## 5 Experimental Results and Their Analysis  
The outcomes of applying the proposed method to the collected dataset are now discussed. Let us start from describing the evaluation process. For each pair of subreddits, named entities from posts and named entities from crossposts are considered separately. We note that named entities that were extracted from crossposts from subreddit \(A\) to subreddit \(B\) and from \(B\) to \(A\) are combined into one set (regardless of the direction of a crosspost). In other words, named entities from crossposts are treated as being present in both subreddits. This can be understood in the following way. If a post with entities \(NE1\), \(NE2\) and \(NE3\) was \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistic** & **Mean** & **Median** \\ \hline named entity count & 5113.14 & 1727 \\ unique named entity count & 1888.92 & 918.5 \\ node count & 1919.44 & 940.5 \\ edge count & 2397.38 & 492 \\ degree & 1.32 & 1.08 \\ degree centrality & 0.004 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network statistics aggregated. crossposted from subreddit \(A\) to subreddit \(B\), then these three named entities materialized in the set of named entities in crosspostols in subreddit \(B\). We recall that for each individual similarity measure, named entities (from posts appearing in pairs of subreddits) are compared and ordered according to a given similarity measure (rankings resulting from the application of the proposed approach). Next, the set of named entities from posts and the set of named entities from crossposts (only) are compared and evaluated using metrics applied in similar problems (as described in Section 2.2)--recall@10 and AUC. Specifically, recall@10 and AUC metrics are calculated between the ranking of similarity of named entities originating from posts and crossposts. Recall is calculated using the following formula: \(|X\cap Y|/|X|\), where \(X\) is the set of named entities found in crossposts between two considered subreddits, \(Y\) is the set of named entities found in the two considered subreddits using the proposed method, and \(|X|\) is the size of the set \(X\). AUC is calculated with _roc_auc_score_ from the sklearn library [93] based on the overlap of named entities found in crossposts and those selected from all posts in the two considered subreddits. The best results, in terms of the recall and AUC, are achieved for summing the metadata (e.g., summing the aggregated _score_ of an entity in subreddits \(A\) with \(B\)) and calculating the negative absolute value of network entities (e.g., calculating \(-|\)_degree of entity in graph of subreddit \(A-\)degree of entity in graph of subreddit \(B\)_). The aggregated results for all pairs of subreddits are presented in Table 2. Individual results for each subreddit pair can be found in the 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). The main observation is that metadata metrics achieved better recall, while network characteristic provided better AUC results. To evaluate the results, a reference point is needed. Although there is no direct comparison, previous works about recommendation systems use similar metrics (recall@10 and AUC) [63; 64; 68; 69; 70; 79; 82; 85; 94; 95]. These works are discussed in Section 2.2 and consider the problems such as hashtag recommendation, subreddit recommendation, and user recommendation. While these problems are different to the task studied in this contribution, the top results, from studies of similar scale datasets, range from 25 to 60% for the recall and 60 to 90% for the AUC. While concerning different problems, they provide some indication as to what could be reasonably expected performance measures when dealing with Reddit data. In Table 2, the best recall results are achieved for the metadata: _score_ (38%), _number of comments_ (35%) and _total awards received_ (35%).