This study investigated the cultural and linguistic adaptability of the Rorschach Performance Assessment System (R-PAS), a new Rorschach administration, scoring, and interpretation system that minimizes psychometric weaknesses of the Comprehensive System (CS). This investigation addressed the validity of R-PAS measures of psychotic characteristics and psychopathology severity in Taiwan, including the incremental validity of the R-PAS relative to the CS variables measuring the same constructs. Ninety Taiwanese individuals (75 psychiatric patients and 15 nonpatients) were tested with standard R-PAS administration and scoring. Two non-Rorschach severity of disturbance measures and 2 psychosis measures served as independent criterion measures. The R-PAS measures were found to be valid in Taiwan in assessing psychotic symptoms and psychopathology severity, thus demonstrating cultural and linguistic adaptability. Moreover, hierarchical regression analyses demonstrated incremental validity for the R-PAS variables over their CS counterparts, providing support that the R-PAS revisions enhance the test psychometrically. These research findings also demonstrate the viability of the R-PAS as a Rorschach system that can be effectively employed outside the U.S. in a different language and culture.

While social media becomes a primary source of news now, it also becomes more challenging for people to distinguish the rumors and non-rumors, which attracts malicious manipulation and may lead to public health harm or economic loss. Consequently, many rumor detection models have been proposed to automatically detect the rumors based on the contents and propagation path. However, most previous works are not aware of malicious attacks, e.g., framing. Therefore, we propose a novel rumor detection framework, Adversary-Aware Rumor Detection including Weighted-Edge Transformer-Graph Network and Position-aware Adversarial Response Generator, to improve the vulnerability of detection models. To the best of our knowledge, this is the first work that can generate the adversarial response with the consideration of the response position. Experimental results show that our model achieves the state-of-theart on various rumor detection tasks by the proposed Weighted-Edge Transformer-Graph Network and can maintain the performance under the adversarial response attack after the adversarial learning by Position-aware Adversarial Response Generator. 11 The codes are released as a public download at https: //github.com/yunzhusong/AARD.

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