  In open source software (OSS) communities, existing leadership indicators are
dominantly measured by code contribution or community influence. Recent studies
on emergent leadership shed light on additional dimensions such as intellectual
stimulation in collaborative communications. To that end, this paper proposes
an automated approach, named iLead, to mine communication styles and identify
emergent leadership behaviors in OSS communities, using issue comments data. We
start with the construction of 6 categories of leadership behaviors based on
existing leadership studies. Then, we manually label leadership behaviors in
10,000 issue comments from 10 OSS projects, and extract 304 heuristic
linguistic patterns which represent different types of emergent leadership
behaviors in flexible and concise manners. Next, an automated algorithm is
developed to merge and consolidate different pattern sets extracted from
multiple projects into a final pattern ranking list, which can be applied for
the automatic leadership identification. The evaluation results show that iLead
can achieve a median precision of 0.82 and recall of 0.78, outperforming ten
machine/deep learning baselines. To demonstrate practical usefulness, we also
conduct empirical analysis and human evaluation of the identified leadership
behaviors from iLead. We argue that emergent leadership behaviors in issue
discussion should be taken into consideration to broaden existing OSS
leadership viewpoints. Practical insights on community building and leadership
skill development are offered for OSS community and individual developers,
respectively.
