A Chinese PhD dropout who has become a viral influencer exposing academic misconduct is building a growing army of followers eager to reveal ethical malpractice among scientific researchers.The former PhD candidate in bioscience at the prestigious
Beihang University,
Geng Hongwei, dropped out in 2025, and became a full-time whistle-blower with over two million followers on an online platform.Geng attracted public attention for a video in April, in which he questioned a paper from the team of the dean of the School of Life Sciences and Technology at
Tongji University,
Wang Ping, published in the British science journal
Nature.Geng noted the abnormal data in the paper.He listed them one by one in his video, for instance the last digit of almost the whole column of figures was 5, and the figures in one column exactly differ by 0.3 from another column.PhD dropout
Geng Hongwei, above, talks about the methods he uses to check academic research work. Photo: QQ.comHe also noted that the 196 laboratory mice’s weight data in the paper were rounded to two decimal places, which was abnormally precise as mice move a lot and cannot be weighed accurately.Also, only one out of the 196 figures ended with zero, which he believed was proof that the figures were not random, but manually fabricated.Geng humorously commented that the paper reflected the scholars’ “love for five”, and advised Wang’s team to improve their fabrication skills.The video has so far attracted 1.5 million views and nearly 5,.The
Tongji University launched an investigation and acknowledged the academic misconduct in May.A modern medical research laboratory complete with the latest technology. Photo: ShutterstockThe university dismissed Wang from his dean position, lowered his professional rank by two levels. The paper’s first author,
Jin Jiali, was also terminated from her employment with the university.Further ReadingGeng also exposed several other academic dishonesty cases, such as a 2024 paper by the
Nankai University College of Life Sciences’ dean
Chen Quan’s team, published in
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Nature Cancer, and a 2024 paper by the
Sun Yat-sen University Cancer Centre’s researcher
Kang Tiebang’s team, published in
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Nature Cell Biology.The method he used does not require superb skills. He simply used the Excel app to summarise data patterns, and used artificial intelligence (AI) to check if the images were authentic.He said that if they forged the data “more skilfully”, he would not have spotted them.An empty, yet imposing, auditorium at a university. Photo: ShutterstockGeng did not work alone. After his first videos went viral, hundreds of people came to him reporting other instances of academic misconduct.Geng said he only targeted the papers in journals under
Nature, by prestigious scholars who were recognised by China’s Changjiang Scholars Programme and the National Science Fund for Distinguished Young Scholars, as he believed these people should stand for the highest level of scientific research.Born to migrant worker parents in the countryside in northeastern China’s Liaoning province, Geng went all the way to the doctoral school with his hard work.He became a PhD candidate in 2020, struggled for five years and finally quit last year.Scientists, above, using a microscope and test tubes to carry out research. Photo: Getty ImagesIdealist Geng said one reason behind him dropping out was that the reality did not match his dream, which was to make even just a little breakthrough in a field that is both meaningful and of interest to him, with real knowledge and research.He said he could also do the bare minimum to get a diploma, but he would feel ashamed.His account was also making money, which prompted him to quit research more resolutely.Geng did not bash the researchers when he spotted dishonesty. He also offered them chances to rectify their mistakes by themselves, by naming just their universities in a recent video to give them a reminder.He said his goal was to push the Chinese academic world to reform, at least to standardise the research replication procedures, which could largely prevent false data.