ALIGN: Analyzing Linguistic Interactions With Generalizable techNiques-A Python Library

Linguistic alignment (LA) is the tendency during a conversation to reuse each other's linguistic expressions, including lexical, conceptual, or syntactic structures. LA is often argued to be a crucial driver in reciprocal understanding and interpersonal rapport, though its precise dynamics and effects are still controversial. One barrier to more systematic investigation of these effects lies in the diversity in the methods employed to analyze LA, which makes it difficult to integrate and compare results of individual studies. To overcome this issue, we have developed ALIGN (Analyzing Linguistic Interactions with Generalizable techNiques), an open-source Python package to measure LA in conversation (https://pypi.python.org/pypi/align) along with in-depth open-source tutorials hosted on ALIGN's GitHub repository (https://github.com/nickduran/align-linguistic-alignment). Here, we first describe the challenges in the study of LA and outline how ALIGN can address them. We then demonstrate how our analytical protocol can be applied to theory-driven questions using a complex corpus of dialogue (the Devil's Advocate corpus; Duran & Fusaroli, 2017). We close by identifying further challenges and point to future developments of the field. Translational Abstract An important field of study involves how to bring about mutual understanding and close interpersonal relationships. The concept of linguistic alignment (LA), which is the tendency to mirror each other's linguistic expressions, is believed to increase such interpersonal rapport. A challenge in this area of research is that there are numerous methods available to study LA, making it difficult to systematically analyze and compare results across studies. The current study presents a new open-source Python package, ALIGN (Analyzing Linguistic Interactions with Generalizable techNiques), to help in analyzing conversation to assess the presence of LA (https://pypi.python.org/pypi/align). We also present in-depth open-source tutorials that are provided on ALIGN's GitHub repository (https://github.com/nickduran/align-linguistic-alignment). We then outline some of the concerns when conducting research on LA, and discuss how the ALIGN package can help deal with these issues. We also show how ALIGN can be used to address theory-driven questions using a complex collection of texts (e.g., the Devil's Advocate corpus; Duran & Fusaroli, 2017). Finally, we offer a summary of other considerations and future directions in this