Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare. Though language technologies are growing more prevalent in these contexts, empowerment has seldom been studied in NLP, and moreover, it is inherently challenging to operationalize because of its implicit nature. This work builds from linguistic and social psychology literature to explore what characterizes empowering language. We then crowdsource a novel dataset of Reddit posts labeled for empowerment, reasons why these posts are empowering to readers, and the social relationships between posters and readers. Our preliminary analyses show that this dataset, which we call TalkUp, can be used to train language models that capture empowering and disempowering language. More broadly, TalkUp provides an avenue to explore implication, presuppositions, and how social context influences the meaning of language.

Annotating workplace bias in text is a noisy and subjective task. In encoding the inherently continuous nature of bias, aggregated binary classifications do not suffice. Best-worst scaling (BWS) offers a framework to obtain real-valued scores through a series of comparative evaluations, but it is often impractical to deploy to traditional annotation pipelines within industry. We present analyses of a small-scale bias dataset, jointly annotated with categorical annotations and BWS annotations. We show that there is a strong correlation between observed agreement and BWS score (Spearman’s r=0.72). We identify several shortcomings of BWS relative to traditional categorical annotation: (1) When compared to categorical annotation, we estimate BWS takes approximately 4.5x longer to complete; (2) BWS does not scale well to large annotation tasks with sparse target phenomena; (3) The high correlation between BWS and the traditional task shows that the benefits of BWS can be recovered from a simple categorically annotated, non-aggregated dataset.