## 4 The Proposed Approach  
Let us now describe the method, which is the core contribution of the reported research. For easier understanding, Figure 1 describes the process step by step, starting with already described data acquisition up to the similarity calculation. To offer a better understanding of the approach, the following simplified pseudoalgorithm summarizes it as well:  
1. Acquire posts from two subreddits. 2. Preprocess the posts. 1. Clean deleted/removed posts; 2. Filter meaningful posts based on their _score_;Extract and store the required metadata for each post (_score, number of comments; total awards received_, does _it contain an image_). 3. Extract named entities from posts using NLP models and deduplicate them, if needed. 4. Create a graph network using named entities for each of the subreddits; here, 1. Named entities become nodes; 2. An edge joins two named entities if they appear in a post together. Multi-edges (when the entities appear jointly in multiple posts) are merged and (for each metrics) their weight becomes the sum of _scores_ originating from posts where the two named entities appear jointly. 5. Calculate graph network characteristics for the two subreddit graphs separately and assign to each node (degree, clustering, average neighbor degree). 6. Filter out named entities (nodes and their edges) that appear only in a single subreddit from the pair (from here on, only subgraphs with named entities common to both subreddits are considered). 7. For each measure (metadata-based and network characteristics) independently, apply the following: 1. For each (common) named entity, 1. Calculate the similarity between the measures for the named entities in the two subgraphs. 2. Create a ranking of named entities based on their similarity; 3. Obtain the top \(N=10\) entries (named entities). The final result for two subgraphs representing two subreddits is a set of rankings (consisting of 10 named entities each) obtained independently for each of the metadata-based and network characteristics-based similarities. For better understanding, a step-by-step example is provided in Appendix A to this work. Let us now provide more details of each step and describe the method fully. The dataset described above was used to create a graph network for each subreddit. As noted, thus far, graph networks have been created with users or subreddits as nodes. As one of the contributions of this work, graph networks were built for each subreddit, with named entities as nodes. Hence, rather than focusing on relations between users  
Figure 1: A scheme of the proposed method. and/or communities (as considered in the past), _the spotlight was shifted to the content of individual subreddits_. The first step of the approach consists of extracting and preprocessing all posts, as described in Section 3. In the second step, a post is assigned aggregated metadata. Specifically, the used metadata were (i) _score_--the sum of the number of upvotes reduced by number of downvotes, for each post containing a given entity; (ii) _number of comments_; (iii) _number of awards received_--awards are a special, paid badges that users can assign to other user posts ([[https://www.reddit.com/r/awards](https://www.reddit.com/r/awards)]([https://www.reddit.com/r/awards](https://www.reddit.com/r/awards)), accessed on 13 February 2024); and (iv) _does a post contain an images_--number of posts that, in addition to the entity under consideration, contained also an image (in any format).