## 5 Results and Discussion
[31] directs its scraping towards specific YouTube endpoints to retrieve the top \(K\) videos displayed without user consent, solely collecting titles and descriptions. Similarly, Ref. [29] also gathers data from YouTube, but it uses a more advanced and semantically enriched latent vocabulary that categorizes results based on ethically questionable practices for accessing protected content. Meanwhile, study [35] employs a hybrid strategy, interspersing the detection of live streams with compromised copyright with a dataset customized for research purposes. **First comparative finding**: Compared to the BERT + DNN methodology, which explores a section of the IoE using five search engines, studies [28; 30; 32] completely restrict their scope without retrieving any information from the IoE; on the other hand, Refs. [29; 31; 35] only address a fraction of the IoE and fail to achieve a broader spectrum in their collection due to the absence of search engine utilization. Only Ref. [33] conducts an effective traversal, but its effort is confined to recovering just 98 sites associated with pirated content, a number not considered substantial for a study of this caliber. In the realm of exploring relevant categories, only the studies [28; 33] propose a wider spectrum of content for infringement detection. For instance, Ref. [28] features a collection of music and videos in line with Apple's rankings, while Ref. [33] turns to the now-defunct AlexaNet to identify sites likely to be monitored in their data collection, covering areas such as video games, torrents, and sports. In a more generic approach, Ref. [30] superficially addresses the software category without specifying which programs might be more prone to illicit use. Regarding videos, Refs. [29; 31; 35] focus on real-time streaming platforms without delving into how to handle TV series, movies, or other streaming content types. In a more limited scope, Ref. [4] relies on brands from recognized providers without describing their specific fields of expertise. Studies [28; 29; 30; 31; 32; 33; 35] fail to incorporate additional features that define the behavior and structure of the sites analyzed. **Second comparative finding**: In contrast to previous research, this methodology (BERT + DNN) encompasses a wider range of categories (\(NI\), \(MS\), \(M\), \(S\), and \(B\)), delving into a more comprehensive analysis of distinctive features of infringing sites. This analysis includes elements such as redirections, reputation assessment, the presence of malicious elements (adware), the use of URL shorteners, and the improper use of JavaScript, among others, factors that have not been addressed by other studies. In the domain of NLP methods, studies [30; 32] do not utilize textual analysis methods, erroneously assuming that infringing content can be detected based on specific dependencies. This approach is significantly flawed as it overlooks semantic subtleties, potential issues like case sensitivity, ambiguity, and comprehension limitations, resulting in a final product that cannot be effectively assessed for performance [99]. Conversely, study [28] employs the Vector Space Model (VSM). However, this method also faces challenges, as it has been shown that a pure vector representation lacks semantic depth, contributes to an unstable vocabulary dimension, and leads to data dispersion, yielding vector outputs of low quality [99]. In study [29], queries based on word similarity are utilized, also through VSM. Such comparisons between sentences risk scale sensitivity in the resulting vectors due to their reliance on the frequency and weight of word occurrences, which can lead to inappropriate similarities [100]. Study [33] adopts a more robust representation by combining Optical Character Recognition (OCR) with Word2 Vec. Nonetheless, there are risks of mismatch and contextual misinterpretation when using OCR with Word Embeddings, as character misrecognition could lead to incorrectly interpreted words that distort the sense captured by Word2 Vec [101].