Bad Users or Bad Content?

Community Question Answering (CQA) services have become popular methods to seek and share information. In CQA, users with an information need, or askers, post a question that community members can answer. This question-answering process allows both askers and answerers to learn through the exchange of information. CQA services have also been widely used in the education domain, as some of such services are designed specifically for students' information seeking. However, due to insufficient knowledge, lack of experience, and other reasons, students often struggle in producing quality or even appropriate content. This low quality production causes their content to be flagged or deleted, further discouraging them from participating in the CQA process and instigating a vicious cycle of bad users and bad content. In an effort to break this cycle, the work reported here focuses on identifying users whose postings demonstrate a high deletion rate with a presumption that the bad content is an indication of a struggling student rather than a malicious user. In this work, experiments are conducted on a large student-oriented online CQA community to understand struggling students' behaviors. A framework is proposed to find these users based solely on their activities. Finally, community feedback (i.e. human judgment) such as moderator evaluation or community votes for good content is used to detect these users in the early stages of their respective struggles. To evaluate this framework, we used data from Brainly, a large educational CQA service that is used in two different markets with more than 3.7 million users and 10.7 million answers. The results show that the human judgment feature identifies early-stage struggling users with high accuracy. Identifying these struggling users (students) could help educators to determine suitable ways to help their students instead of presuming them to be bad users and cutting them off from the community.