@article{e79f38226ef34d5f9477a6dc6ea62e6d,
title = "ALIGN: Analyzing Linguistic Interactions with Generalizable tech Niques-A python library",
abstract = "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.",
keywords = "Automated text analysis, Conflict, Deception, Interpersonal coordination, Linguistic alignment",
author = "Nicholas Duran and Alexandra Paxton and Riccardo Fusaroli",
note = "Funding Information: The collection of the DA data was supported by the National Science Foundation under a Minority Postdoctoral Research Fellowship [SBE-1103356] to author Nicholas D. Duran. Funding for this project also came from an Aarhus University Interacting Minds Centre seed funding grant in 2013 to authors Riccardo Fusaroli (PI), Nicholas D. Duran, and Alexandra Paxton (“The Linguistic Dynamics of Conflict And Deception”). This project was also funded in part by a National Science Foundation grant [DUE-1660894] to Nicholas D. Duran and a Moore-Sloan Data Science Environments Fellowship to Alexandra Paxton (thanks to grants from the Gordon and Betty Moore Foundation [Grant GBMF3834] and the Alfred P. Sloan Foundation [Grant 2013-10-27] to the University of California, Berkeley). Please note that an early presentation on this work was given in 2015 at the Annual Meeting of the Society for Computers in Psychology. Our thanks go to Rick Dale (University of California, Los Angeles) for his crucial feedback in the development and design of the DA study analyzed in this paper and for his thoughtful conversations about the nature and quantification of alignment. We thank J. P. Gonzales and Josh Espano for helping to collect and transcribe the DA study data while serving as research assistants at the University of California, Merced, and Grace Petersen while a research assistant at Arizona State University. Finally, we would also like to thank Nelle Varoquaux (University of California, Berkeley) for her assistance and advice on Python packaging and Zoe Hopkins (University of Edinburgh) for sharing her early work on automated analyses of syntactic alignment. Funding Information: The collection of the DA data was supported by the National Science Foundation under a Minority Postdoctoral Research Fellowship [SBE-1103356] to author Nicholas D. Duran. Funding for this project also came from an Aarhus University Interacting Minds Centre seed funding grant in 2013 to authors Riccardo Fusaroli (PI), Nicholas D. Duran, and Alexandra Paxton ({"}The Linguistic Dynamics of Conflict And Deception{"}). This project was also funded in part by a National Science Foundation grant [DUE-1660894] to Nicholas D. Duran and a Moore-Sloan Data Science Environments Fellowship to Alexandra Paxton (thanks to grants from the Gordon and Betty Moore Foundation [Grant GBMF3834] and the Alfred P. Sloan Foundation [Grant 2013-10-27] to the University of California, Berkeley). Please note that an early presentation on this work was given in 2015 at the Annual Meeting of the Society for Computers in Psychology. Our thanks go to Rick Dale (University of California, Los Angeles) for his crucial feedback in the development and design of the DA study analyzed in this paper and for his thoughtful conversations about the nature and quantification of alignment. We thank J. P. Gonzales and Josh Espano for helping to collect and transcribe the DA study data while serving as research assistants at the University of California, Merced, and Grace Petersen while a research assistant at Arizona State University. Finally, we would also like to thank Nelle Varoquaux (University of California, Berkeley) for her assistance and advice on Python packaging and Zoe Hopkins (University of Edinburgh) for sharing her early work on automated analyses of syntactic alignment. Publisher Copyright: {\textcopyright} 2019 American Psychological Association.",
year = "2019",
month = aug,
doi = "10.1037/met0000206",
language = "English (US)",
volume = "24",
pages = "419--438",
journal = "Psychological Methods",
issn = "1082-989X",
publisher = "American Psychological Association Inc.",
number = "4",
}