dataset. Since some green practices are currently more common than others [28], the classes, that is, the mentions of different types of green practices, are unevenly distributed in the dataset. It is expected to cause lower quality regarding the machine learning models for rarely represented classes. This paper compares several methods of data augmentation for the minority classes. It was demonstrated that augmentation can significantly improve the quality of the detection of the mention of a few common green practices. The main contributions of this work can be summarized as follows:  
1. GreenRu, the first dataset for detecting the mentions of green practices in Russian social media posts, is described. The paper presents our annotation scheme for green practice mentions that can be easily adapted for application in other languages and domains. 2. The multi-label and one-versus-rest current state-of-the-art models for text classification are evaluated while performing the task of detecting the mentions of green practices. 3. The performance of several data augmentation methods is estimated to handle class imbalance since the distribution of the mentions of green practices is unequal. The  
Figure 1: Adaptive vs. transformative green practices. results are provided both in terms of classification metrics and human evaluation. The results can be used in other tasks related to imbalanced multi-class text classification. The paper is organized as follows. Section 2 describes the processes of data collection and annotation. The section includes the subsections presenting the types of green practices, the details of the collecting posts, and the annotation guidelines. Section 3 describes the experimental setup.