Mimicry in online conversations: an exploratory study of linguistic analysis techniques

Tom Carrick, Awais Rashid, Paul Jonathon Taylor

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

2 Citations (Scopus)

Abstract

A number of computational techniques have been proposed that aim to detect mimicry in online conversations. In this paper, we investigate how well these reflect the prevailing cognitive science model, i.e. the Interactive Alignment Model. We evaluate Local Linguistic Alignment, word vectors, and Language Style Matching and show that these measures tend to show the features we expect to see in the IAM, but significantly fall short of the work of human classifiers on the same data set. This reflects the need for substantial additional research on computational techniques to detect mimicry in online conversations. We suggest further work needed to measure these techniques and others more accurately.
Original languageEnglish
Title of host publicationAdvances in Social Networks Analysis and Mining (ASONAM)
Subtitle of host publication2016 IEEE/ACM International Conference on
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherIEEE
Pages732-736
Number of pages5
ISBN (Print)9781509028474
DOIs
Publication statusPublished - 18 Aug 2016

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    Carrick, T., Rashid, A., & Taylor, P. J. (2016). Mimicry in online conversations: an exploratory study of linguistic analysis techniques. In R. Kumar, J. Caverlee, & H. Tong (Eds.), Advances in Social Networks Analysis and Mining (ASONAM) : 2016 IEEE/ACM International Conference on (pp. 732-736). IEEE. https://doi.org/10.1109/ASONAM.2016.7752318