Progress in technology and increased levels of private investment in startup AI companies is accelerating, according to the 2021 AI Index, an annual study of AI impact and progress developed by an interdisciplinary team at the Stanford Institute for Human-Centered Artificial Intelligence. Indeed, AI is showing up just about everywhere. In recent weeks, there have been stories of how AI is used to monitor the emotional state of cows and pigs, dodge space junk in orbit, teach American Sign Language, speed up assembly lines, win elite crossword puzzle tournaments, assist fry cooks with hamburgers, and enable "hyperautomation." Soon there will be little left for humans to do beyond writing long-form journalism -- until that, too, is replaced by AI. The text generation engine GPT-3 from OpenAI is potentially revolutionary in this regard, leading a New Yorker essay to claim: "Whatever field you are in, if it uses language, it is about to be transformed." AI is marching forward, and its wonders are increasingly evident and applied.

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The rapid development of social media changes the lifestyle of people and simultaneously provides an ideal place for publishing and disseminating rumors, which severely exacerbates social panic and triggers a crisis of social trust. Early content-based methods focused on finding clues from the text and user profiles for rumor detection. Recent studies combine the stances of users' comments with news content to capture the difference between true and false rumors. Although the user's stance is effective for rumor detection, the manual labeling process is time-consuming and labor-intensive, which limits the application of utilizing it to facilitate rumor detection. In this paper, we first finetune a pre-trained BERT model on a small labeled dataset and leverage this model to annotate weak stance labels for users' comment data to overcome the problem mentioned above. Then, we propose a novel Stance-aware Reinforcement Learning Framework (SRLF) to select high-quality labeled stance data for model training and rumor detection. Both the stance selection and rumor detection tasks are optimized simultaneously to promote both tasks mutually. We conduct experiments on two commonly used real-world datasets. The experimental results demonstrate that our framework outperforms the state-of-the-art models significantly, which confirms the effectiveness of the proposed framework.

User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two different types of attribute interactions, inner interactions and cross interactions: inner interactions are those between only user attributes or those between only item attributes; cross interactions are those between user attributes and item attributes. Existing models do not distinguish these two types of attribute interactions, which may not be the most effective way to exploit the information carried by the interactions. To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation. In our model, the two essential recommendation procedures, characteristic learning and preference matching, are explicitly conducted through graph learning (based on inner interactions) and node matching (based on cross interactions), respectively. Experimental results show that our model outperforms state-of-the-art models. Further studies verify the effectiveness of GMCF in improving the accuracy of recommendation.