Designing incentives for online question-and-answer forums

Jain, Shaili; Parkes, David C.

doi:10.1016/j.geb.2012.11.003

Cited by 41 publications

(54 citation statements)

References 22 publications

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“…In a game-theoretical analysis of social Q&A, Jain et al (2009) usefully distinguish between new answers that substitute for existing ones (providing an overall better answer alternative which renders existing answers redundant) and those that complement them (adding to and enriching the information already provided). Through a simulation exercise, they show that the existing voting system for Yahoo Answers is satisfactory in the case of substitution answers but less so for complementary answers.…”

Section: Quality In Social Q and Amentioning

confidence: 99%

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Knowledge fixation and accretion: longitudinal analysis of a social question-answering site

Matthews

2014

Journal of Documentation

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Knowledge fixation and accretion: Longitudinal analysis of a social question-answering site AbstractPurpose -The aim of this work was to investigate longitudinal features of an established social questionanswering site to study how question-answer resources and other community features change over time.Design/methodology/approach -Statistical analysis and visualisation was performed on the full data dump from the StackOverflow social question-answering site for programmers.Findings -The timing of answers is as strong a predictor of acceptance -a proxy for user satisfaction -as the structural features of provided answers sometimes associated with quality. While many questions and answer exchanges are short-lived, there is a small yet interesting subset of questions where new answers receive community approval and which may end up being ranked more highly than early answers.Research limitations/implications -As a large-scale data oriented research study, this work says little about user motivations to find and contribute new knowledge to old questions or about the impact of the resource on the consumer. This will require complementary studies using qualitative and evaluative methods.Practical implications -While content contribution to social question-asking is largely undertaken within a very short time frame, content consumption is usually over far longer periods. Methods and incentives by which content can be updated and maintained need to be considered. This work should be of interest to knowledge exchange community designers and managers.Originality/value -Few studies have looked at temporal patterns in social question-answering and how time and the moderation and voting systems employed may shape resource quality

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Section: Quality In Social Q and Amentioning

confidence: 99%

“…(Paul, Hong et al, 2012). In addition to the possibility of distributing allocated upvotes amongst complementary answers (Jain, Chen et al, 2009) it has been suggested that voting on randomly presented samples of content might also alleviate this problem (Stray , 2009), though such an approach has yet to be field -tested.…”

Section: Redressing Voting Biasmentioning

confidence: 99%

Knowledge fixation and accretion: longitudinal analysis of a social question-answering site

Matthews

2014

Journal of Documentation

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“…In [29], Horton and Chilton investigated the relationship of wages and task difficulty, and proposed a model to estimate participants' reservation wage in crowdsourcing tasks. A number of reward and punishment mechanisms [40,46,57] have also been proposed to encourage users to provide quality contributions in the context of peer production systems. Lee et al [46], for example, introduced a voting-based reward mechanism for online Q&A forums, where not only the contributor of the winning answer but also the users who voted for the answer were rewarded.…”

Section: E Reward and Punishment Mechanismsmentioning

confidence: 99%

Crowdsourcing Multimedia QoE Evaluation: A Trusted Framework

Chen

Chang

et al. 2013

IEEE Trans. Multimedia

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Crowdsourcing has emerged in recent years as a potential strategy to enlist the general public to solve a wide variety of tasks. With the advent of ubiquitous Internet access, it is now feasible to ask an Internet crowd to conduct QoE (Quality of Experience) experiments on their personal computers in their own residences rather than in a laboratory. The considerable size of the Internet crowd allows researchers to crowdsource their experiments to a more diverse set of participant pool at a relatively low economic cost. However, as participants carry out experiments without supervision, the uncertainty of the quality of their experiment results is a challenging problem.In this paper, we propose a crowdsourceable framework to quantify the QoE of multimedia content. To overcome the aforementioned quality problem, we employ a paired comparison method in our framework. The advantages of our framework are: 1) trustworthiness due to the support for cheat detection; 2) a simpler rating procedure than that of the commonly-used but more difficult mean opinion score (MOS), which places less burden on participants; 3) economic feasibility since reliable QoE measures can be acquired with less effort compared with MOS; and 4) generalizability across a variety of multimedia content. We demonstrate the effectiveness and efficiency of the proposed framework by a comparison with MOS. Moreover, the results of four case studies support our assertion that the framework can provide reliable QoE evaluation at a lower cost.

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“…Also, creating a competitive environment where more contribution makes the user the top contributor of the category, this ensures higher rate of returning and contribution from the participants [13]. An approval-voting scoring rule and a proportional-share scoring rule can enable the most efficient equilibrium with complements information, under certain conditions, by providing incentives for early responders as well as the user who submits the final answer [6].…”

Section: Incentive Modelmentioning

confidence: 99%

Linked data in crowdsourcing purposive social network

Singh

Shadbolt

2013

Proceedings of the 22nd International Conference on World Wide Web

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Internet is an easy medium for people to collaborate and crowdsourcing is an efficient feature of social web where people with common interest and expertise come together to solve specific problems by collective thinking and create a community. It can also be used to filter out important information from large data, remove spams, and gamification techniques are used to reward the users for their contribution and keep a sustainable environment for the growth of the community. Semantic web technologies can be used to structure the community data so it can be combined, decentralized and be used across platform. Using such tools knowledge can be enhanced and easily discovered and merged together. This paper discusses the concept of a purposive social network where people with similar interest and varied expertise come together, use crowdsourcing technique to solve a common problem and build tools for common purpose. The StackOverflow website is chosen to study the purposive network, different network ties and roles of user is studied. Linked Data is used for name disambiguation of keywords and topics for easier search and discovery of experts in a field and provide useful information that is otherwise unavailable in the website.

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Designing incentives for online question-and-answer forums

Cited by 41 publications

References 22 publications

Knowledge fixation and accretion: longitudinal analysis of a social question-answering site

Knowledge fixation and accretion: longitudinal analysis of a social question-answering site

Crowdsourcing Multimedia QoE Evaluation: A Trusted Framework

Linked data in crowdsourcing purposive social network

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