Predicting leadership perception with large-scale natural language data

Abstract

We present a computational method for predicting, and identifying the correlates of, leadership perceptions for prominent individuals. Our approach proxies knowledge representations for these individuals using high-dimensional semantic vectors derived from large-scale news media datasets. It then applies machine learning techniques to build a model that maps these vectors onto participant ratings of leadership effectiveness. This method greatly outperforms other approaches and achieves accuracy rates comparable to human participants in predicting leadership effectiveness judgments. Crucially, it relies on attributes and associations identified by established theories of leadership perception—notably implicit leadership theories—as guiding lay leadership perception. Thus, our model appears to have learnt the same implicit leadership cues as our human participants. In addition, we show that our approach can be used to not only predict leadership effectiveness judgments, but also to identify dimensions that people associate with effective leadership, as well as quantify the extent of this association for each dimension. We illustrate the broad applicability of our method by using it to predict leadership perceptions for over 6000 individuals in the public sphere, and to algorithmically uncover the particular traits, concepts, and attributes that people most strongly associate with effective leaders.