TY - JOUR AU - Hadgu, Asmelash Teka AU - Gundam, Jayanth Kumar Reddy PY - 2020/05/26 Y2 - 2024/03/28 TI - Learn2Link: Linking the Social and Academic Profiles of Researchers JF - Proceedings of the International AAAI Conference on Web and Social Media JA - ICWSM VL - 14 IS - 1 SE - Full Papers DO - 10.1609/icwsm.v14i1.7295 UR - https://ojs.aaai.org/index.php/ICWSM/article/view/7295 SP - 240-249 AB - <p>People have presence across different information networks on the social web. The problem of user identity linking, is the task of establishing a connection between accounts of the same user across different networks. Solving this problem is useful for: personalized recommendations, cross platform data enrichment and verifying online information among others. In this paper, we propose a deep learning based approach that jointly models heterogeneous data: text content, network structure as well as profile names and images, in order to solve the user identity linking problem. We perform experiments on a real world problem of connecting the social profile (Twitter) and academic profile (DBLP) of researchers. Experimental results show that our joint model achieves a 97% F1 score outperforming state-of-the-art results that consider profile, content or network features only.</p> ER -