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Framing is a process of emphasizing a certain aspect of an issue over theothers, nudging readers or listeners towards different positions on the issueeven without making a biased argument. {Here, we propose FrameAxis, a methodfor characterizing documents by identifying the most relevant semantic axes("microframes") that are overrepresented in the text using word embedding. Ourunsupervised approach can be readily applied to large datasets because it doesnot require manual annotations. It can also provide nuanced insights byconsidering a rich set of semantic axes. FrameAxis is designed toquantitatively tease out two important dimensions of how microframes are usedin the text. \textit{Microframe bias} captures how biased the text is on acertain microframe, and \textit{microframe intensity} shows how actively acertain microframe is used. Together, they offer a detailed characterization ofthe text. We demonstrate that microframes with the highest bias and intensitywell align with sentiment, topic, and partisan spectrum by applying FrameAxisto multiple datasets from restaurant reviews to political news.} The existingdomain knowledge can be incorporated into FrameAxis {by using custommicroframes and by using FrameAxis as an iterative exploratory analysisinstrument.} Additionally, we propose methods for explaining the results ofFrameAxis at the level of individual words and documents. Our method mayaccelerate scalable and sophisticated computational analyses of framing acrossdisciplines.

FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding