## 2 Pertinent State of the Art of Reddit-Related Research
### Use of Crossposts in Reddit Information Structure and Content Analysis  
As noted earlier, one of Reddit's features that is almost non-existent in the literature are the so-called crossposts. Crossposts were introduced in 2017 ([[https://www.reddit.com/r/modnews/comments/7a5](https://www.reddit.com/r/modnews/comments/7a5) ubn/crossposting_coming_soon_to_your_subreddit]([https://www.reddit.com/r/modnews/comments/7a5](https://www.reddit.com/r/modnews/comments/7a5) ubn/crossposting_coming_soon_to_your_subreddit), accessed on 13 February 2024). These are posts which appeared in one subreddit and were manually linked (crossposted) to a different one by a user. Since crossposts reference the original subreddit, they (1) show which posts (and named entities found in them) are seen by the readers of one subreddit as being of likely interest to the readers of another subreddit; and (2) establish a directional link between subreddits. We note that crossposts are the only user-generated content that explicitly indicates potential existence of shared interests between separate communities (the originating subreddit and the subreddit the post was crossposted to). In this context, authors of [80] suggested that crossposts may help to better understand the information structure of Reddit [14].  
Obviously, knowledge brought by crossposts needs to be taken with caution. The fact that a user \(A\) believes that a post \(X\) from subreddit \(S1\) would be of interest to the readers of subreddit \(S2\) does not represent universal truth. It is possible that the user \(A\) is mistaken (or their crosspost is a result of a deliberate misinformation campaign). However, after manual inspection of a randomly picked sample of 200 crossposts, it was established that only less than 5% can be seen as potentially malicious or misguided. Hence, application of crossposts in analysis of subreddits is further explored in this contribution.  
Nevertheless, it is important to note that since crossposts are scarce and may be accidental (rather than a result of in-depth analysis performed by the user), they should be seen more as a hint than hard evidence. So, their use in analysis of data should not apply standard effectiveness metrics, e.g., accuracy, precision, etc. In the literature, classification problems with an uncompleted response variable are often addressed with the _positive-unlabeled_ (PU) approach [81], which offers two solutions: (1) assumption that that unlabeled cases are negative, or (2) estimation of the distribution of \(Y\). However, in the case of crosspost-based analysis, missing are features that the PU needs. First, there are no negative samples (i.e., topics that are definitely not interesting to the two subreddit communities). Second, the positives (i.e., topics interesting to the two groups) are captured too rarely. Moreover, estimators of \(Y\) are not known to exist (and cannot be expected to be established in the future). Therefore, instead of PU-evaluation, the use of _recall_ and _AUC_ was selected. Here, we notice that recall rewards capturing the entities that actually appeared in crossposts, while AUC counters the bias in using recall alone. Here, let us recall that this approach was also used for ranking evaluation in similar problems described in Section 2.2, e.g., subreddit recommendation [82; 83], hashtag recommendation [64; 68; 84; 85; 86], or in the case of user recommendation systems [63]. There, quality of rankings was evaluated using hit-rate (i.e., recall@5, or recall@10) and ROC AUC. Taking into account the size of the available dataset, it was decided to apply _recall@10_ and _ROC AUC_ to estimate how well the entities detected with the proposed method as being common to two subreddits fit with the entities found in the crossposts.