Piao, Guangyuan (2021) A Simple Language Independent Approach for Distinguishing Individuals on Social Media. HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media. pp. 251-256.
|
Download (1MB)
| Preview
|
Abstract
Nowadays, the large-scale human activity traces on social media platforms such as Twitter provide new opportunities for various research areas such as mining user interests, understanding user behaviors, or conducting social science studies in a large scale. However, social media platforms contain not only individual accounts but also other accounts that are associated with non-individuals such as organizations or brands. Therefore, distinguishing individuals out of all accounts is crucial when we conduct research such as understanding human behavior based on data retrieved from those platforms. In this paper, we propose a language-independent approach for distinguishing individuals from non-individuals with the focus on leveraging their profile images, which has not been explored in previous studies. Extensive experiments on two datasets show that our proposed approach can provide competitive performance with state-of-the-art language-dependent methods, and outperforms alternative language-independent ones.
| Item Type: | Article |
|---|---|
| Keywords: | Account Classification; Deep Learning; Social Media Analysis; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 15631 |
| Identification Number: | https://doi.org/10.1145/3465336.3475092 |
| Depositing User: | Guangyuan Piao |
| Date Deposited: | 08 Mar 2022 11:41 |
| Journal or Publication Title: | HT 2021 - Proceedings of the 32nd ACM Conference on Hypertext and Social Media |
| Publisher: | ACM Digital Library |
| Refereed: | Yes |
| URI: | |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only(login required)
 |
Item control page |
Downloads
Downloads per month over past year
Altmetric
Altmetric