Interpreting Twitter User Geolocation

Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, Yi Yang


Abstract
Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called “black-box” GNN-based models by investigating the effect of individual nodes.
Anthology ID:
2020.acl-main.79
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
853–859
Language:
URL:
https://aclanthology.org/2020.acl-main.79
DOI:
10.18653/v1/2020.acl-main.79
Bibkey:
Cite (ACL):
Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, and Yi Yang. 2020. Interpreting Twitter User Geolocation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 853–859, Online. Association for Computational Linguistics.
Cite (Informal):
Interpreting Twitter User Geolocation (Zhong et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.79.pdf
Video:
 http://slideslive.com/38929254