## 5 Discussion
This can indicate both that websites that are popular are more keen to invest in Semantic Web integration and that Semantic Web integration might actually provide a minimal boost to popularity. To make more of this linear relationship the researchers proceeded to a gradient boosting analysis the results of which were presented in Section 4.2. The Gradient Boosting analysis was performed as mentioned using the XGBoost algorithm and provided some interesting findings. We can see from the overall statistics presented in Table 11 that overall prediction accuracy surpassed the value of 0.3. Considering the random prediction accuracy for the four defined intervals would be 0.25 there appears to be a small but noticeable increase. The increase's persistence can be observed by the minimum and maximum values of the 95% confidence interval which are both above the baseline of 0.25. Moreover, this increase indicates that, even though Semantic Web integration as measured by this study is not directly correlated with each website's overall popularity, it can still be used to an extent to more accurately predict under which popularity class a website would fall. By assessing the statistics by class as seen in Table 12, it appears that Class 1, which includes the top 25% of the websites in ranking, displays higher values than the other classes in Positive Prediction Value, Precision and Balanced Accuracy. This might indicate higher credibility of Semantic Web metrics when attempting to predict the popularity of top-ranking websites. In Figure 8 the features used in the Gradient Boost analysis are presented by order of calculated importance in the accurate prediction of popularity. They are clustered in four groups according to that importance. First and only feature in the most important cluster is the \"_schema_org\" feature which is an indicator of the percentage of crawled pages that include schema.org structured data. Usage of the schema.org vocabularies is promoted by Google, Microsoft, Yahoo, Yandex and more search engine providers which means that their inclusion to a larger extent, not only provides machine-readable content, but also increases the website's Search Engine Optimization Score (SEO) which in turn influences popularity. In the second cluster the features _og, _twitter and _html_variety appear. The first two assist with social media integration and thus make a page easier to diffuse through the multiple social media platforms available. The _html_variety feature represents the effort, from a developer's perspective, to enhance a web page's semantic value by using a greater variety of HTML5 semantic elements. The other features appear to have less importance and are grouped in the remaining clusters. Social media-related rich-data content variety as indicated by \"_og_variety\" and \"_twitter_variety\" seems to matter but to a smaller extent. This makes sense if we consider that content can sometimes be accurately described even without much variation in the description itself. The feature \"_rss_feeds\" which indicates the usage of RSS also plays a more minor role. RSS, though still useful, seems to be of waning importance as a means to convey machine readable information. Additionally, all metrics relating to microformats appear to be irrelevant. This is to be expected judging by how few implementations of this Semantic Web tool were detected during the crawling process.