Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current “state of the field” and thus, to enter it. This brought the id-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

As the largest open social medium on the Internet, Reddit is widely studied in the scientific literature. Due to its structured form and division into topical subfora (subreddits), conducted research often concerns connections and interactions between users and/or whole, subreddit-structure-based communities. Overall, the relations between communities are most often studied by applying graph networks, with various creation algorithms. In this work, a novel approach is proposed to build and understand the structure of Reddit. It is based on crossposts—posts that appeared on one subreddit and then were crossposted to another. After capturing one year of crossposts, a directed weighted graph network, using seven million posts from over 10,000 of the most popular subreddits, has been created. Using graph network algorithms, its characteristics are captured and compared to similar studies. We identify the information “sinks” and “sources”—the most active crossposting subreddits. Moreover, we obtained graph network metrics: the degree (modeled with the Power Law), clustering, community detection algorithms, and connected components structure network are compared to previous studies on Reddit network(s), yielding consistent, but also novel results. Finally, the relations between extensively studied subreddits (e.g., r/AITA, r/Parenting, r/politics) and new ones, which were not accounted for in previous research, opening new paths for data-driven studies, are summarized.

This contribution argues that Reddit, as a massive, categorized, open-access dataset, is a useful data source, on "almost any topic", which can be used for data science and knowledge exploration. This statement is backed-up with presented analysis, based on 180 manually annotated papers, related to Reddit itself, and data acquired from top databases of scientific papers. Finally, an open source tool is introduced, which provides easy access to Reddit resources, and exploratory data analysis of how Reddit covers selected topics. This is a prelude analysis to a broader exploration of Reddit's applicability.

Multiple research directions have been proposed to study the information structure of Reddit. One of them is to model inter-subreddit relations but modeling user interactions in the form of a graph. Building upon prior work centered on political subreddits using pre-2020 data, we expand this investigation to include a more extensive dataset spanning 2022 and encompassing diverse topic areas. Employing NLP techniques such as text embeddings, we model subreddit content directly and construct a subreddit graph network based on cosine similarity. Community detection using the Louvain method reveals distinct subreddits and allows the analysis of inter-community connections via previous works’ concepts of “bridges” and “gateways”. Surprisingly, our findings indicate redundancy between bridges and gateways in the utilized dataset. Therefore, we introduce a new concept, “highways”. Highways, representing the most traversed paths between subreddits, unveil insights not captured by previous analyses, underscoring the significance of novel conceptual frameworks in uncovering latent knowledge within Reddit’s online community structures.

The rapid influx of low-quality data visualizations is one of the main challenges in today’s commu- nication. Misleading, unreadable, or confusing visualizations spread misinformation. Furthermore, they fail to deliver their message to the viewer. The scale of the problem is big enough that there already exist public fora gathering tens of thousands of users criticizing graphics and charts (reddit.com/r/dataisugly) made with obvious mistakes. Current attempts at data visualization appear mostly as simple and overgeneralized checklists, and often lack systematicity and versatility. The lack of proper tooling for evaluating data visualization quality further heightens the problem. Therefore, this paper proposes VisQualdex, a systematic set of guidelines for static data visual- ization. The codex categorization is based and inspired by the theory of Grammar of Graphics. It contains dozens of criteria designed to catch various errors and mistakes of different categories and magnitude. Furthermore, it has been peer-reviewed and tested by experts of data visualization, data science, graphics design, information technology and computer science. To apply theory in the real world and increase the practical impact of VisQualdex, this contribution also introduces a practical tool. The implementation of the guidelines is available in the form of the web server, https://visqual.onrender.com, developed as a single page application in JavaScript using Vue.js and Material Design principles.