Social media have emerged to be increasingly popular and have been used as tools for gathering and propagating information. However, the vigorous growth of social media contributes to the fast-spreading and far-reaching rumors. Rumor detection has become a necessary defense. Traditional rumor detection methods based on hand-crafted feature selection are replaced by automatic approaches that are based on Artificial Intelligence (AI). AI decision making systems need to have the necessary means, such as explainability to assure users their trustworthiness. Inspired by the thriving development of Generative Adversarial Networks (GANs) on text applications, we propose LEX-GAN, a GAN-based layered explainable rumor detector to improve the detection quality and provide explainability. Unlike fake news detection that needs a previously collected verified news database, LEX-GAN realizes explainable rumor detection based on only tweet-level text. LEX-GAN is trained with generated non-rumor-looking rumors. The generators produce rumors by intelligently inserting controversial information in non-rumors, and force the discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. The layered structures in both generative and discriminative model contributes to the high performance. We show LEX-GAN's mutation detection ability in textural sequences by performing a gene classification and mutation detection task.
In the paper, authors proposed a generative adversarial network-based rumor detection model that can label short text like Twitter posts as rumor or not. The model can further highlight the words that are responsible for the rumor accusation. 
Proposed model consists of 4 sub models: a G_Where model finds the word to replace so to create an artificial rumor; a G_replace model decides what the replacement word should be; a D_classify model detects if a sequence is a rumor; a final D_explain model pinpoints the word of concern. D_ models and G_ models are trained in an adversarial competing way.
Experiments showed that the LEX-GAN model outperforms other non-GAN models by a large margin on a previously published rumor dataset (PHEME) and in a gene classification task.
My questions:
1) The task modeled is essentially a word replacement detection problem. Is this equivalent to rumor detection? Even if it performs really well on a static dataset, it could be very vulnerable to attackers. Various previous works mentioned in the paper, including the PHEME paper by Kochkina et al, used supporting evidence for detection, which sounds like a more robust approach.
2) Authors didn't explain the rationale behind the choice of model structure, e.g. GRU vs LSTM vs Conv. The different structures have been used in mix in the paper. Are those choices irrelevant or critical?
3) I would like to see more discussion on the nature of errors from those models, but it's lacking in the paper. This could be critical to understand the model’s ability and limitation, esp given that it’s not looking at supporting evidences from other sequences.
Small errors noticed: The citation for PHEME paper (Kochkina et al) points to a preprint version, while an ACL Anthology published version exists.