Speaker landscapes: machine learning opens a window on the everyday language of opinion

We propose a new method that embeds speakers into a spatial representation according to the linguistic similarity of their contributions to a debate. Such “speaker landscapes” can be constructed quantitatively using word embeddings by annotating text corpora of speech samples by tokens representing the speakers. The way embeddings are constructed from predictive machine learning models means that speaker-tokens are placed closer together if they are easier to confuse given their speech samples. The result is a nuanced measure of similarity in speech which takes into account the wealth of linguistic signification and structure that the text prediction model can learn. We validate this tool using two South African case studies: the Twitter debate around the arrest and imprisonment of former president Jacob Zuma, and quotes from the news media about land reform. The results show that speaker landscapes make social qualities such as group membership and opinions evident, and we discuss how speaker landscapes open up new methods for studying opinion discourse with text data.