From Asking to Answering: Getting More Involved on Stack Overflow

• URL: http://arxiv.org/abs/2010.04025v1
• Date: Thu, 8 Oct 2020 14:41:22 GMT
• Title: From Asking to Answering: Getting More Involved on Stack Overflow
• Authors: Timur Bachschi, Aniko Hannak, Florian Lemmerich, Johannes Wachs
• Abstract summary: This paper investigates issues on Stack Overflow, a popular question and answer community for computer programming. We document evidence of a "leaky pipeline", specifically that there are many active users on the platform who never post an answer. We find a user's individual features, such as their tenure, gender, and geographic location, have a significant relationship with their likelihood to post answers.
• Score: 3.1160359353275346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online knowledge platforms such as Stack Overflow and Wikipedia rely on a large and diverse contributor community. Despite efforts to facilitate onboarding of new users, relatively few users become core contributors, suggesting the existence of barriers or hurdles that hinder full involvement in the community. This paper investigates such issues on Stack Overflow, a widely popular question and answer community for computer programming. We document evidence of a "leaky pipeline", specifically that there are many active users on the platform who never post an answer. Using this as a starting point, we investigate potential factors that can be linked to the transition of new contributors from asking questions to posting answers. We find a user's individual features, such as their tenure, gender, and geographic location, as well as features of the subcommunity in which they are most active, such as its size and the prevalence of negative social feedback, have a significant relationship with their likelihood to post answers. By measuring and modeling these relationships our paper presents a first look at the challenges and obstacles to user promotion along the pipeline of contributions in online communities.

Related papers

• Answering Ambiguous Questions with a Database of Questions, Answers, and Revisions [95.92276099234344]
  We present a new state-of-the-art for answering ambiguous questions that exploits a database of unambiguous questions generated from Wikipedia. Our method improves performance by 15% on recall measures and 10% on measures which evaluate disambiguating questions from predicted outputs.
  arXiv  Detail & Related papers  (2023-08-16T20:23:16Z)
• Best-Answer Prediction in Q&A Sites Using User Information [2.982218441172364]
  Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features.
  arXiv  Detail & Related papers  (2022-12-15T02:28:52Z)
• Friendliness Of Stack Overflow Towards Newbies [0.6316693022958221]
  We analyzed the effectiveness of Stack Overflow in helping newbies to programming. The platform had a steady growth up to 2013 after which it started declining. During the pandemic 2020, we can see rejuvenated activity on the platform.
  arXiv  Detail & Related papers  (2022-08-21T05:10:19Z)
• Features that Predict the Acceptability of Java and JavaScript Answers on Stack Overflow [5.332217496693262]
  We studied the Stack Overflow dataset by analyzing questions and answers for the two most popular tags (Java and JavaScript) Our findings reveal that the length of code in answers, reputation of users, similarity of the text between questions and answers, and the time lag between questions and answers have the highest predictive power for differentiating accepted and unaccepted answers.
  arXiv  Detail & Related papers  (2021-01-08T03:09:38Z)
• CASS: Towards Building a Social-Support Chatbot for Online Health Community [67.45813419121603]
  The CASS architecture is based on advanced neural network algorithms. It can handle new inputs from users and generate a variety of responses to them. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support.
  arXiv  Detail & Related papers  (2021-01-04T05:52:03Z)
• Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning [69.42679922160684]
  We propose feedback-weighted learning based on importance sampling to improve upon an initial supervised system using binary user feedback. Our work opens the prospect to exploit interactions with real users and improve conversational systems after deployment.
  arXiv  Detail & Related papers  (2020-11-01T19:50:34Z)
• Study of the usability of LinkedIn: a social media platform meant to connect employers and employees [91.3755431537592]
  This paper is assessing LinkedIn's usability using both user and expert evaluation. The overall usability of LinkedIn application has been measured by using SUS (System Usability Scale)
  arXiv  Detail & Related papers  (2020-06-06T18:19:45Z)
• Information Consumption and Social Response in a Segregated Environment: the Case of Gab [74.5095691235917]
  This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic. We find that there are no strong statistical differences in the social response to questionable and reliable content. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
  arXiv  Detail & Related papers  (2020-06-03T11:34:25Z)
• Improving Quality of a Post's Set of Answers in Stack Overflow [2.0625936401496237]
  A large number of low-quality posts on Stack Overflow require improvement. We propose an approach to automate the identification process of such posts and boost their set of answers.
  arXiv  Detail & Related papers  (2020-05-30T19:40:19Z)
• Unsupervised Question Decomposition for Question Answering [102.56966847404287]
  We propose an algorithm for One-to-N Unsupervised Sequence Sequence (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions. We show large QA improvements on HotpotQA over a strong baseline on the original, out-of-domain, and multi-hop dev sets.
  arXiv  Detail & Related papers  (2020-02-22T19:40:35Z)
• The Phantom Steering Effect in Q&A Websites [37.098578930642745]
  Badges are commonly used in online platforms as incentives for promoting contributions. This paper provides a new probabilistic model of user behavior in the presence of badges. We find that steering is not as widely applicable as was previously understood.
  arXiv  Detail & Related papers  (2020-02-14T18:20:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.