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EFFECTS OF LABEL USAGE ON QUESTION LIFECYCLE IN Q&A COMMUNITY

2022 | European Conference On Information Systems | Citations: 0
Authors: Sha, Alyssa; Haller, Armin; Shi, Yingnan

Abstract: Community question answering (CQA) sites have developed into vast collections o ... Expand

Abstract: Community question answering (CQA) sites have developed into vast collections of valuable knowledge. Questions, as CQA's central component, go through several phases after they are posted, which are often referred to as the questions' lifecycle or questions' lifespan. Different questions have different lifecycles, which are closely linked to the topics of the questions that can be determined by their attached labels. We conduct an empirical analysis based on the dynamic panel data of a Q&A website and propose a framework for explaining the time sensitivity of topic labels. By applying a Discrete Fourier Transform and a Knee point detection method, we demonstrate the existence of three broad label clusters based on their recurring features and four common question lifecycle patterns. We further prove that the lifecycles of questions in disparate clusters vary significantly. The findings support our hypothesis that questions with more time-sensitive labels are more likely to hit their saturation point sooner than questions with less time-sensitive labels. The research results could be applied for better CQA interface design and more efficient digital resources management. Collapse

Semantic filters: user experience DBSCAN

Topics: website IT resource management Python user behavior information lifecycle management

Methods: longitudinal research k-means clustering DBSCAN cluster analysis time series analysis
Visualizing Unfair Ratings in Online Reputation Systems

2015 | European Conference On Information Systems | Citations: 2
Authors: Sänger, Johannes; Richthammer, Christian; Kunz, Michael; Meier, Stefan; Pernul, Günther

Abstract: Reputation systems provide a valuable method to measure the trustworthiness of s ... Expand

Abstract: Reputation systems provide a valuable method to measure the trustworthiness of sellers or the quality of products in an e-commerce environment. Due to their economic importance, reputation systems are subject to many attacks. A common problem are unfair ratings which are used to unfairly increase or decrease the reputation of an entity. Although being of high practical relevance, unfair rating attacks have only rarely been considered in literature. The few approaches that have been proposed are furthermore quite non-transparent to the user. In this work, we employ visual analytics to identify colluding digital identities. The ultimate beneﬁt of our approach is the transparent revelation of the true reputation of an entity by interactively using both endogenous and exogenous discounting methods. We thereto introduce a generic conceptual design of a visual analytics component that is independent of the underlying reputation system. We then describe how this concept was implemented in a software prototype. Subsequently, we demonstrate its proper functioning by means of an empirical study based on two real-world datasets from eBay and Epinions. Overall, we show that our approach notably enhances transparency, bares an enormous potential and might thus lead to substantially more robust reputation systems and enhanced user experience. Collapse

Semantic filters: user experience DBSCAN

Topics: online reputation system visual analytics electronic commerce electronic market online review

Methods: design artifact exploratory data analysis cluster analysis case study computational algorithm

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EFFECTS OF LABEL USAGE ON QUESTION LIFECYCLE IN Q&A COMMUNITY

Visualizing Unfair Ratings in Online Reputation Systems