Chinese creator Tianran Mu went viral for mimicking the eerie, unsettling aesthetic of AI videos, but his work is 100 percent human. Our fellow terminally online readers probably have seen this video, which originated on Chinese social media . In it, two guys who look at first like they are about to get into a fistfight suddenly break out into a romantic, yet slightly robotic tango dance routine. The next second, they pull a wine glass and a bowl of noodles out of nowhere. It looks like it's generated by AI, but it isn't.

Fox News White House correspondent Jacqui Heinrich has the latest on the countries' alliance amid Chinese tensions on'Special Report.' Japan's defense chief Friday called for the bolstering of its anti-drone capability after a drone footage posted on Chinese social media showed a Japanese aircraft carrier docked at a restricted navy port west of Tokyo. Defense Minister Minoru Kihara called it a serious security threat. Kihara's acknowledgement of the vulnerability comes more than a month after a video filmed by a drone showed JS Izumo, one of two Japanese helicopter carriers, being retrofitted to carry stealth fighters to strengthen Japan's counter-strike capability in the face of China's assertive military actions in the Indo-Pacific. The footage, also showing plants, buildings and other facility at the Japan Maritime Self-Defense Force's Yokosuka naval base was posted on a Chinese social media site in March, prompting investigation by ministry officials.

In the realm of social media, users frequently convey personal sentiments, with some potentially indicating cognitive distortions or suicidal tendencies. Timely recognition of such signs is pivotal for effective interventions. In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification. We propose a comprehensive benchmark using both supervised learning and large language models, especially from the GPT series, to evaluate performance on these datasets. To assess the capabilities of the large language models, we employed three strategies: zero-shot, few-shot, and fine-tuning. Furthermore, we deeply explored and analyzed the performance of these large language models from a psychological perspective, shedding light on their strengths and limitations in identifying and understanding complex human emotions. Our evaluations underscore a performance difference between the two approaches, with the models often challenged by subtle category distinctions. While GPT-4 consistently delivered strong results, GPT-3.5 showed marked improvement in suicide risk classification after fine-tuning. This research is groundbreaking in its evaluation of large language models for Chinese social media tasks, accentuating the models' potential in psychological contexts. All datasets and code are made available.

Named entity recognition (NER) in Chinese social media is important but difficult because of its informality and strong noise. Previous methods only focus on in-domain supervised learning which is limited by the rare annotated data. However, there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts. The unified model contains two major functions. One is for cross-domain learning and another for semi-supervised learning. Cross-domain learning function can learn out-of-domain information based on domain similarity. Semi-Supervised learning function can learn in-domain unannotated information by self-training. Both learning functions outperform existing methods for NER in Chinese social media. Finally, our unified model yields nearly 11% absolute improvement over previously published results.