Suchergebnisse

"The article discusses changes in scientific work (academic and applied) associated with new potentials, but also coercions of information technologies. Background for this interest is the experience gained in several digital library projects that inclinations and willingness to use these technical possibilities is much less common than the developers of these systems, and we all, tended to think in recent years. This seems to be true even in those scientific disciplines which were and are at the forefront of the development, e.g. physics, mathematics, etc. The background for this observation is discussed looking at general economic and social changes, viewing the environments of work in the scientific sphere, the contents and their quantity and quality of supply in scientific IT systems, the user side in their communities of practice, and the technological and organizational basis of scientific information. Some strategic issues to improve the situation are discussed in the final part of the paper." (author's abstract)

Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Although these causes usually receive public attention when it involves companies' higher managing staff, athletes, politicians or artists, the general public is also subject to these issues. To address this problem, we envision a mechanism that can suggest users the appropriate privacy setting for their posts taking into account their profiles. In this paper, we present a thorough analysis of privacy settings in Facebook posts and evaluate prediction models that can anticipate the desired privacy settings with high accuracy, making use of the users' previous posts and preferences.