  Community Question Answering (CQA) sites have spread and multiplied
significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are
becoming popular amongst people interested in finding answers to diverse
questions. One practical way of finding such answers is automatically
predicting the best candidate given existing answers and comments. Many studies
were conducted on answer prediction in CQA but with limited focus on using the
background information of the questionnaires. We address this limitation using
a novel method for predicting the best answers using the questioner's
background information and other features, such as the textual content or the
relationships with other participants. Our answer classification model was
trained using the Stack Exchange dataset and validated using the Area Under the
Curve (AUC) metric. The experimental results show that the proposed method
complements previous methods by pointing out the importance of the
relationships between users, particularly throughout the level of involvement
in different communities on Stack Exchange. Furthermore, we point out that
there is little overlap between user-relation information and the information
represented by the shallow text features and the meta-features, such as time
differences.
