Noise Pollution: A Multi-Step Approach to Assessing the Consequences of (Not) Validating Search Terms on Automated Content Analyses

Abstract

Advances in analytical methodologies and an avalanche of digitized data have opened new avenues for (digital) journalism research—and with it, new challenges. One of these challenges concerns the sampling and evaluation of data using (non-validated) search terms in combination with automated content analyses. This challenge has largely been neglected by research, which is surprising, considering that noise slipping in during the process of data collection can generate great methodological concerns. To address this gap, we first offer a systematic interdisciplinary literature review, revealing that the validation of search terms is far from acknowledged as a required standard procedure, both in and beyond journalism research. Second, we assess the consequences of validating search terms, using a multi-step approach and investigating common research topics from the field of (digital) journalism research. Our findings show that careless application of non-validated search terms has its pitfalls: while scattershot search terms can make sense in initial data exploration, final inferences based on insufficiently validated search terms are at higher risk of being obscured by noise. Consequently, we provide a step-by-step recommendation for developing and validating search terms.