Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.

Social media can be a mirror of human interaction, society, and historic disruptions. Their reach enables the global dissemination of information in the shortest possible time and, thus, the individual participation of people worldwide in global events in almost real-time. However, these platforms can be equally efficiently used in information warfare to manipulate human perception and opinion formation. Within this paper, we describe a dataset of raw tweets collected via the Twitter Streaming API in the context of the onset of the war, which Russia started in Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia invasion of Ukraine. This paper details the acquisition process and provides first insights into the content of the data stream. In addition, the data has been annotated with availability tags, resulting from rehydration attempts at two points in time: directly after data acquisition and shortly before manuscript submission. This may provide information on Twitter moderation policies. Further, we provide a detailed list of other published dataset covering the same topic. On the content level, we can show that our dataset comprises several distinct topics related to the conflict and conspiracy narratives -- topics that deserve more profound investigation. Therefore, the presented dataset is also made available to the community in an extended version with pseudonymized tweet content upon request.