Pairing users in social media via processing meta-data from conversational files

Abstract

Massive amounts of data today are being generated from users engaging on social media. Despite knowing that whatever they post on social media can be viewed, downloaded and analyzed by unauthorized entities, a large number of people are still willing to compromise their privacy today. On the other hand though, this trend may change. Improved awareness on protecting content on social media, coupled with governments creating and enforcing data protection laws, mean that in the near future, users may become increasingly protective of what they share. Furthermore, new laws could limit what data social media companies can use without explicit consent from users. In this paper, we present and address a relatively new problem in privacy-preserved mining of social media logs. Specifically, the problem here is the feasibility of deriving the topology of network communications (i.e., match senders and receivers in a social network), but with only meta-data of conversational files that are shared by users, after anonymizing all identities and content. More explicitly, if users are willing to share only (a) whether a message was sent or received, (b) the temporal ordering of messages and (c) the length of each message (after anonymizing everything else, including usernames from their social media logs), how can the underlying topology of sender-receiver patterns be generated. To address this problem, we present a Dynamic Time Warping based solution that models the meta-data as a time series sequence. We present a formal algorithm and interesting results in multiple scenarios wherein users may or may not delete content arbitrarily before sharing. Our performance results are very favorable when applied in the context of Twitter. Towards the end of the paper, we also present interesting practical applications of our problem and solutions. To the best of our knowledge, the problem we address and the solution we propose are unique, and could provide important future perspectives on learning from privacy-preserving mining of social media logs.