Repository for the paper "A Simple Language Independent Approach for Distinguishing Individuals on Social Media" at ACM Hypertext 2021.
proposed-clf.ipynbcontains the source code for training and evaluating different approaches including the proposed approach.- The trained models for experiments can be downloaded from the shared folder here.
- Due to the limit of uploadable file size, data is not included in the current repository.
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.
Guangyuan Piao, "A Simple Language Independent Approach for Distinguishing Individuals on Social Media", 32nd ACM Conference on Hypertext and Social Media, 2021. [PDF] [BibTex]