Assessing perceived organizational leadership styles through twitter text mining
This work provides a rapid assessment tool for enterprises to evaluate perceived leadership capabilities from social media interactions, though it is incremental as it applies existing methods to new data.
The researchers developed a support vector machine-based text classification tool to assess perceived organizational leadership styles from Twitter data, analyzing communication from leaders and stakeholders of top Italian companies to understand associations with leadership dimensions using a 10-factor model.
We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000-out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10-factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.