  Software developers have benefited from various sources of knowledge such as
forums, question-and-answer sites, and social media platforms to help them in
various tasks. Extracting software-related knowledge from different platforms
involves many challenges. In this paper, we propose an approach to improve the
effectiveness of knowledge extraction tasks by performing cross-platform
analysis. Our approach is based on transfer representation learning and word
embeddings, leveraging information extracted from a source platform which
contains rich domain-related content. The information extracted is then used to
solve tasks in another platform (considered as target platform) with less
domain-related contents. We first build a word embeddings model as a
representation learned from the source platform, and use the model to improve
the performance of knowledge extraction tasks in the target platform. We
experiment with Software Engineering Stack Exchange and Stack Overflow as
source platforms, and two different target platforms, i.e., Twitter and
YouTube. Our experiments show that our approach improves performance of
existing work for the tasks of identifying software-related tweets and helpful
YouTube comments.
