Can ChatGPT Predict Article Retraction Based on Twitter Mentions?

Concepts de base

Twitter mentions can predict article retraction, with ChatGPT showing superior performance compared to other methods.

Résumé

This study explores the potential of Twitter mentions in predicting article retraction using ChatGPT. It analyzes a dataset of retracted and non-retracted articles, evaluating prediction methods like manual labeling, keyword identification, machine learning models, and ChatGPT. The study uncovers the effectiveness of Twitter mentions in signaling problems before retraction and highlights ChatGPT's potential in assisting human judgment for predicting article retraction.

Abstract:

• Detecting problematic research articles is crucial.
• Study examines if Twitter mentions can predict article retraction.
• Dataset analyzed includes 3,505 retracted and non-retracted articles.
• Manual labeling and four prediction methods evaluated.

Introduction:

• Focus on correcting flawed scientific literature.
• Growing attention on article retraction for research integrity.
• Conventional approaches have limitations in detecting misconduct.

Data Extraction:

• "A dataset comprising 3,505 retracted articles and their associated Twitter mentions is analyzed."
• "The effectiveness of Twitter mentions in predicting article retraction is evaluated by four prediction methods."

Stats

Using the manual labelling results as the baseline, ChatGPT demonstrates superior performance compared to other methods.

Citations

"ChatGPT demonstrates superior performance compared to other methods."
"Twitter mentions can predict article retraction effectively."

Idées clés tirées de

Can ChatGPT predict article retraction based on Twitter mentions?

by Er-Te Zheng,... à arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16851.pdf

Questions plus approfondies

How can social media platforms beyond Twitter enhance predictions?

Expanding the analysis to include social media platforms beyond Twitter can provide a more comprehensive understanding of public opinions regarding research articles. Platforms like Facebook and Reddit offer different user demographics and engagement styles, which can offer diverse perspectives on scholarly articles. By incorporating data from these platforms, researchers can gain a broader view of how articles are perceived and discussed online. This multi-platform approach can lead to more robust predictions by capturing a wider range of reactions and sentiments towards research publications.

What are the risks associated with relying solely on AI like ChatGPT for predictions?

While AI tools like ChatGPT show promise in predicting article retractions based on social media data, there are inherent risks in relying solely on them for predictions. One significant risk is the potential for biases in the training data that could influence the model's outputs. If the AI is trained on biased or incomplete datasets, it may produce inaccurate or skewed predictions. Additionally, there is a risk of over-reliance on AI without human oversight, leading to errors that go unchecked. Ethical concerns also arise around accountability and transparency when using AI models for critical decision-making processes.

How can AI tools like ChatGPT be leveraged to improve research integrity beyond predicting retractions?

AI tools like ChatGPT have the potential to enhance research integrity in various ways beyond predicting article retractions. One application could be in identifying potential cases of plagiarism or academic misconduct by analyzing text similarities across publications. ChatGPT could assist in flagging suspicious content that warrants further investigation by researchers or publishers. Moreover, these tools could aid in automating literature reviews, helping researchers identify gaps or inconsistencies in existing knowledge more efficiently. By leveraging AI for tasks related to quality control and verification within scholarly communication, research integrity can be strengthened through increased scrutiny and vigilance against unethical practices.