Work

Quantity or quality? Comparing social media data sampling strategies for government crisis communication research

Year: 2025

Type: article

Abstract: Social media platforms are popular data sources for communication research, with X (formerly Twitter) being the most commonly used. However, strategies for high-quality social media data collection that form the cornerstone of rigorous research are rarely discussed or evaluated. In this paper, we use a case study of government crisis communication during Covid-19 in Germany and Italy to compare deductive and inductive sampling strategies on X in two different timeframes. We used different metrics (Jaccard index, precision, and recall) and different comparisons (relevant users and content, top users and terms, and tweeting frequency over time) to analyze which sampling strategy would provide high-quality data for research. Our results revealed that the deductive sampling strategy outperformed the inductive strategy in all the studied cases, this emphasizes the importance of data quality over quick access or the size of the data set. Further implications for comparative and computational research are discussed.

Source: International Journal of Disaster Risk Reduction

DOI: 10.1016/j.ijdrr.2025.105531

Authors: Jingyuan Yu, Emese Domahidi, Khaoula Benmaarouf, Nadine Steinmetz

Institutions: Technische Universität Ilmenau, University of Applied Sciences Erfurt

Language: English

Topic: Public Relations and Crisis Communication

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,200

Funder: Deutsche Forschungsgemeinschaft