Community-based question answering (CQA) platforms are crowd-sourced services for sharing user expertise on various topics, from mechanical repairs to parenting. While they naturally build-in an online social network infrastructure, they carry a very different purpose from Facebook-like social networks, where users "hang-out" with their friends and tend to share more personal information. It is unclear, thus, how the privacy concerns and their correlation with user behavior in an online social network translate into a CQA platform. This study analyzes one year of recorded traces from a mature CQA platform to understand the association between users' privacy concerns as manifested by their account settings and their activity in the platform. The results show that privacy preference is correlated with behavior in the community in terms of engagement, retention, accomplishments and deviance from the norm. We find privacy-concerned users have higher qualitative and quantitative contributions, show higher retention, report more abuses, have higher perception on answer quality and have larger social circles. However, at the same time, these users also exhibit more deviant behavior than the users with public profiles.
User perspective research sheds light on why users contribute content: that is, why users ask questions (askers are failed searchers, in that, they use CQA sites when web search fails @cite_14 ) and why they answer questions (e.g., they refrain from answering sensitive questions to avoid being reported for abuse and potentially lose access to the community @cite_17 ). Moreover, @cite_33 explore the factors that influence users' answering behavior in (e.g., when users tend to answer and how they choose questions). @cite_40 investigate truthfulness of users and offer a quantitative proof that users post sensitive and accurate information to fulfill specific information needs.
@cite_40 @cite_14 @cite_33 @cite_17
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Internet users notoriously take an assumed identity or masquerade as someone else, for reasons such as financial profit or social benefit. But often the converse is also observed, where people choose to reveal true features of their identity, including deeply intimate details. This work attempts to explore several of the conditions that allow this to happen by analyzing the content generated by these users. We examine multiple social media on the Web, specifically focusing on Yahoo! Answers, encompassing more than a billion answers posted since 2006. Our analysis covers discussions of personal topics such as body measurements and income, and of socially sensitive subjects such as sexual behaviors. We offer quantitative proof that people are aware of the fact that they are posting sensitive information, and yet provide accurate information to fulfill specific information needs. Our analysis further reveals that on community question answering sites, when users are truthful, their expectation of an accurate answer is met. While Web search has become increasingly effective over the last decade, for many users' needs the required answers may be spread across many documents, or may not exist on the Web at all. Yet, many of these needs could be addressed by asking people via popular Community Question Answering (CQA) services, such as Baidu Knows, Quora, or Yahoo! Answers. In this paper, we perform the first large-scale analysis of how searchers become askers. For this, we study the logs of a major web search engine to trace the transformation of a large number of failed searches into questions posted on a popular CQA site. Specifically, we analyze the characteristics of the queries, and of the patterns of search behavior that precede posting a question; the relationship between the content of the attempted queries and of the posted questions; and the subsequent actions the user performs on the CQA site. Our work develops novel insights into searcher intent and behavior that lead to asking questions to the community, providing a foundation for more effective integration of automated web search and social information seeking. A key functionality in Collaborative Question Answering (CQA) systems is the assignment of the questions from information seekers to the potential answerers. An attractive solution is to automatically recommend the questions to the potential answerers with expertise or interest in the question topic. However, previous work has largely ignored a key problem in question recommendation - namely, whether the potential answerer is likely to accept and answer the recommended questions in a timely manner. This paper explores the contextual factors that influence the answerer behavior in a large, popular CQA system, with the goal to inform the construction of question routing and recommendation systems. Specifically, we consider when users tend to answer questions in a large-scale CQA system, and how answerers tend to choose the questions to answer. Our results over a dataset of more than 1 million questions draw from a real CQA system could help develop more realistic evaluation methods for question recommendation, and inform the design of future question recommender systems. Posing a question to an online question and answer community does not guarantee a response. Significant prior work has explored and identified members' motivations for contributing to communities of collective action (e.g., Yahoo! Answers); in contrast it is not well understood why members choose to not answer a question they have already read. To explore this issue, we surveyed 135 active members of Yahoo! Answers. We show that top and regular contributors experience the same reasons to not answer a question: subject nature and composition of the question; perception of how the questioner will receive, interpret and react to their response; and a belief that their response will lose its meaning and get lost in the crowd if too many responses have already been given. Informed by our results, we discuss opportunities to improve the efficacy of the question and answer process, and to encourage greater contributions through improved design.