## 1 Introduction  
Reddit is a very large social network, with information divided into topical fora called subreddits. In March 2023, Reddit had about 52 million active users ([[https://backlinko.com/reddit-users](https://backlinko.com/reddit-users)]([https://backlinko.com/reddit-users](https://backlinko.com/reddit-users)), accessed on 13 February 2024) and over 3.4 million subreddits ([[https://www.businessdit.com/how-many-subreddits-are-there](https://www.businessdit.com/how-many-subreddits-are-there)]([https://www.businessdit.com/how-many-subreddits-are-there](https://www.businessdit.com/how-many-subreddits-are-there)), accessed on 13 February 2024). Analysis of pertinent literature shows that there are two main approaches to using Reddit-extracted data in research. The first focuses on a _single subreddit_ and analyzes various phenomena within it. Here, existing studies have been focused, among other topics, on politics [1], gaming [2], online harassment [3], mental health [4, 5], suicide prevention [6], dermatology [7], parenting [8] or teaching [9], and others [10]. On the other hand, Reddit-based data, _treated as a single dataset_, have been used in natural language [11] or image processing [12]. Here, the Reddit structure(s) was (were) ignored, and all considered posts were treated as a single large collection of texts. However, recent researchresearch ([13]) suggested that additional big scale studies are needed, to properly capture Reddit information structure. In this context, to the best of our knowledge, no research used a large Reddit dataset, while taking into account the existence and caveats (such as crossposts) of thematic subfora. This is somewhat surprising, taking into account the fact that it is the existence of the topical subfora that distinguishes Reddit from the other social networks. Therefore, the aim of this contribution is to deliver a more comprehensive understanding of the information structure of Reddit. In particular, the focus of reported work is on automatically establishing common topics of interest shared by readers of individual subreddits. Here, we note that there exists a Reddit-only phenomenon that is completely absent from the Reddit research. These are crossposts, i.e., posts that were posted to one subreddit and later linked in another one. In other words, crossposts represent the situation when individual users of a specific subreddit believe that selected posts could be of interest to readers of another subreddit. As such, crossposts explicitly capture how individual users conceptualize similarities between subreddits. However, it has to be acknowledged that crossposts are relatively infrequent (vis-a-vis the volume of data posted regularly on Reddit). This means that their appearance can be used only as a corroboration of the existence of common interests between readers of different subreddits (i.e., it cannot be treated as a ground truth for subreddit similarity). Nevertheless, it is our belief that they are worthy of a closer examination. In this context, the aim of this contribution was to establish (1) ways to automatically uncover topical similarities between subreddits and (2) the role that crossposts can play in this process. The proposed approach to reach these goals is based on natural language processing (NLP), Named Entity Recognition (NER), and graph networks. Here, NER is used to find entities which are then used to build graph networks. The goal of the process is to capture and elaborate on similarities between found named entities. We note that this work can have practical application. Let us assume that researchers are interested in topics related to mental health (as they are represented in the Reddit posts). In this case, the most natural subreddit for their work would be r/mentalhealth. However, if it would be possible to establish which other subreddits involve topics similar to these discussed in r/mentalhealth, then such subreddits could be also included in the research.