2020 EMNLP EMNLP 2020

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

Abstract

AbstractWhen 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.

The Questioner
🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — interaction style
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning