Recommending who to follow in the software engineering Twitter space

• Main Authors: SHARMA, Abhabhisheksh, TIAN, Yuan, SULISTYA, Agus, WIJEDASA, Dinusha, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Recommendation systems; Software engineering; Twitter; Social Media; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4304; https://ink.library.smu.edu.sg/context/sis_research/article/5307/viewcontent/a16_sharma.pdf
• Institution: Singapore Management University

With the advent of social media, developers are increasingly using it in their software development activities. Twitter is one of the popular social mediums used by developers. A recent study by Singer et al. found that software developers use Twitter to “keep up with the fast-paced development landscape.” Unfortunately, due to the general-purpose nature of Twitter, it’s challenging for developers to use Twitter for their development activities. Our survey with 36 developers who use Twitter in their development activities highlights that developers are interested in following specialized software gurus who share relevant technical tweets.To help developers perform this task, in this work we propose a recommendation system to identify specialized software gurus. Our approach first extracts different kinds of features that characterize a Twitter user and then employs a two-stage classification approach to generate a discriminative model, which can differentiate specialized software gurus in a particular domain from other Twitter users that generate domain-related tweets (aka domain-related Twitter users). We have investigated the effectiveness of our approach in finding specialized software gurus for four different domains (JavaScript, Android, Python, and Linux) on a dataset of 86,824 Twitter users who generate 5,517,878 tweets over 1 month. Our approach can differentiate specialized software experts from other domain-related Twitter users with an F-Measure of up to 0.820. Compared with existing Twitter domain expert recommendation approaches, our proposed approach can outperform their F-Measure by at least 7.63%.