@poster {
    orvium-648c23454522a414fdf4c8b4,
    title = "Can ChatGPT predict article retraction through tweets?",
    abstract = "Detecting academic misconduct is a vital task, and reader comments have become a key source for identifying such misconduct. This study aims to investigate whether tweets can predict the retraction of articles. A total of 3,505 retracted articles mentioned on Twitter from 2012 to 2021 were collected, and the Coarsened Exact Matching (CEM) method was used to gather 3,505 non-retracted articles with similar features, along with all relevant tweets. The study analyzed the tweets through keyword identification, machine learning methods, and ChatGPT. The results showed that tweets could predict the retraction of articles to some extent, but the predictive performance of each model still needs to be improved. ChatGPT showed higher accuracy in predicting article retractions than other models, and could assist manual prediction, accelerating the purification of academic misconduct. However, the ChatGPT prediction method still has issues with logical inference and over-interpretation, which need to be improved in future studies.",
    keywords = "social media, prediction, retraction, machine learning, ChatGPT",
    author = "Er-Te Zheng and Hui-Zhen Fu and Zhichao Fang",
    year = "2023",
    doi = "10.55835/644126a8763e8d2091a0cfdc",
    language = "English",
    url = "https://dapp.orvium.io/deposits/648c23454522a414fdf4c8b4/view",
}