Predicting information seeker satisfaction in community question answering

Abstract

Question answering communities such as Naver and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of popular portals such as Yahoo! Answers already have submitted millions of questions and received hundreds of millions of answers from other participants. However, it may also take hours -and sometime days-until a satisfactory answer is posted. In this paper we introduce the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a largescale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. Our models and predictions could be useful for a variety of applications such as user intent inference, answer ranking, interface design, and query suggestion and routing.

Exploiting Surface Features for the Prediction of Podcast Preference

“…In this respect, our goals are comparable to those of [1,6]. In particular, we want to know the effectiveness of our complete set of features, of individual features, and of features grouped by level (feed, episode, enclosure), both for classifying podcasts as Popular or Non-Popular and for ranking podcasts.…”

“…In the domain of on-line discussions, the quality of posts has been automatically assessed using a combination of features from categories designated: surface, lexical, syntactic, forum specific and similarity [12]. Community-based answers to questions have also been automatically assessed for quality, expressed as user satisfaction [1,6]. Other related work includes research which has investigated the exploitation of topic independent information for improving the quality of information retrieval.…”

Community Engagement and Quality Knowledge with Stackoverflow’s Reputation System: Applications to Bullipedia

“…Instead, most Q&A sites use collaborative voting mechanisms for users inside the community to evaluate and maintain high quality questions and answers (Tian et al 2013). By a quality answer, we mean the one that satisfies the asker (Liu et al 2008) and also other web users who will face similar problems in the future (Liu et al 2011 to traditional sources of information for programmers, like books, blogs, or other existing Q&A sites. The fact that both Atwood and Spolsky were popular bloggers contributed to its success in the early stages of the project as they brought their two communities of readers to their new site and generated the critical mass that made it work (Atwood 2008;Spolsky 2008).…”

Different Facets of Text Based Automated Question Answering System

“…The work described in paper emphasized on the improvements and changes implemented to adapt Spanish language along with Portuguese for cross-lingual extension. Liu et al, [26]has worked on issue of predicting information seeker satisfaction in collaborative question answering communities. A prediction model has been developed to predict whether a question author will be satisfied with the answers submitted by other community member.…”