Stack Exchange Research Articles

Which Size Matters? Effects of Crowd Size on Solution Quality in Big Data Q&A Communities

Question Answering websites have evolved into one of the most important platforms for knowledge sharing and problem solving online. Despite widespread adoption of Q&As by technical communities as well as an abundance of domain experts, many questions fail to attract a sufficient audience to obtain a good solution or any solution at all. We investigate the effects of crowd size on solution quality in Stack Exchange Q&A communities on topics related to big data. We find that three distinct levels of group size in the crowd (topic audience size, question audience size, and number of contributors) affect solution quality. Therefore, we argue that group size in the crowd is not unitary, but rather a multi-level construct. This work advances a theoretical model of group size in the crowd and the relation between crowd size and performance. The work also has practical implications for system designers trying to route crowds to problems efficiently.

Dynamics of Content Quality in Collaborative Knowledge Production

We explore the dynamics of user performance in collaborative knowledge production by studying the quality of answers to questions posted on Stack Exchange. We propose four indicators of answer quality: answer length, the number of code lines and hyperlinks to external web content it contains, and whether it is accepted by the asker as the most helpful answer to the question. Analyzing millions of answers posted over the period from 2008 to 2014, we uncover regular short-term and long-term changes in quality. In the short-term, quality deteriorates over the course of a single session, with each successive answer becoming shorter, with fewer code lines and links, and less likely to be accepted. In contrast, performance improves over the long-term, with more experienced users producing higher quality answers. These trends are not a consequence of data heterogeneity, but rather have a behavioral origin. Our findings highlight the complex interplay between short-term deterioration in performance, potentially due to mental fatigue or attention depletion, and long-term performance improvement due to learning and skill acquisition, and its impact on the quality of user-generated content.

Inverse Dynamical Inheritance in Stack Exchange Taxonomies

Question Answering websites are popular repositories of expert knowledge and cover areas as diverse as linguistics, computer science, or mathematics. Knowledge is commonly organized via user defined tags which implicitly create population folksonomies. However, the interplay between latent knowledge structures and the answering behavior of users has not been fully explored yet. Here, we propose a model of a dynamical tagging process guided by taxonomies, devise a robust algorithm that allow us to uncover hidden topic hierarchies, apply our method to analyze several Stack Exchange websites. Our results show that the dynamics of the system strongly correlate with uncovered taxonomies.

The Myopia of Crowds: A Study of Collective Evaluation on Stack Exchange

Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the “wisdom of crowds” effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer’s salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend more on heuristics, compared to voting users, when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic: they are more likely to choose the answer after it is accepted than before that very same answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grow.

The myopia of crowds: Cognitive load and collective evaluation of answers on Stack Exchange

Crowds can often make better decisions than individuals or small groups of experts by leveraging their ability to aggregate diverse information. Question answering sites, such as Stack Exchange, rely on the “wisdom of crowds” effect to identify the best answers to questions asked by users. We analyze data from 250 communities on the Stack Exchange network to pinpoint factors affecting which answers are chosen as the best answers. Our results suggest that, rather than evaluate all available answers to a question, users rely on simple cognitive heuristics to choose an answer to vote for or accept. These cognitive heuristics are linked to an answer’s salience, such as the order in which it is listed and how much screen space it occupies. While askers appear to depend on heuristics to a greater extent than voters when choosing an answer to accept as the most helpful one, voters use acceptance itself as a heuristic, and they are more likely to choose the answer after it has been accepted than before that answer was accepted. These heuristics become more important in explaining and predicting behavior as the number of available answers to a question increases. Our findings suggest that crowd judgments may become less reliable as the number of answers grows.

A Guide to Text Analysis with Latent Semantic Analysis in R with Annotated Code Studying Online Reviews and the Stack Exchange Community

A Guide to Text Analysis with Latent Semantic Analysis in R with Annotated Code Studying Online Reviews and the Stack Exchange Community

Web-service for integration of current information from the leading MOOCs

Purpose. A phenomenon called MOOC (Massive Open Online Course) plays a special, “revolutionary” role, introducing of online learning for all stages of the educational process. Today there are a lot of MOOCs, the largest of them: Coursera, edX, Udacity. In connection with the large amount of resources for additional education, a number of questions arise: “How to choose the suitable course as an additional education, how to use the capabilities of MOOCs to improve the quality of the educational process at the university, how to minimize the time for finding the desired course on different platforms?” The purpose of the work is to create a unified information resource that allows you to integrate relevant information from various MOOCs, to reduce the time for finding an appropriate online course for the user, and to raise awareness about new courses and popular specializations, related to information technology. Materials and methods. Web-service is designed as one of the components of the information-educational environment of the Institute of System Analysis and Management. It is aimed at different groups of users: “motivated” students, “unmotivated” students, and lecturers. The initial data for the service is open information about the current online courses on the MOOCs Coursera, edX and Udacity. Each project has its own API, which allows you to get information about online courses. Since MOOCs do not have a common vocabulary in order to use it for searching, there is the problem of creating such a dictionary. To do this we use the Stack Exchange resource (stackexchange.com). The list of keywords is formed through the Stack Exchange API – we select the most popular tags on sites softwareengineering.stackexchange.com and cs.stackexchange.com. Results. As the first stage of implementation, a web application was developed that allows you to view and filter the lists of courses and to see detailed information about the courses. Conclusion. Today there are alpha-versions of two web-services within the information-educational environment of the Dubna State University. The first one is the web-service for collecting and analyzing data on the vacancies of specialists in the field of information technologies from the most popular Internet resources and the webservice that allows integrating information about online courses from the leading MOOC-sites.

Toward a mixed-initiative QA system: from studying predictors in Stack Exchange to building a mixed-initiative tool

This article envisions a new customer support solution that merges the efficiency of crowd-based Question and Answer (QA) sites with the effectiveness of traditional customer care services. QA sites use crowdsourcing to solve problems in a very efficient way and they represent a new approach that can compete with traditional customer support services. Despite the remarkable efficiency of popular QA sites, if a question is not solved almost immediately, the chances are that it will not be solved soon or perhaps ever. This article provides evidence of a consistent Dark Side, a group of questions that remain unsatisfied or are satisfied very late, in eight popular QA sites on Stack Exchange. About 25–30% of all the questions in these sites fall into this Dark Side group. The findings show that predicting if a question will end up in the Dark Side is feasible, although with some approximation, without relying on content features. On the basis of this evidence, the article first presents and tests a model to predict the Dark Side and then presents a proof-of-concept of a mixed-initiative tool that helps a crowd-manager to decide whether an incoming question will be solved by the crowd or it should be redirected to a dedicated operator. Multiple evaluations of the proposed tool are reported. Finally, it concludes with lessons for the design and management of future QA platforms.

Altmetric: Top 50 dental articles in 2014

Introduction Altmetrics is a new and emerging scholarly tool that measures online attention surrounding journal articles. Altmetric data resources include: policy documents, news outlets, blogs, online reference managers (eg Mendeley and CiteULike), post-publication peer-review forums (eg PubPeer and Publons), social media (eg Twitter, Facebook, Weibo, Google(+), Pinterest, Reddit), Wikipedia, sites running Stack Exchange (Q&A), and reviews on F1000 and YouTube.Methods To identify the top 50 dental articles in 2014, PubMed was searched using the following query "("2014/1/1"[PDAT]:"2014/12/31"[PDAT]) and jsubsetd[text]" in December, 2015. Consequently, all PubMed records were extracted and sent to Altmetric LLP (London, UK) as a CSV file for examination. Data were analysed by Microsoft Office Excel 2010 using descriptive statistics and charts.Results Using PubMed searches,15,132 dental articles were found in 2014. The mean Altmetric score of 50 top dental articles in 2014 was 69.5 ± 73.3 (95% CI: -74.14 to 213.14). The British Dental Journal (48%) and Journal of Dental Research (16%) had the maximum number of top articles. Twitter (67.13%), Mendeley (15.89%) and news outlets (10.92%) were the most popular altmetric data resources.Discussion Altmetrics are intended to supplement bibliometrics, not replace them. Altmetrics is a fresh and emerging arena for the dental research community. We believe that dental clinical practitioners, research scientists, research directors and journal editors must pay more attention to altmetrics as a new and rapid tool to measure the social impact of scholarly articles.

Analyze the Positive Comments in the Stack Exchange

Analyze the Positive Comments in the Stack Exchange

The Role of Diverse Strategies in Sustainable Knowledge Production.

Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.

Large exchange bias in polycrystalline MnN/CoFe bilayers at room temperature

We report on the new polycrystalline exchange bias system MnN/CoFe, which shows exchange bias of up to 1800Oe at room temperature with a coercive field around 600Oe. The room temperature values of the interfacial exchange energy and the effective uniaxial anisotropy are estimated to be $J_\mathrm{eff} = 0.41\,\mathrm{mJ}/\mathrm{m}^2$ and $K_\mathrm{eff} = 37\,\mathrm{kJ}\,/\,\mathrm{m}^3$. The thermal stability was found to be tunable by controlling the nitrogen content of the MnN. The maximum blocking temperature exceeds $325^\circ$C, however the median blocking temperature in the limit of thick MnN is $160^\circ$C. Good oxidation stability through self-passivation was observed, enabling the use of MnN in lithographically defined microstructures. As a proof-of-principle we demonstrate a simple GMR stack exchange biased with MnN, which shows clear separation between parallel and antiparallel magnetic states. These properties come along with a surprisingly simple manufacturing process for the MnN films.

Complex Functional Systems with Three Different Types of Dynamic Covalent Bonds.

Multicomponent surface architectures are introduced that operate with three different dynamic covalent bonds. Disulfide exchange under basic conditions accounts for the growth of π stacks on solid surfaces. Hydrazone exchange under acidic conditions is used to add a second coaxial string or stack, and boronic ester exchange under neutral conditions is used to co-align a third one. The newly introduced boronic ester exchange chemistry is compatible with stack and string exchange without interference from the orthogonal hydrazone and disulfide exchange. The functional relevance of surface architectures with three different dynamic covalent bonds is exemplified with the detection of polyphenol natural products, such as epigallocatechin gallate, in competition experiments with alizarin red. These results describe synthetic strategies to create functional systems of unprecedented sophistication with regard to dynamic covalent chemistry.

A Cluster based Approach with N-grams at Word Level for Document Classification

A breakneck progress of computers and web makes it easier to collect and store large amount of information in the form of text; e.g., reviews, forum postings, blogs, web pages, news articles, email messages. In text mining, growing size of text datasets and high dimensionality associated with natural language is great challenge which makes it difficult to classify documents in various categories and sub-categories. This paper focuses on cluster based document classification technique so that data inside each cluster shares some common trait. The common approach for document clustering problem is bag of words model (BOW), where words are considered as features. But some semantic information is always lost as only words are considered. Thus we aim at using vector-space model based on N-grams at word level which helps to reduce the loss of semantic information. The problem of high dimensionality is solved with feature selection technique by applying threshold on feature values of vector space model. The vector space is mapped into a modified one with latent semantic analysis (LSA). Clustering of documents is done using k-means algorithm. Experiments are performed on Stack Exchange data set of some categories. R is used as text mining tool for implementation purpose. Experiment results show that tri-grams give better clustering results than words and bi-grams.

Identifying Authorities in Online Communities

Several approaches have been proposed for the problem of identifying authoritative actors in online communities. However, the majority of existing methods suffer from one or more of the following limitations: (1) There is a lack of an automatic mechanism to formally discriminate between authoritative and nonauthoritative users. In fact, a common approach to authoritative user identification is to provide a ranked list of users expecting authorities to come first. A major problem of such an approach is the question of where to stop reading the ranked list of users. How many users should be chosen as authoritative? (2) Supervised learning approaches for authoritative user identification suffer from their dependency on the training data. The problem here is that labeled samples are more difficult, expensive, and time consuming to obtain than unlabeled ones. (3) Several approaches rely on some user parameters to estimate an authority score. Detection accuracy of authoritative users can be seriously affected if incorrect values are used. In this article, we propose a parameterless mixture model-based approach that is capable of addressing the three aforementioned issues in a single framework. In our approach, we first represent each user with a feature vector composed of information related to its social behavior and activity in an online community. Next, we propose a statistical framework, based on the multivariate beta mixtures, in order to model the estimated set of feature vectors. The probability density function is therefore estimated and the beta component that corresponds to the most authoritative users is identified. The suitability of the proposed approach is illustrated on real data extracted from the Stack Exchange question-answering network and Twitter.

Analysis the Effectiveness of Three Online Vocabulary Flashcard Websites on L2 Learners’ Level of Lexical Knowledge

This study compared and contrasted 64 Taiwanese college freshmen’s perceptions of and attitudes toward three online vocabulary flashcard websites, Quizlet, Study Stack, and Flashcard Exchange. Four types of data were collected in two freshmen English classes in a university in Taiwan from February to April 2013. Data included online flashcard websites, classroom observations, participants’ online flashcards and learning records, and interview. The study has the following two findings. First, these three online vocabulary flashcard websites mainly offered freshmen with the word knowledge in terms of form and words’ meaning. Second, participants held positive attitudes toward learning and improving their vocabulary abilities via online flashcards and their related activities. Suggestions are provided to make online flashcards more effective vocabulary-learning tools: provision of user-friendly guidelines on using online flashcards, provision of flashcards with word meanings and related information, and a variety of easy exercises designed to develop learners’ word knowledge.

Mining History of Gamification Towards Finding Expertise in Question and Answering Communities: Experience and Practice with Stack Exchange

Recently, an online Q&A tool has become an essential part of individual communities and organizations of experts on specific topics. Using the answers to questions about specific topics will help such communities work more efficiently in their fields. Currently, Q&A online communities are adopting gamification to engage users by granting awards to successful users. In this paper, we investigate how to mine award achievement histories to find expertise. We propose the use of sequence analysis and clustering techniques. Specifically, we study the history of Stack Exchange, a large Q&A community that employs gamification. To the best of our knowledge, this is the first study of using award achievement history to find expertise in Q&A communities.

A special rational function with vanishing integral

The integral of a rational function proposed as a question in Mathematics Stack Exchange is evaluated. The integrand has a polynomial of degree 4 as denominator. A natural extension to degree 8 is shown to vanish.

ChemInform Abstract: Self‐Organizing Surface‐Initiated Polymerization, Templated Self‐Sorting and Templated Stack Exchange: Synthetic Methods to Build Complex Systems

AbstractReview: 26 refs.

Self‐Organizing Surface‐Initiated Polymerization of Multicomponent Photosystems: Stack Exchange with Fullerenes

Like beads on a string: A synthetic method for the directional construction of strings of spherical fullerenes along stacks of planar oligothiophenes is described. The key to success was the preparation of fullerenes with two solubilizing tri(ethylene glycol) tails (bold) and an aromatic aldehyde for covalent capture by hydrazides along the oligothiophene stacks (red).