When people talk, they tend to adopt the behaviors, gestures, and language of their conversational partners. This "accommodation" to one's partners is largely automatic, but the degree to which it occurs is influenced by social factors, such as gender, relative power, and attraction. In settings where such social information is not known, this accommodation can be a useful cue for the missing ... [Show full abstract] information. This is especially important in web-based communication, where social dynamics are often fluid and rarely stated explicitly. But connecting accommodation and social dynamics on the web requires accurate quantification of the different amounts of accommodation being made.
We focus specifically on accommodation in the form of "linguistic alignment": the amount that one person's word use is influenced by another's. Previous studies have used many measures for linguistic alignment, with no clear standard. In this paper, we lay out a set of desiderata for a linguistic alignment measure, including robustness to sparse and short messages, explicit conditionality, and consistency across linguistic features with different baseline frequencies. We propose a straightforward and flexible model-based framework for calculating linguistic alignment, with a focus on the sparse data and limited social information observed in social media. We show that this alignment measure fulfills our desiderata on simulated data. We then analyze a large corpus of Twitter data, both replicating previous results and extending them: Our measure's improved resolution reveals a previously undetectable effect of interpersonal power in Twitter interactions.