A Deep Metric Learning Approach to Account Linking

Aleem Khan, Elizabeth Fleming, Noah Schofield, Marcus Bishop, Nicholas Andrews


Abstract
We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and meta-data of the corresponding document streams. We focus on learning an embedding that maps variable-sized samples of user activity–ranging from single posts to entire months of activity–to a vector space, where samples by the same author map to nearby points. Our approach does not require human-annotated data for training purposes, which allows us to leverage large amounts of social media content. The proposed model outperforms several competitive baselines under a novel evaluation framework modeled after established recognition benchmarks in other domains. Our method achieves high linking accuracy, even with small samples from accounts not seen at training time, a prerequisite for practical applications of the proposed linking framework.
Anthology ID:
2021.naacl-main.415
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5275–5287
Language:
URL:
https://aclanthology.org/2021.naacl-main.415
DOI:
10.18653/v1/2021.naacl-main.415
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.415.pdf
Code
 noa/naacl2021 +  additional community code