Detecting Social Media Manipulation in Low-Resource Languages
This addresses the challenge of social media manipulation in understudied low-resource languages, which is crucial for combating disinformation across diverse regions, though it is incremental as it builds on existing transfer learning methods.
The paper tackles the problem of detecting malicious social media accounts in low-resource languages like Tagalog, achieving promising accuracy by using text embedding and transfer learning without prior training on malicious content in that language, and significantly outperforming state-of-the-art models such as BERT.
Social media have been deliberately used for malicious purposes, including political manipulation and disinformation. Most research focuses on high-resource languages. However, malicious actors share content across countries and languages, including low-resource ones. Here, we investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language. We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data -- the norm when dealing with detecting malicious activity in online platforms.