CIQFIRE: Overview of the Shared Task on FineGrained Classification of Insincere Questions Given the scale of CQA forums on the web, manual identification of Insincere Questions becomes challenging and costly. Hence automated approaches are needed, in addition to manual moderation to filter insincere questions in CQA sites. While this can be simplistically formulated as a binary classification task, insincere questions exhibit diverse characteristics such as Rhetorical, or DisparagingInflammatory, Hypothetical, or Objectionable. The proposed CIQ track aims to find a suitable model for Categorisation of Insincere Questions(CIQ) on CQA forums. We propose that segmenting insincerenoninformation seeking questions will not only provide better identification, but also help to enable effective countermeasures based on the fine grained category it belongs to.

Discovering, Explaining and Summarizing Controversial Discussions in Community Q&A Sites Developers often look for solutions to programming problems in community Qu0026A sites like Stack Overflow. Due to the crowdsourcing nature of these Qu0026A sites, many userprovided answers are wrong, less optimal or outofdate. Relying on communitycurated quality indicators (e.g., accepted answer, answer vote) cannot reliably identify these answer problems. Such problematic answers are often criticized by other users. However, these critiques are not readily discoverable when reading the posts. In this paper, we consider the answers being criticized and their critique posts as controversial discussions in community Qu0026A sites. To help developers notice such controversial discussions and make more informed choices of appropriate solutions, we design an automatic open information extraction approach for systematically discovering and summarizing the controversies in Stack Overflow and exploiting official API documentation to assist the understanding of the discovered controversies. We apply our approach to millions of javaandroidtagged Stack overflow questions and answers and discover a large scale of controversial discussions in Stack Overflow. Our manual evaluation confirms that the extracted controversy information is of high accuracy. A user study with 18 developers demonstrates the usefulness of our generated controversy summaries in helping developers avoid the controversial answers and choose more appropriate solutions to programming questions.