Not only did COVID-19 give rise to a global pandemic, but also it resulted in an infodemic comprising misinformation, rumor, and propaganda. The consequences of this infodemic can erode public trust, impede the containment of the virus, and outlive the pandemic itself. The evolving and fragmented media landscape, particularly the extensive use of social media, is a crucial driver of the spread of misinformation. Focusing on the Chinese social media Weibo, we collected four million tweets, from December 9, 2019, to April 4, 2020, examining misinformation identified by the fact-checking platform Tencent-a leading Chinese tech giant. Our results show that the evolution of misinformation follows an issue-attention cycle pertaining to topics such as city lockdown, cures and preventive measures, school reopening, and foreign countries. Sensational and emotionally reassuring misinformation characterizes the whole issue-attention cycle, with misinformation on cures and prevention flooding social media. We also study the evolution of sentiment and observe that positive sentiment dominated over the course of Covid, which may be due to the unique characteristic of "positive energy" on Chinese social media. Lastly, we study the media landscape during Covid via a case study on a controversial unproven cure known as Shuanghuanglian, which testifies to the importance of scientific communication in a plague. Our findings shed light on the distinct characteristics of misinformation and its cultural, social, and political implications, during the COVID-19 pandemic. The study also offers insights into combating misinformation in China and across the world at large.

Purpose This study/paper aims to understand the public perceptions of AI through mass media discourse. In the past few years, significant progress has been made in the field of artificial intelligence (AI). The benefits of AI are obvious, but there is still huge uncertainty and controversy over the public perception of AI. How does the mass media conceptualize AI? Design/methodology/approach In this paper, the authors analyze the evolution of AI covered by five major news media outlets in the past 30 years from 7 dimensions: scientific subject, keyword, country, institution, people, topic and opinion polarity. Findings First of all, different subjects are competing for and dividing up the right to speak of AI, leading to the gradual fragmentation of the concept of AI. Second, reporting on AI often includes reference to commercial institutions and scientists, showing a successful integration of science and business. Moreover, the result of topic modeling shows that news media mainly defines AI from three perspectives: an imagination, a commercial product and a field of scientific research. Finally, negative reports have focused on various issues relating to AI ethics. Originality/value The results can help bridge various conversations surrounding AI and promote richer discussions, increase the participation of scientists, businesses, governments and the public and provide more perspectives on the functions, prospects and pitfalls of AI.

Diffusion channels are critical to determining the adoption scale, which leads to the ultimate impact of an innovation. The aim of this study is to develop an integrative understanding of the impact of two diffusion channels (i.e., broadcasting vs. virality) on innovation adoption. Using citations of a series of classic algorithms and the time series of co‐authorship as the footprints of their diffusion trajectories, we propose a novel method to analyze the intertwining relationships between broadcasting and virality in the innovation diffusion process. Our findings show that broadcasting and virality have similar diffusion power, but play different roles across diffusion stages. Broadcasting is more powerful in the early stages but may be gradually caught up or even surpassed by virality in the later period. Meanwhile, diffusion speed in virality is significantly faster than broadcasting and members from virality channels tend to adopt the same innovation repetitively.

As an endoscopic technology for the enhancement of images, linked color imaging (LCI) performs well when used for the early detection and diagnosis of gastrointestinal cancer. However, literature data are lacking for LCI in the detection of high-grade gastric intraepithelial neoplasia. Therefore, the aim of the present study was to investigate the efficacy of LCI compared with traditional white light imaging (WLI) in the detection of high-grade gastric intraepithelial neoplasia via the comparison of detection rates between senior and junior endoscopists using both techniques. Overall, 84 lesions from 81 patients with high-grade gastric intraepithelial neoplasia diagnosed between January 2017 and December 2017 were considered. Following the exclusion of three patients with two lesions, 78 patients who had only one lesion were enrolled. The two types of endoscopy, WLI and LCI, were performed in the same patients under the same conditions. Four senior and four junior endoscopists retrospectively compared the images. The detection rate of high-grade gastric intraepithelial neoplasia was significantly higher with LCI than with WLI when performed by senior and junior endoscopists. With WLI, the detection rate obtained by senior endoscopists was significantly higher than that obtained by junior endoscopists. However, for LCI, the detection rates for junior and senior endoscopists were comparable. Interobserver agreement was good to satisfactory. These findings indicate that LCI is superior to WLI in the detection and identification of gastric cancer and provides highly accurate diagnostic results from endoscopic examinations, regardless of the experience of the endoscopist. LCI may be used to narrow the gap in the detection rate of high-grade gastric intraepithelial neoplasia between junior and senior endoscopists.