Linky: Visualizing user identity linkage results for multiple online social networks (Demo)

• Main Authors: LEE, Roy Ka-Wei, HEE, Ming Shan, PRASETYO, Philips Kokoh, LIM, Ee-Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: social network; user identity linkage; visualization; Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/4262; https://ink.library.smu.edu.sg/context/sis_research/article/5265/viewcontent/22._Dec04_2018___Linky_Visualizing_User_Identity_Linkage_Results_For_Mulitple_Online_Social_Networks___Demo__ICDM18_.pdf
• Institution: Singapore Management University

User identity linkage across online social networks is an emerging research topic that has attracted attention in recent years. Many user identity linkage methods have been proposed so far and most of them utilize user profile, content and network information to determine if two social media accounts belong to the same person. In most cases, user identity linkage methods are evaluated by performing some prediction tasks with the results presented using some overall accuracy measures. However, the methods are rarely compared at the individual user level where a predicted matched (or linked) pair of user identities from different online social networks can be visually compared in terms of user profile (e.g. username), content and network information. Such a comparison is critical to determine the relative strengths and weaknesses of each method. In this work, we present Linky, a visual analytical tool which extracts the results from different user identity linkage methods performed on multiple online social networks and visualizes the user profiles, content and ego networks of the linked user identities. Linky is designed to help researchers to (a) inspect the linked user identities at the individual user level, (b) compare results returned by different user linkage methods, and (c) provide a preliminary empirical understanding on which aspects of the user identities, e.g. profile, content or network, contributed to the user identity linkage results.