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ASK: A taxonomy of accuracy, social, and knowledge information seeking posts in social question and answering
Author(s) -
Liu Zhe,
Jansen Bernard J.
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23655
Subject(s) - ask price , computer science , question answering , classifier (uml) , taxonomy (biology) , information retrieval , natural language processing , artificial intelligence , botany , biology , economy , economics
Many people turn to their social networks to find information through the practice of question and answering. We believe it is necessary to use different answering strategies based on the type of questions to accommodate the different information needs. In this research, we propose the ASK taxonomy that categorizes questions posted on social networking sites into three types according to the nature of the questioner's inquiry of accuracy, social, or knowledge. To automatically decide which answering strategy to use, we develop a predictive model based on ASK question types using question features from the perspectives of lexical, topical, contextual, and syntactic as well as answer features. By applying the classifier on an annotated data set, we present a comprehensive analysis to compare questions in terms of their word usage, topical interests, temporal and spatial restrictions, syntactic structure, and response characteristics. Our research results show that the three types of questions exhibited different characteristics in the way they are asked. Our automatic classification algorithm achieves an 83% correct labeling result, showing the value of the ASK taxonomy for the design of social question and answering systems.