## 6 Experimental Results
### Experiment Datasets  
The proposed framework has been tested over several datasets belonging to different domains, ranging from social networks and digital libraries (e.g., Twitter, Wikipedia) to scientific papers (e.g., PubMed collection). The corpora have been chosen to have different characteristics, ranging from the number of documents to the length of each individual document, and from lexical richness to the average frequency of terms. Moreover, in the same corpus, the documents should be characterized by homogeneous lengths and heterogeneous subjects, as well as be produced by different authors. In this way, these features allow results to be comparable and generic, avoiding overfitting of datasets. We have grouped the datasets based on their source and typology. In particular, datasets from D1 to D3 are collected from English documents from the Wikipedia collection ([[https://en.wikipedia.org/wiki/Wikipedia](https://en.wikipedia.org/wiki/Wikipedia)]([https://en.wikipedia.org/wiki/Wikipedia](https://en.wikipedia.org/wiki/Wikipedia)), accessed on 1 April 2022), which is the largest knowledge-base ever known. The categories of each dataset have been chosen to be sufficiently separate and, therefore, detectable by the clustering algorithms. For each category, the _top-k_ articles are extracted to form our corpus. From these categories, different datasets have been generated, divergent to the number of documents extracted for each topic. To construct the first dataset (i.e., D1), 200 articles were taken from the following 5 categories: _cooking_, _literature_, _mathematics_, _music_, and _sport_. Instead, the following ten categories were chosen to build datasets D2 and D3: _astronomy_, _cooking_, _geography_, _history_, _literature_, _mathematics_, _music_, _politics_, _religion_, and _sports_. D2 and D3 consist of 2500 and 5000 documents, respectively, chosen from these 10 categories. Table 2 shows the _statistical features_ of the three Wikipedia datasets used to test ESCAPE. On the other hand, dataset D4 includes short messages extracted from Twitter. Twitter can be crawled to extract subsets of tweets related to a specific topic. We corroborated ESCAPE with experiments on a crisis tweet collection [58] that has 60,005 tweets with 16,345 distinguished words. Tweets were gathered from six large events in 2012 and 2013 (2012 Hurricane Sandy, 2013 Boston Bombings, 2013 Oklahoma Tornado, 2013 West Texas Explosion, 2013 Alberta Floods, and 2013 Queensland Floods). Hence, the dataset contains 10,000 tweets for each natural disaster, and each tweet is labeled with relatedness (i.e., _on-topic_ or _off-topic_). In our analysis, we remove the a priori knowledge of each label in order to understand if ESCAPE is able to eliminate the noise present in the collection. Dataset D5 involves 1000 papers extracted from the PubMed collection, which is an interface to MEDLINE ([[https://www.ncbi.nlm.nih.gov/pubmed/](https://www.ncbi.nlm.nih.gov/pubmed/)]([https://www.ncbi.nlm.nih.gov/pubmed/](https://www.ncbi.nlm.nih.gov/pubmed/)), accessed on 1 April 2022), the largest biomedical literature database in the world. The number of expected categories is not known a priori. Lastly, dataset D6 comprehends documents extracted from the Reuters collection ([[http://www.daviddlews.com/resources/testcollections/reuters21578](http://www.daviddlews.com/resources/testcollections/reuters21578)]([http://www.daviddlews.com/resources/testcollections/reuters21578](http://www.daviddlews.com/resources/testcollections/reuters21578)), accessed on 1 April 2022), which is a widely used test collection for research purposes. The subset used for this study is the whole _Apte' split 90 categories_, created by merging the test and the training parts together for a total of 15,437 documents. The statistical features are reported in Table 3.  
Through the analysis of the proposed statistical features, we are able to categorize the datasets into few groups according to their statistical indices.