A Lot of Talk and a Badge: An Exploratory Analysis of Personal
Achievements in GitHub
- URL: http://arxiv.org/abs/2303.14702v3
- Date: Fri, 2 Feb 2024 10:43:17 GMT
- Title: A Lot of Talk and a Badge: An Exploratory Analysis of Personal
Achievements in GitHub
- Authors: Fabio Calefato and Luigi Quaranta and Filippo Lanubile
- Abstract summary: GitHub introduced a new element through personal achievements, whereby badges are unlocked and displayed on developers' personal profile pages in recognition of their development activities.
We present an exploratory analysis using mixed methods to study the diffusion of personal badges in GitHub.
We find that most of the developers sampled own at least a badge, but we also observe an increasing number of users who choose to keep their profile private and opt out of displaying badges.
- Score: 15.236182782295732
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Context. GitHub has introduced a new gamification element through personal
achievements, whereby badges are unlocked and displayed on developers' personal
profile pages in recognition of their development activities. Objective. In
this paper, we present an exploratory analysis using mixed methods to study the
diffusion of personal badges in GitHub, in addition to the effects and
reactions to their introduction. Method. First, we conduct an observational
study by mining longitudinal data from more than 6,000 developers and performed
correlation and regression analysis. Then, we conduct a survey and analyze over
300 GitHub community discussions on the topic of personal badges to gauge how
the community responded to the introduction of the new feature. Results. We
find that most of the developers sampled own at least a badge, but we also
observe an increasing number of users who choose to keep their profile private
and opt out of displaying badges. Besides, badges are generally poorly
correlated with developers' qualities and dispositions such as timeliness and
desire to collaborate. We also find that, except for the Starstruck badge
(reflecting the number of followers), their introduction does not have an
effect. Finally, the reaction of the community has been in general mixed, as
developers find them appealing in principle but without a clear purpose and
hardly reflecting their abilities in the current form. Conclusions. We provide
recommendations to GitHub platform designers on how to improve the current
implementation of personal badges as both a gamification mechanism and as
sources of reliable cues of ability for developers' assessment
Related papers
- LogoSticker: Inserting Logos into Diffusion Models for Customized Generation [73.59571559978278]
We introduce the task of logo insertion into text-to-image models.
Our goal is to insert logo identities into diffusion models and enable their seamless synthesis in varied contexts.
We present a novel two-phase pipeline LogoSticker to tackle this task.
arXiv Detail & Related papers (2024-07-18T17:54:49Z) - How do Software Engineering Researchers Use GitHub? An Empirical Study of Artifacts & Impact [0.2209921757303168]
We ask whether and how authors engage in social coding related to their research.
Ten thousand papers in top SE research venues, hand-annotating their GitHub links, and studying 309 paper-related repositories.
We find a wide distribution in popularity and impact, some strongly correlated with publication venue.
arXiv Detail & Related papers (2023-10-02T18:56:33Z) - Topic-Guided Self-Introduction Generation for Social Media Users [34.41343865143143]
We explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests.
Here we exploit a user's tweeting history to generate their self-introduction.
We propose a novel unified topic-guided encoder-decoder framework.
arXiv Detail & Related papers (2023-05-24T13:35:08Z) - Rethinking People Analytics With Inverse Transparency by Design [57.67333075002697]
We propose a new design approach for workforce analytics we refer to as inverse transparency by design.
We find that architectural changes are made without inhibiting core functionality.
We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.
arXiv Detail & Related papers (2023-05-16T21:37:35Z) - These Deals Won't Last! Longevity, Uniformity and Bias in Product Badge
Assignment in E-Commerce Platforms [5.582405594617256]
We try to answer questions such as: How long does a product retain a badge on a given platform?
We collect longitudinal data from several e-commerce platforms over 45 days, and find that although most of the badges are short-lived, there are several permanent badge assignments.
It is unclear how the badge assignments are done, and we find evidence that highly-rated products are missing out on badges compared to lower quality ones.
arXiv Detail & Related papers (2022-04-26T19:16:34Z) - Rumor Detection with Self-supervised Learning on Texts and Social Graph [101.94546286960642]
We propose contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better.
We term this framework as Self-supervised Rumor Detection (SRD)
Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
arXiv Detail & Related papers (2022-04-19T12:10:03Z) - Exploring Visual Context for Weakly Supervised Person Search [155.46727990750227]
Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person re-identification.
Existing approaches follow a fully supervised setting where both bounding box and identity annotations are available.
This paper inventively considers weakly supervised person search with only bounding box annotations.
arXiv Detail & Related papers (2021-06-19T14:47:13Z) - ConsNet: Learning Consistency Graph for Zero-Shot Human-Object
Interaction Detection [101.56529337489417]
We consider the problem of Human-Object Interaction (HOI) Detection, which aims to locate and recognize HOI instances in the form of human, action, object> in images.
We argue that multi-level consistencies among objects, actions and interactions are strong cues for generating semantic representations of rare or previously unseen HOIs.
Our model takes visual features of candidate human-object pairs and word embeddings of HOI labels as inputs, maps them into visual-semantic joint embedding space and obtains detection results by measuring their similarities.
arXiv Detail & Related papers (2020-08-14T09:11:18Z) - 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) - How Gamification Affects Software Developers: Cautionary Evidence from a
Natural Experiment on GitHub [6.123324869194196]
We find that the unannounced removal of daily activity streak counters from the user interface was followed by significant changes in behavior.
Long-running streaks of activity were abandoned and became less common.
We find that some developers abandon a goal to make contributions for 100 days in a row following the removal of the public streak counter.
arXiv Detail & Related papers (2020-06-03T16:35:47Z) - 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.