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

Research output: Contribution in Book/Report/ProceedingsConference contribution

Published
Publication date18/08/2016
Host publicationAdvances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on
PublisherIEEE
ISBN (Electronic)9781509028467
ISBN (Print)9781509028474
<mark>Original language</mark>English
EventIEEE/ACM ASONAM 2016 - San Francisco, United States

Conference

ConferenceIEEE/ACM ASONAM 2016
CountryUnited States
CitySan Francisco
Period18/08/1621/08/16

Conference

ConferenceIEEE/ACM ASONAM 2016
CountryUnited States
CitySan Francisco
Period18/08/1621/08/16

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.