## 4 The Proposed Approach
It should be noted that the following metadata were also tried: _is_meta_ (mean) ([[https://reddit.com/r/NoStupidQuestions/comments/b9](https://reddit.com/r/NoStupidQuestions/comments/b9) xe4d/what_exactly_is_a_meta_post]([https://reddit.com/r/NoStupidQuestions/comments/b9](https://reddit.com/r/NoStupidQuestions/comments/b9) xe4d/what_exactly_is_a_meta_post), accessed on 13 February 2024), _is_original_content_ (mean) ([[https://www.reddit.com/r/OutOfTheLoop/comments/1](https://www.reddit.com/r/OutOfTheLoop/comments/1) vlej/what_does_oc_mean]([https://www.reddit.com/r/OutOfTheLoop/comments/1](https://www.reddit.com/r/OutOfTheLoop/comments/1) vlej/what_does_oc_mean), accessed on 13 February 2024), _is_video_ (mean), _over_18_ (mean), _spoiler_ (mean), _upvote_ratio_ (mean). However, they did not visibly influence the results, most likely due to them being represented as boolean values (except the upvote_ratio). We interpret this fact as follows. The boolean information was not enough for any tried similarity measure to influence the similarity. The zero or one values can either be equal or not, there is no more nuanced similarity that could be represented, as it is in case of, e.g., floating point numbers. However, joint use of all the metadata, together with all available posts, may be explored in the future. The next step is named entity extractions with dslim/bert-base-NER ([[https://huggingface.co/dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER)]([https://huggingface.co/dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER)), accessed on 13 February 2024) (pre-trained BERT large models fine-tuned on the CoNLL-2003 [44] dataset), which was already covered in Sections 2.1. Extracted entities are deduplicated. The result of this step is a set of named entities extracted from posts. The following step is the creation of graphs representing each subreddit. Here, an edge connects a pair of nodes (recognized named entities if they appear in at least one post together. Multi-edges are combined into single edges and their _edge weight_ equals to the sum of _scores_ of posts in which the connected entities appear. The result is a graph representing the structure of the information content of a given subreddit. It includes all recognized named entities and their features encoded in nodes, edges and edge weights. All individual subreddit graphs are available in a Zenodo repository ([[https://zenodo.org/record/8037573](https://zenodo.org/record/8037573)]([https://zenodo.org/record/8037573](https://zenodo.org/record/8037573)), accessed on 13 February 2024). Table 1 summarizes the basic characteristics of obtained graphs. It also illustrates their scale. What is important to note is the large heterogeneity of obtained graphs. This is clearly visible by the differences between the means and the medians of, for instance, the node count (1919 vs. 918) and the edge count (2397 vs. 495). Furthermore, the graphs have nonuniform distribution of periphery sizes [90]. In 47% of graphs, 10% of the nodes contribute to a periphery. On the other hand, in 25% of graphs, over 50% of nodes belong to a periphery. A similar situation emerges for isolates (nodes with zero degree). A total of 40% of graphs have over 50% of isolated nodes. This is consistent with graph density [91] being, on average, barely 0.002. To further highlight the \"1% rule\" of the graph structure, it is worth mentioning that in 74% of graphs, the average shortest path is less than three, meaning that, on average, any node is connected with any other node with a distance of two.