Social Search Behavior in a Social Q&A Service: Goals, Strategies, and Outcomes

information seekers to reach out to a larger, more distributed group of people online when searching for information. In this study, people’s question-asking behavior using a social Q&A service is conceptualized as social search behavior. We are particularly interested in investigating social search goals, strategies, tactics, informational outcomes, and social outcomes. We collected a total of 406 questions posted on Yahoo! Answers by 78 participants over one week. Interviews based on those questions and answers they received were conducted and content-analyzed. We identify five distinct search strategies and 15 tactics positioned on a continuum of two different dimensions in terms of answer quantity and answer quality. Pursuit of quantity or quality is influenced by five categories of goals identified in this study. The goals and associated strategies and tactics also influence people’s perceived informational outcomes and social outcomes. Contributions of this study to the social search research community and implications for practitioners in the area of social Q&A services are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115871/1/Jeon Rieh ASIST 2015 published.pd

A machine learning-based approach to predicting success of questions on social question-answering

While social question-answering (SQA) services are becoming increasingly popular, there is often an issue of unsatisfactory or missing information for a question posed by an information seeker. This study creates a model to predict question failure, or a question that does not receive an answer, within the social Q&A site Yahoo! Answers. To do so, observed shared characteristics of failed questions were translated into empirical features, both textual and non-textual in nature, and measured using machine extraction methods. A classifier was then trained using these features and tested on a data set of 400 questions – half of them successful, half not – to determine the accuracy of the classifier in identifying failed questions. The results show the substantial ability of the approach to correctly identify the likelihood of success or failure of a question, resulting in a promising tool to automatically identify ill-formed questions and/or questions that are likely to fail and make suggestions on how to revise them.published or submitted for publicationis peer reviewe

The language of information need : differentiating conscious and formalized information needs

Information need is a fundamental concept within Information Science. Robert Taylor's seminal contribution in 1968 was to propose a division of information needs into four levels: the visceral, conscious, formalized and compromised levels of information need. Taylor's contribution has provided much inspiration to Information Science research but this has largely remained at the discursive and conceptual level. In this paper, we present a novel empirical investigation of Taylor's information need classification. We analyse the linguistic differences between conscious and formalized needs using several hundred postings to four major Internet discussion groups. We show that descriptions of conscious needs are more emotional in tone, involve more sensory perception and contain different temporal dimensions than descriptions of formalized needs. We show that it is possible to differentiate levels of information need based on linguistic patterns and that the language used to express information needs can reflect an individual's understanding of their information problem. This has implications for the theory of information needs and practical implications for supporting moderators of online news groups in responding to information needs and for developing automated support for classifying information needs