How to Ask for Technical Help? Evidence-based Guidelines for Writing Questions on Stack Overflow

Context: The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others' questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and feedback. Understanding the dynamics of the participation in Q&A communities is essential to improve the value of crowdsourced knowledge. Objective: In this paper, we investigate how information seekers can increase the chance of eliciting a successful answer to their questions on Stack Overflow by focusing on the following actionable factors: affect, presentation quality, and time. Method: We develop a conceptual framework of factors potentially influencing the success of questions in Stack Overflow. We quantitatively analyze a set of over 87K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests. The information seeker reputation is included as a control factor. Furthermore, to understand the role played by affective states in the success of questions, we qualitatively analyze questions containing positive and negative emotions. Finally, a survey is conducted to understand how Stack Overflow users perceive the guideline suggestions for writing questions. Results: We found that regardless of user reputation, successful questions are short, contain code snippets, and do not abuse with uppercase characters. As regards affect, successful questions adopt a neutral emotional style. Conclusion: We provide evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help. As for the role of affect, we empirically confirmed community guidelines that suggest avoiding rudeness in question writing.Comment: Preprint, to appear in Information and Software Technolog

Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora

Quora is one of the most popular community Q&A sites of recent times. However, many question posts on this Q&A site often do not get answered. In this paper, we quantify various linguistic activities that discriminates an answered question from an unanswered one. Our central finding is that the way users use language while writing the question text can be a very effective means to characterize answerability. This characterization helps us to predict early if a question remaining unanswered for a specific time period t will eventually be answered or not and achieve an accuracy of 76.26% (t = 1 month) and 68.33% (t = 3 months). Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74.18%. We also compare our method with some of the similar works (Dror et al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.Comment: 1 figure, 3 tables, ICWSM 2017 as poste

The Social World of Content Abusers in Community Question Answering

Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.Comment: Published in the proceedings of the 24th International World Wide Web Conference (WWW 2015

On statistical approaches to generate Level 3 products from satellite remote sensing retrievals

Satellite remote sensing of trace gases such as carbon dioxide (CO$_2$) has increased our ability to observe and understand Earth's climate. However, these remote sensing data, specifically~Level 2 retrievals, tend to be irregular in space and time, and hence, spatio-temporal prediction is required to infer values at any location and time point. Such inferences are not only required to answer important questions about our climate, but they are also needed for validating the satellite instrument, since Level 2 retrievals are generally not co-located with ground-based remote sensing instruments. Here, we discuss statistical approaches to construct Level 3 products from Level 2 retrievals, placing particular emphasis on the strengths and potential pitfalls when using statistical prediction in this context. Following this discussion, we use a spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The FRK predictions allow us to validate statistically the Level 2 retrievals globally even though the data are at locations and at time points that do not coincide with validation data. Importantly, the validation takes into account the prediction uncertainty, which is dependent both on the temporally-varying density of observations around the ground-based measurement sites and on the spatio-temporal high-frequency components of the trace gas field that are not explicitly modelled. Here, for validation of remotely-sensed CO$_2$ data, we use observations from the Total Carbon Column Observing Network. We demonstrate that the resulting FRK product based on Version 8r compares better with TCCON data than that based on Version 7r.Comment: 28 pages, 10 figures, 4 table

Evidence-based commissioning in the English NHS : who uses which sources of evidence? A survey 2010/2011

Objectives: To investigate types of evidence used by healthcare commissioners when making decisions and whether decisions were influenced by commissioners’ experience, personal characteristics or role at work. Design: Cross-sectional survey of 345 National Health Service (NHS) staff members. Setting: The study was conducted across 11 English Primary Care Trusts between 2010 and 2011. Participants: A total of 440 staff involved in commissioning decisions and employed at NHS band 7 or above were invited to participate in the study. Of those, 345 (78%) completed all or a part of the survey. Main outcome measures: Participants were asked to rate how important different sources of evidence (empirical or practical) were in a recent decision that had been made. Backwards stepwise logistic regression analyses were undertaken to assess the contributions of age, gender and professional background, as well as the years of experience in NHS commissioning, pay grade and work role. Results: The extent to which empirical evidence was used for commissioning decisions in the NHS varied according to the professional background. Only 50% of respondents stated that clinical guidelines and cost-effectiveness evidence were important for healthcare decisions. Respondents were more likely to report use of empirical evidence if they worked in Public Health in comparison to other departments (p<0.0005, commissioning and contracts OR 0.32, 95%CI 0.18 to 0.57, finance OR 0.19, 95%CI 0.05 to 0.78, other departments OR 0.35, 95%CI 0.17 to 0.71) or if they were female (OR 1.8 95% CI 1.01 to 3.1) rather than male. Respondents were more likely to report use of practical evidence if they were more senior within the organisation (pay grade 8b or higher OR 2.7, 95%CI 1.4 to 5.3, p=0.004 in comparison to lower pay grades). Conclusions: Those trained in Public Health appeared more likely to use external empirical evidence while those at higher pay scales were more likely to use practical evidence when making commissioning decisions. Clearly, National Institute for Clinical Excellence (NICE) guidance and government publications (eg, National Service Frameworks) are important for decision-making, but practical sources of evidence such as local intelligence, benchmarking data and expert advice are also influential

Electronic word of mouth in social media: The common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Marketers desire to utilise electronic word of mouth (eWOM) marketing on social media sites. However, not all online content generated by marketers has the same effect on consumers; some of them are effective while others are not. This paper aims to examine different characteristics of marketer-generated content (MGC) that of which one lead users to eWOM. Twitter was chosen as one of the leading social media sites and a content analysis approach was employed to identify the common characteristics of retweeted and favourited tweets. 2,780 tweets from six companies (Booking, Hostelworld, Hotels, Lastminute, Laterooms and Priceline) operating in the tourism sector are analysed. Results indicate that the posts which contain pictures, hyperlinks, product or service information, direct answers to customers and brand centrality are more likely to be retweeted and favourited by users. The findings present the main eWOM drivers for MGC in social media.Abdulaziz Elwalda and Mohammed Alsagga