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Disclosure Standards for Social Media and Generative Artificial Intelligence Research: Toward Transparency and
Ganna Kostygina1, Yoonsang Kim1, Zachary Seeskin1
1NORC at the University of Chicago, USA.
Social Media + Society
|January 19, 2024
Summary
Social media research lacks standards, creating risks of misuse and bias. This study proposes clear criteria and guidelines for transparent data collection and analysis to ensure research quality and credibility.
Area of Science:
- Social Sciences
- Information Science
- Data Science
Background:
- Social media platforms are primary information sources for research.
- Lack of procedural standards and transparency in social media data analysis poses risks.
- Proprietary algorithms and artificial intelligence (AI) introduce bias and limit replicability.
Purpose of the Study:
- To address the "wild west" nature of social media research.
- To propose criteria for evaluating the quality of social media data studies.
- To offer guidelines for transparent and proper use of social data in research.
Main Methods:
- Grounded in established scientific practice.
- Proposed criteria for evaluating study quality.
- Developed clear documentation and disclosure guidelines.
Main Results:
- Established criteria for assessing social media research quality.
- Provided documentation guidelines for transparency.
- Proposed a checklist for minimal reporting standards.
Conclusions:
- Implementing proposed criteria enhances the quality, credibility, and comparability of social media research.
- Transparency in data collection and analysis is crucial for reliable findings.
- Standardized reporting will mitigate risks associated with social media data analysis.
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