Despite the increasing use of digital media data in communication research, a central challenge persists: retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in communication research reveals a rudimentary one-step process. Cross-disciplinary reviews suggest an iterative query expansion guided by human knowledge and computer intelligence. Introducing the WordPPR method for keyword choice and retrieval from expansive digital media corpora, our approach entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed word(s) and subsequently selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1-3 to determine if additional data collection is needed. This method reduces the need for exhaustive corpus analysis and minimizes manual annotation, making it especially suited for large corpora. We validate WordPPR with specific topics on Twitter through simulations, contrasting it with alternative methods and demonstrating its effectiveness in targeted data mining. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond.
