Misinformation and misbehavior mining on the web(MIS2) workshop is held in Los Angeles, California, USA on February 9, 2018, and co-located with the 11th ACM International Conference on Web Search and Data Mining(WSDM 2018). Web is a dynamic ecosystem that enables malicious users to create and spread deceptive information to a wide audience in a matter of minutes. These malicious actors work on a wide variety of platforms, such as social media, e-commerce, and more. The main object of MIS2 is to discuss new and upcoming research on modeling, discovery, detection, and mitigation methods of misbehavior and misinformation on the web. MIS2 is an interdisciplinary venue for leading researchers and practitioners from the areas of data mining, social network analysis, cybersecurity, communications, human-computer interaction, and natural language processing. The topics addressed in MIS2 are extremely timely and the research presented by refereed papers and invited keynote speakers will participants a full dose of emerging research.

Information access is becoming increasingly heterogeneous. Especially when the user's information need is for exploratory purpose, returning a set of diverse results from different resources could benefit the user. For example, when a user is planning a trip to China, retrieving and showing results from vertical search engines like travel, flight information, map and Q2A sites can satisfy the user's rich and diverse information need. This heterogeneous search paradigm is useful in many contexts and brings many new challenges.

The HIA'15 workshop aims to bring together information retrieval practitioners from industry and academic researchers concerned with heterogeneous information access and search federation. We would like to create a forum to encourage discussion and exchange of ideas on heterogeneous information access in different contexts. To facilitate the discussion, we encourage submissions on ideas and results from different aspects of heterogeneous information access including aggregated search, composite retrieval, personal search, structured search, etc. Another objective of the workshop is to encourage submissions with novel ideas (e.g. new applications) on heterogeneous information access and potential future directions of this area.

Question-and-answer (Q&A) websites, such as Yahoo! Answers, Stack Overflow and Quora, have become a popular and powerful platform for Web users to share knowledge on a wide range of subjects. This has led to a rapidly growing volume of information and the consequent challenge of readily identifying high quality objects (questions, answers and users) in Q&A sites. Exploring the interdependent relationships among different types of objects can help find high quality objects in Q&A sites more accurately. In this paper, we specifically focus on the ranking problem of co-ranking questions, answers and users in a Q&A website. By studying the tightly connected relationships between Q&A objects, we can gain useful insights toward solving the co-ranking problem. However, co-ranking multiple objects in Q&A sites is a challenging task: a) With the large volumes of data in Q&A sites, it is important to design a model that can scale well; b) The large-scale Q&A data makes extracting supervised information very expensive. In order to address these issues, we propose an unsupervised Network-based Co-Ranking framework (NCR) to rank multiple objects in Q&A sites. Empirical studies on real-world Yahoo! Answers datasets demonstrate the effectiveness and the efficiency of the proposed NCR method.