Automated Detection of Leadership Qualities Using Textual Data at the Message Level

Abstract

Efficient leadership plays an important role in organizations, with the military being one of the more obvious examples of this statement. In this context, it is not surprising that ensuring a culture of excellence is at the heart of Navy leadership. However, it is not easy to maintain or increase the quality of leadership among staff, as such efforts require constant training and practice. To address this need for continuous monitoring and improvement in human leadership expressed in everyday communication, we demonstrate the feasibility of automatically detecting and classifying military leadership messages. We achieve this goal by 1) curating a data set of short text messages that are written in the military-specific language, have some characteristics of spoken language, and are human-annotated with labels referring to selected leadership roles and 2) demonstrating the performance of selected automation methods that allow classes to be predicted for each analyzed message. This study shows that recent deep learning methods provide reasonable performance, even when limited data is provided. Future efforts should focus on creating an automated self-assessment tool that would enable continuous monitoring and training of leadership skills required in the Navy domain.

Keywords

• Automatic detection
• leadership
• message level
• natural language processing
• navy leadership
• twitter