Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success

Farzana Rashid, Tommaso Fornaciari, Dirk Hovy, Eduardo Blanco, Fernando Vega-Redondo

Abstract
When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. We study the chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. We manually annotate a set of about 5,500 chat interactions with four dimensions of interaction styles (motivation, cooperation, equality, advice). We find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success. Our findings indicate that successful communicators are also successful in other domains.
Anthology ID:
2020.findings-emnlp.214
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2366–2371
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.214
DOI:
10.18653/v1/2020.findings-emnlp.214
Bibkey:
Cite (ACL):
Farzana Rashid, Tommaso Fornaciari, Dirk Hovy, Eduardo Blanco, and Fernando Vega-Redondo. 2020. Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2366–2371, Online. Association for Computational Linguistics.
Cite (Informal):
Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success (Rashid et al., Findings 2020)
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