Automatic quality evaluation of Web information is a task with many fields of applications and of great relevance, especially in critical domains, like the medical one. We move from the intuition that the quality of content of medical Web documents is affected by features related with the specific domain. First, the usage of a specific vocabulary (Domain Informativeness); then, the adoption of specific codes (like those used in the infoboxes of Wikipedia articles) and the type of document (e.g., historical and technical ones). In this paper, we propose to leverage specific domain features to improve the results of the evaluation of Wikipedia medical articles, relying on Natural Language Processing (NLP) and dictionaries-based techniques. The results of our experiments confirm that, by considering domain-oriented features, it is possible to improve existing solutions, mainly with those articles that other approaches have less correctly classified.

In the last decade, the advertisement market spread significantly in the web and mobile app system. Its effectiveness is also due thanks to the possibility to target the advertisement on the specific interests of the actual user, other than on the content of the website hosting the advertisement. In this scenario, became of great value services that collect and hence can provide information about the browsing user, like Facebook and Google. In this paper, we show how to maliciously exploit the Google Targeted Advertising system to infer personal information in Google user profiles. In particular, the attack we consider is external from Google and relies on combining data from Google AdWords with other data collected from a website of the Google Display Network. We validate the effectiveness of our proposed attack, also discussing possible application scenarios. The result of our research shows a significant practical privacy issue behind such type of targeted advertising service, and call for further investigation and the design of more privacy-aware solutions, possibly without impeding the current business model involved in online advertisement.