With the widespread use of social media platforms and people’s increasing dependence on them, social media has emerged as one of the most important channels for advertorials. However, there is currently a lack of research on detecting advertorials on social media platforms. This research focuses on detecting advertorials, a type of advertisement that frequently conceals itself within normal articles, blurring the nature of advertising and deceiving users. To effectively carry out research on advertorial detection, we have constructed a multi-topic advertorial dataset in Chinese with rich social information. This dataset is obtained from the Chinese question-answering platform ZHIHU, and it is publicly available to facilitate further research. Furthermore, we propose AdDetector, a novel dual-tower model that detects advertorials by jointly leveraging the article’s textual and social information. In addition, we use fine-grained sentence-level classification labels to improve the model’s generalization capability on previously unseen topic articles. Experiment results show that our model significantly improves the F 1 score by 1.29% in the intra-domain advertorial detection setting and 1.52% in the transfer setting in comparison with several strong baselines. The extensive ablation studies and thorough performance analyses also validate the complementary and beneficial values of the novel components of AdDetector. We also make our source code publicly available to facilitate future studies. This research provides crucial support for user protection and advertising management.
Bai et al. (Sat,) studied this question.