Unmask It! AI-Generated Product Review Detection in Dravidian Languages
It addresses the threat of AI-generated fake reviews undermining trust in online platforms for consumers in low-resource language settings, though it is incremental by applying existing methods to new data.
This study tackled the problem of detecting AI-generated product reviews in low-resource Dravidian languages like Tamil and Malayalam, finding that transformer-based models such as Indic-BERT and XLM-RoBERTa effectively identify such content.
The rise of Generative AI has led to a surge in AI-generated reviews, often posing a serious threat to the credibility of online platforms. Reviews serve as the primary source of information about products and services. Authentic reviews play a vital role in consumer decision-making. The presence of fabricated content misleads consumers, undermines trust and facilitates potential fraud in digital marketplaces. This study focuses on detecting AI-generated product reviews in Tamil and Malayalam, two low-resource languages where research in this domain is relatively under-explored. We worked on a range of approaches - from traditional machine learning methods to advanced transformer-based models such as Indic-BERT, IndicSBERT, MuRIL, XLM-RoBERTa and MalayalamBERT. Our findings highlight the effectiveness of leveraging the state-of-the-art transformers in accurately identifying AI-generated content, demonstrating the potential in enhancing the detection of fake reviews in low-resource language settings.