The Collaborative Search by Tag-Based User Profile in Social Media

Abstract

Recently, we have witnessed the popularity and proliferation of social media applications (e.g., Delicious, Flickr, and YouTube) in the web 2.0 era. The rapid growth of user-generated data results in the problem of information overload to users. Facing such a tremendous volume of data, it is a big challenge to assist the users to find their desired data. To attack this critical problem, we propose the collaborative search approach in this paper. The core idea is that similar users may have common interests so as to help users to find their demanded data. Similar research has been conducted on the user log analysis in web search. However, the rapid growth and change of user-generated data in social media require us to discover a brand-new approach to address the unsolved issues (e.g., how to profile users, how to measure the similar users, and how to depict user-generated resources) rather than adopting existing method from web search. Therefore, we investigate various metrics to identify the similar users (user community). Moreover, we conduct the experiment on two real-life data sets by comparing the Collaborative method with the latest baselines. The empirical results show the effectiveness of the proposed approach and validate our observations.

Security information sharing via Twitter: 'Heartbleed' as a case study

“…For example, Twitter profile information about contacts has been used to make personality trait attributions (e.g., Quercia et al, 2011), to examine user contributions and contacts (see Zhang and Nasraoui, 2008), and to cluster individuals into groups or derive group profiles for use in simulated learning systems (see Ammari et al, 2012). Our goal was to identify similar groups using keywords in each profile (Fernandez et al, 2014;Sloan et al, 2015; for other methods see Mizzaro et al, 2015;Xie et al, 2014).…”