Scaling constructs with semantic networks

This paper introduces a method for creating scales of constructs based on word bigram cooccurrences in natural language text. Instead of using a stop-word list to drop less useful words, we use a start-word list that enables computing the cooccurrences of only these “smart words.” In this way, we can create scales to measure communication constructs by first listing the key terms in the conceptual definition, then expanding the terms by looking up synonyms in dictionaries such as WordNet. Following this, we compute the cooccurrence network among these words with a sliding window. Next, we extract the first dimension through principal components analysis and identify the words that load most highly on the first factor. For these words, we sum the frequencies, which produces the final index for the construct. This operationalization yields index scales that have high construct validity, which contributes to external validity. Extending the procedures of classic psychometric index construction into the natural language domain avoids the biases of data based on fixed-choice questionnaires. To demonstrate the procedures for construct operationalization, we use a dataset of news stories about the BP Deepwater Horizon Gulf Oil Spill, scaling environmental uncertainty and organizational responses to it including innovation, strategic planning, and changes in organizational structure.