Analyzing and Predicting Privacy Settings in the Social Web

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.

Authors: Kaweh Djafari Naini, Ismail Sengor Altingovde, Ricardo Kawase, Eelco Herder, Claudia Niederée

PDF: naini-umap2015.pdf

Characterizing high-impact features for content retention in social web applications

One of the core challenges of automatically creating Social Web summaries is to decide which posts to remember, i.e., to consider for summary inclusion and which to forget. Keeping everything would overwhelm the user and would also neglect the often intentionally ephemeral nature of Social Web posts. In this paper, we analyze high-impact features that characterize memorable posts as a first step for this selection process. Our work is based on a user evaluation for discovering human expectations towards content retention.

Authors: Kaweh Djafari Naini, Ricardo Kawase, Nattiya Kanhabua and Claudia Niederée

PDF: naini-www2013naini-www2014