Unvilling In Twitter Inference Attack On Browsing History Of Twittwe Users Using Public Click Analytics And Twitter Metadata

Abstract

Twitter is a popular online social network service for sharing short messages (tweets) among friends. Its users frequently use URL shortening services that provide (i) a short alias of a long URL for sharing it via tweets and (ii) public click analytics of shortened URLs. The public click analytics is provided in an aggregated form to preserve the privacy of individual users. In this paper, we propose practical attack techniques inferring who clicks which shortened URLs on Twitter using the combination of public information: Twitter metadata and public click analytics. Unlike the conventional browser history stealing attacks, our attacks only demand publicly available information provided by Twitter and URL shortening services. Evaluation results show that our attack can compromise Twitter users’ privacy with high accuracy. While these platforms offer unprecedented opportunities for connectivity and information sharing, they have also become fertile ground for the rapid dissemination of misinformation. In response to this growing concern, this study delves into a novel problem termed the Activity Minimization of Misinformation Influence (AMMI) problem. The core objective of the AMMI problem is to strategically identify and block a specific set of K nodes within a given social network G in such a way that the total amount of misinformation interaction between the remaining nodes (TAMIN) is minimized. Essentially, we aim to select K
influential nodes whose removal would most effectively curtail the spread and interaction of misinformation across the network. Neutralize key nodes that facilitate misinformation flow represents a crucial step towards effective network governance and the preservation of information integrity in the digital age.