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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Mimicry in online conversations : an exploratory study of linguistic analysis techniques. / Carrick, Tom; Rashid, Awais; Taylor, Paul Jonathon.

Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, 2016.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Carrick, T, Rashid, A & Taylor, PJ 2016, Mimicry in online conversations: an exploratory study of linguistic analysis techniques. in Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, IEEE/ACM ASONAM 2016, San Francisco, United States, 18/08/16. https://doi.org/10.1109/ASONAM.2016.7752318

APA

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

Vancouver

Carrick T, Rashid A, Taylor PJ. Mimicry in online conversations: an exploratory study of linguistic analysis techniques. In Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE. 2016 https://doi.org/10.1109/ASONAM.2016.7752318

Author

Carrick, Tom ; Rashid, Awais ; Taylor, Paul Jonathon. / Mimicry in online conversations : an exploratory study of linguistic analysis techniques. Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on. IEEE, 2016.

Bibtex

@inproceedings{69003688ebef433ea7cff2c25fe610ac,
title = "Mimicry in online conversations: an exploratory study of linguistic analysis techniques",
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.",
author = "Tom Carrick and Awais Rashid and Taylor, {Paul Jonathon}",
year = "2016",
month = aug,
day = "18",
doi = "10.1109/ASONAM.2016.7752318",
language = "English",
isbn = "9781509028474",
booktitle = "Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on",
publisher = "IEEE",
note = "IEEE/ACM ASONAM 2016 ; Conference date: 18-08-2016 Through 21-08-2016",

}

RIS

TY - GEN

T1 - Mimicry in online conversations

T2 - IEEE/ACM ASONAM 2016

AU - Carrick, Tom

AU - Rashid, Awais

AU - Taylor, Paul Jonathon

PY - 2016/8/18

Y1 - 2016/8/18

N2 - 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.

AB - 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.

U2 - 10.1109/ASONAM.2016.7752318

DO - 10.1109/ASONAM.2016.7752318

M3 - Conference contribution/Paper

SN - 9781509028474

BT - Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on

PB - IEEE

Y2 - 18 August 2016 through 21 August 2016

ER -