## 3 Dataset Preparation
### Collection and Filtering of the Dataset  
The initial dataset was collected via Pushshift API [87], with custom scripts realizing the preprocessing pipeline. It contained all posts from 3000 most popular subreddits by subscriber count, spanning the whole year 2022. However, preliminary data check indicated that very few crossposts exist between these 3000 subreddits. Therefore, the dataset was extended with posts from additional 250 subreddits, which contained most crossposts from/to the initially selected 3000.  
Next, the following categories of inadequate subreddits were filtered. (1) Administrative subreddits (e.g., r/annourements--with posts by Reddit administrators, or r/help--a technical support subreddit). (2) Image-only subreddits (e.g., r/aww) that cannot be analyzed directly using NLP. (3) Subreddits that impose a specific post structure (e.g.,r/copypasta, or r/hmm), which require specialized tools for entity recognition. (4) Subreddits, in which NER run into technical issues (e.g., r/merirl, r/merilgbt and similar). For example, all posts in r/merirl are forced to have \"meirl\" as the post title, negatively influencing results of NER analysis, because the NER model is only fed strings saying \"meirl\" and mistakenly considers it as a named entity (the only named entity in this subreddit).  
In addition to subreddit-level cleaning, pruning was applied to individual posts. Here, it was noticed that multiple posts did not capture the attention of other users. These posts ere filtered out based on their _score_. The _score_ is the Reddit's appreciation mechanism. A user can give a post an upvote (+1) or a downvote (\(-1\)). Here, we note that all posts are upvoted by default (the starting _score_ is 1). The resulting _score_ is the sum of upvotes and downvotes. Interestingly, it very rarely happens that a post accumulates a negative _score_. As a matter of fact, such cases were absent in the collected dataset. However, the number of upvotes and downvotes of a given post is not known. Hence, it is possible that a highly controversial post with a lot of upvotes and downvotes ended up as being _score_-neutral. Here, it was assumed that posts with a low _score_ can be omitted. In view of the above, this decision may seem as somewhat controversial, but it was based on the observation that the probability that the number of upvotes and downvotes is almost equal is relatively low. We note that, obviously, there is no universal threshold of when a post becomes significant. However, typically, post popularity fits the Internet \"1% rule\" [88]. Therefore, in reported work, only the top 20% of most popular posts were retained. This reduced the size of the dataset, allowing faster processing. Using all (more) posts is one of potential future research directions. This is appealing also since it takes care of the potential of upvotes and downvotes cancelling out. Nevertheless, preliminary explorations suggested that the use of all posts does not bring significantly different results while substantially increasing processing time.  
Overall, after filtering, the dataset contained 32,203,763 posts from 3189 subreddits (median of 341,243 subscribers per subreddit), with a median of 3875 posts per subreddit. The posts had medians of 192 _score_, 14.5 comments, and 0.5 named entities per posts. We note that, to the best of our knowledge, no research focused on various subreddits used such a large Reddit dataset (see also [82]). Finally, about 650,000 posts (2.3% of all posts) were crossposted.