Various bits of information about users accessing Web sites. some of which are private, have been gathered since the inception of the Web. Increasingly the gathering, aggregation, and processing has been outsourced to third parties. The goal of this work is to examine the effectiveness of specific techniques to limit this diffusion of private information to third parties. We also examine the impact of these privacy protection techniques on the usability and quality of the Web pages returned. Using objective measures for privacy protection and page quality we examine their tradeoffs for different privacy protection techniques applied to a collection of popular Web sites as well as a focused set of sites with significant privacy concerns. We study privacy protection both at a browser and at a proxy.

The task of protecting users' privacy is made more difficult by their attitudes towards information disclosure without full awareness and the economics of the tracking and advertising industry. Even after numerous press reports and widespread disclosure of leakages on the Web and on popular Online Social Networks, many users appear not be fully aware of the fact that their information may be collected, aggregated and linked with ambient information for a variety of purposes. Past attempts at alleviating this problem have addressed individual aspects of the user's data collection. In this paper we move towards a comprehensive and efficient client-side tool that maximizes users' awareness of the extent of their information leakage. We show that such a customizable tool can help users to make informed decisions on controlling their privacy footprint.

Developments in open data have prompted a range of proposals and innovations in the domain of governance and public administration. Within the democratic tradition, transparency is seen as a fundamental element of democratic governance. While the use of open government data has the potential to enhance transparency and trust in government, realising any ideal of transparent democratic governance implies responding to a range of sociotechnical design challenges.

Video games and their design are complex in nature, given the variety of aspects and challenges to face and the different areas of expertise involved. Furthermore, serious games have an even tougher challenge, since the knowledge acquisition has the same importance and relevance as entertainment and pleasure for the players. Serious games in cultural heritage require additional effort to introduce immersivity and collaboration among players. This article introduces a framework, named FRACH, to conceive, design, and evaluate immersive and collaborative serious games in cultural heritage. In particular, FRACH provides a design framework with steps to follow during the whole process that is from the early design phase to the evaluation phase of a serious game. We assessed the efficacy of our framework, with a specific case study in cultural heritage, by implementing a section of a serious game named HippocraticaCivitasGame , where players were allowed to visit the thermae of the historical site of San Pietro a Corte and Palazzo Fruscione in the city of Salerno, Italy, and to solve a given puzzle. Results of the game evaluation showed that the game was effective in terms of knowledge acquisition, the participants enjoyed the game, were highly involved in the immersive experience, and, finally, positively rated the idea of using the game for educational learning in the field of cultural heritage.

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Nowadays social media are the main means for conducting discussions and sharing opinions. The huge amount of information generated by social media users is helpful for predicting outcomes of real-world events in different fields, including business, politics and the entertainment industry. In this paper, we studied the possibility of forecasting the success of music albums by analyzing heterogeneous data sources spanning from social media (Twitter, Instagram and Facebook) to mainstream American newspapers (e.g., New York Times, Rolling Stones). The idea is to exploit music albums' pre-release hype and post-release approval to predict the album's rank with reference to the well-known Billboard 200 album chart, which tabulates the weekly popularity of music albums in the USA. To predict the success of a music album, that is its rank in the chart, we identified metrics based on the messages' posting trend, the variation of the sentiment associated to such messages, the number of followers of the album's author, and the importance of the people who talk about it. To evaluate the effectiveness of the proposed metrics we have compared the prediction performances of several models based on supervised learning approaches among those most used in literature. As a result, we obtained that the Random Forest approach is able to predict the music album rank in the Billboard 200 Chart with an expected accuracy of 97%. As a further validation, using this specific model, we also conducted an additional real usage test obtaining an almost matching result (accuracy of 94%).INDEX TERMS Social media, machine learning, prediction, sentiment analysis, music industry.We design a machine learning-based prediction model that we call Billboard 200 Predictor, or BB200P.

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People on the Web are generating and disclosing an ever-increasing amounts of data, often without full awareness of who is recording what about them, and who is aggregating and linking pieces of data with context information, for a variety of purposes. Awareness can help users to be informed about what silently happens during their navigation while learning from disclosure of personal information may help to discriminate potential harmful activities from daily and regular activities that can be performed online. Our main objective is to study whether a highly customized tool can help users to learn the value of privacy from their behaviors and make informed decisions to reduce their degree of exposure. To this aim, we present an evaluation study to analyze general perceptions, attitudes, and beliefs about privacy online, and to explore the resultant behaviors for two different groups of participants from an academic environment. Results show that users from the ICT field (Information and Communication Technology) are less concerned than non-ICT ones (i.e., not technological-oriented students), and that skill and expertise can influence the perception of the risks as well as the corresponding behaviors. Finally, students with less expertise in the ICT field learned more than the others, by showing greater willingness to adopt technologies to protect their privacy online.