# “My GitHub Sponsors profile is live!” Investigating the Impact of Twitter/X
Mentions on GitHub Sponsors

Youmei Fan  Nara Institute of Science and Technology, Japan
<EMAIL_ADDRESS>,  Tao Xiao  Nara Institute of Science and
Technology, Japan<EMAIL_ADDRESS>,  Hideaki Hata  Shinshu
UniversityJapan<EMAIL_ADDRESS>,  Christoph Treude  University of
MelbourneAustralia<EMAIL_ADDRESS>and  Kenichi Matsumoto
Nara Institute of Science and Technology, Japan<EMAIL_ADDRESS>
(2024)

###### Abstract.

GitHub Sponsors was launched in 2019, enabling donations to open-source
software developers to provide financial support, as per GitHub’s slogan:
“Invest in the projects you depend on”. However, a 2022 study on GitHub
Sponsors found that only two-fifths of developers who were seeking sponsorship
received a donation. The study found that, other than internal actions (such
as offering perks to sponsors), developers had advertised their GitHub
Sponsors profiles on social media, such as Twitter (also known as X).
Therefore, in this work, we investigate the impact of tweets that contain
links to GitHub Sponsors profiles on sponsorship, as well as their reception
on Twitter/X. We further characterize these tweets to understand their context
and find that (1) such tweets have the impact of increasing the number of
sponsors acquired, (2) compared to other donation platforms such as Open
Collective and Patreon, GitHub Sponsors has significantly fewer interactions
but is more visible on Twitter/X, and (3) developers tend to contribute more
to open-source software during the week of posting such tweets. Our findings
are the first step toward investigating the impact of social media on
obtaining funding to sustain open-source software.

Open-source Software, Sponsorship, Social Media

††journalyear: 2024††copyright: acmlicensed††conference: 2024 IEEE/ACM 46th
International Conference on Software Engineering; April 14–20, 2024; Lisbon,
Portugal††booktitle: 2024 IEEE/ACM 46th International Conference on Software
Engineering (ICSE ’24), April 14–20, 2024, Lisbon, Portugal††doi:
10.1145/3597503.3639127††isbn: 979-8-4007-0217-4/24/04††ccs: Social and
professional topics Sustainability††ccs: Software and its engineering Open
source model

##  1\. Introduction

Open-source software (OSS) is ubiquitous, but sustaining it is a challenge 29.
Maintaining an OSS project requires not only intrinsic motivation (e.g., joy
of participation) but also extrinsic motivation (e.g., financial incentives)
50. The last few years have seen the emergence of many platforms that allow
open-source developers to receive donations for their work, such as PayPal 31,
Open Collective 12, Patreon 30, and GitHub Sponsors 38. Several platforms
support sponsoring OSS projects in cryptocurrencies, with the rise in
popularity of cryptocurrencies today, e.g., Gitcoin Grants 17 and Giveth 21.
However, as Overney et al.’s paper title “How to not get rich: an empirical
study of donations in open source” 29 suggests, simply having a platform for
donations is not enough. In a 2022 study on GitHub Sponsors, Shimada et al. 36
found that out of approximately 9,000 developers who had activated their
GitHub Sponsors profile, less than 40% had received a donation.

If simply creating a sponsorship profile is not enough, what else can open-
source software developers do to attract donations? Following the long line of
work on studying the intersection between social media and software
development 40, 39, 16, in this paper, we investigate the impact of tweeting
about a GitHub Sponsors profile on sponsorship. To make it easy for its users
to reach a large audience, GitHub provides tweet templates that users can use
to advertise a new GitHub Sponsors profile (“My GitHub Sponsors profile is
live! You can sponsor me to support my open source work [width=1em]sparkling
heart”) or to broadcast that they made a donation (“[width=1em]sparkling heart
I’m sponsoring [username] because…”). The impact of tweets on open-source
software development has been investigated before. Fang et al. 16 found that
tweets have a significant effect on obtaining new stars and new contributors
for an open-source project and that the formation of an active Twitter/X
community plays an important role in attracting new contributors. The role of
tweets has also been studied in the context of bug fixing 25 and trend
awareness 37. To the best of our knowledge, the role of Twitter/X in obtaining
funding for open-source development has not yet been studied.

We first characterize the state of the practice by quantitatively and
qualitatively analyzing more than 10,000 tweets linking to GitHub Sponsors
profiles to understand the context of such tweets. We then measure the impact
of the tweets in terms of their reception on Twitter/X and their effect on
sponsorship. We found links to GitHub Sponsors profiles on Twitter/X are
common and the majority of such tweets are written by users other than the
profile owner, such as a sponsor. We identified a significant positive effect
of GitHub Sponsor profile mentions in tweets on the number of sponsors
acquired. Tweet mentions have the impact of increasing the number of sponsors
by 1.22. Although GitHub Sponsors has surpassed other platforms such as Open
Collective and Patreon in terms of visibility on Twitter/X, tweets about
GitHub Sponsors received significantly fewer likes, retweets, and replies
compared to other platforms. Developers tended to be more active during the
week of a tweet, in particular, in terms of the number of commits.

Significance of research contribution. The findings of our study have
significant implications, indicating a strong interconnection between social
media channels and donation pathways within the social programmer ecosystem
43. Our research demonstrates that actively engaging on social media platforms
to promote sponsorship opportunities for open-source development can yield
fruitful outcomes. This suggests that open-source developers stand to benefit
from expanding their presence and networking efforts beyond the GitHub
platform. Furthermore, our study highlights the notion that publicity and
visibility in the realm of open-source sponsorship need not be limited to a
unidirectional flow. Rather, sponsors themselves have the potential to enhance
the exposure and reach of open-source projects by publicizing their donations.
In doing so, they serve as exemplars, setting a positive precedent and
inspiring others to follow suit. By emphasizing these key findings, we provide
compelling evidence to support the notion that using social media channels,
diversifying online networks, and fostering mutual publicity between sponsors
and developers can yield substantial advantages within the open-source
community. These insights encourage open-source developers and sponsors alike
to consider the broader potential of social media engagement and collaborative
promotion to achieve their goals.

##  2\. Research Questions

The main objective of our study is to understand the state of practice and the
impact of GitHub Sponsors profile mentions on Twitter/X. The insights drawn
from this study will not only contribute to the academic understanding but
also have practical implications for developers, sponsors, and platforms like
GitHub. Furthermore, our findings can shed light on the relationship between
social activities and monetary contributions, ultimately serving to augment
the appeal of developers. To guide our investigation, we present main
questions and sub-questions, along with motivations and relevance.

RQ1: _How are GitHub Sponsors profiles discussed on Twitter/X?_ The motivation
behind RQ1 is to provide insights into the dynamics of GitHub Sponsors profile
mentions, ultimately informing better strategies for developers seeking
sponsorship.
RQ1.1 _What are the characteristics of tweets mentioning GitHub Sponsors
profiles from organizational and personal accounts?_ Understanding the
language, account types, and programming languages in these tweets will enable
developers to craft more appealing content for potential sponsors, ultimately
enhancing engagement.
RQ1.2 _Who mentions GitHub Sponsors profiles on Twitter/X?_ By identifying who
engages with these profiles, sponsorship acquisition strategies can be
tailored to target specific demographics.
RQ1.3 _What is the context of GitHub Sponsors profile mentions on Twitter/X?_
Investigating the context in which profiles are mentioned will shed light on
why tweets are used in sponsorship communication, potentially informing
strategies for developers seeking sponsorship.
RQ1.4 _When are GitHub Sponsors profiles mentioned on Twitter/X?_ Analyzing
the timing of mentions can lead to the discovery of optimal moments for
posting, which could help in securing sponsorship.

RQ2: _What is the impact of GitHub Sponsors profile mentions on Twitter/X?_
Building on RQ1, RQ2 explores the effects of the dynamics uncovered, allowing
us to measure and interpret their impact. RQ2.1 _How are GitHub Sponsors
profile mentions received on Twitter/X?_ Understanding the reception will aid
platforms like GitHub in providing targeted support and tools to developers,
such as social media templates and guidelines.
RQ2.2 _How are GitHub Sponsors profile mentions discussed on Twitter/X?_ By
examining engagement metrics and replies, we will gain a deeper understanding
of the conversations, ultimately helping in crafting more effective strategies
for community engagement.
RQ2.3 _How do GitHub Sponsors profile mentions impact sponsorship?_ Through a
quasi-experimental approach, our goal is to provide quantitative evidence of
the causal impact, which can guide both developers in improving their social
media strategies and platforms in enhancing features that facilitate
sponsorship acquisition.

By addressing these research questions, we aim to provide insights into the
dynamics and consequences of GitHub Sponsors mentions on Twitter/X. This
exploration contributes to theoretical understanding and practical strategies,
offering value to the broader Open Source community.

##  3\. Research Methods

This section describes our methods for data collection and our quantitative
and qualitative analyses.

###  3.1. Data Collection

In this study, we examine tweets containing links to GitHub Sponsors profiles.
We successfully applied for Twitter/X’s Academic Research Access 46, which
offers a higher limit on the number of tweets that can be retrieved per month,
and we analyzed tweets from May 2019, when GitHub Sponsors was launched,
through April 2022, using Twitter/X API v2 45 in May 2022. The Twitter/X API
provides a search function that allows for a set of query mechanisms against
tweets. We use the “url” query to retrieve tweets that contain links with the
specific substring “github.com/
sponsors/” so that we ensure all the tweets are developer-related. We obtained
11,582 tweets that contain GitHub Sponsors profile links. Among these tweets,
the majority (91%) were written in English, accounting for 10,531 tweets. We
only use English tweets for the following quantitative and qualitative
analyses, except RQ1.1.

###  3.2. Quantitative Analysis

To understand the characteristics of tweets mentioning GitHub Sponsors
profiles (RQ1.1), we investigate written languages, types of GitHub accounts,
and primary programming languages of developers mentioned in the tweets. For
written languages, we calculate the distribution of languages in tweets. In
cases where Twitter/X cannot determine the language of a tweet (e.g., the
tweet only contains hashtags, emojis, or links), Undetermined is used.

Since one GitHub Sponsors profile may appear in different tweets, we obtained
distinct GitHub Sponsors profiles in tweets to collect the types of GitHub
accounts and the primary programming languages of the corresponding
developers. We obtained 3,766 distinct GitHub Sponsors profiles from the
11,582 tweets. For the types of GitHub accounts, we calculate the distribution
of the types of GitHub accounts (i.e., personal or organizational) across all
distinct GitHub Sponsors profiles in tweets. Since the URL of a GitHub
Sponsors profile is organized as https://github.com/sponsors/ [username], we
can retrieve the corresponding GitHub account using username in the GitHub
GraphQL API 20.

The primary programming languages of the repositories can also be retrieved
using the GitHub GraphQL API. Same to the previous work 36, we take the most
common primary language of the repositories to which each developer
contributed as the primary language of that developer. This is an
approximation because we did not analyze whether the developer actually
committed in that language. If the occurrences of each programming language
per repository are the same, we consider the primary programming language to
be Undetermined. The primary languages of developers identified in this way
can be interpreted as the programming languages of the ecosystems to which the
developers mainly contributed.

To attract potential sponsors, developers might be particularly active on
GitHub around the time they advertise their GitHub Sponsors profile on
Twitter/X. To investigate whether such correlations exist, we considered three
time periods related to a “My GitHub Sponsors profile is live!” tweet, i.e., a
week before posting this tweet, the week in which the tweet was posted, and a
week after posting this tweet (RQ1.4). For example, if a tweet has been posted
on 15 June 2020, these three periods will be from 2020-06-05 to 2020-06-11,
2020-06-12 to 2020-06-18, and 2020-06-19 to 2020-06-25, respectively.
Following the approach of related work 11, which used a time frame of one week
before and after, our decision to adopt a one-week duration allows us to
quickly assess immediate changes in productivity, engagement, and quality.
This analysis involves scrutinizing short-term developer activities before and
after sponsorship, facilitating a timely evaluation. We obtained 810 distinct
GitHub Sponsors profiles that were posted using that template from our data
set. Then we investigate different categories of contribution activities in
each period, as shown below. To collect these contribution activities in a
week, we retrieve them from the profile pages of the GitHub accounts as
https://github.com/[username] ?tab=overview& from=[time period]&to=[time
period].

  * •

opening pull request: The profile page indicates that the GitHub account has
opened pull requests, including substrings such as “Created a pull request”,
“Opened 1 other pull request”, “Opened [number] pull requests”, and “Opened
their first pull request”.

  * •

submitting pull request review: The profile page indicates that the GitHub
account has reviewed pull requests, including a substring such as “Reviewed
[number] pull requests”.

  * •

opening issue: The profile page indicates that the GitHub account has opened
issues, including substrings such as “Created an issue”, “Opened [number]
other issues”, “Opened their first issue”, and “Opened [number] issues”.

  * •

opening discussion: The profile page indicates that the GitHub account started
a GitHub Discussion, including a substring such as “Started [number]
discussions”.

  * •

answering discussion: The profile page indicates that the GitHub account
answered a GitHub Discussion, including a substring such as “Answered [number]
discussions”.

  * •

committing: The profile page indicates that the GitHub account has authored
commits, including a substring such as “Created [number] commits”.

  * •

contributing in private repository: The profile page indicates that the GitHub
account contributed to private repositories, including a substring such as
“[number] contributions in private repositories”.

  * •

creating repository: The profile page indicates that the GitHub account
created private repositories, including substrings such as “Created [number]
other repositories”, “Created [number] repositories”, and “Created their first
repository”.

  * •

joining organization: The profile page indicates that the GitHub account
joined an organization, including a substring such as “Joined the [name]
organization”.

We conduct Mann-Whitney U tests to compare activities in these three periods,
i.e., between a week before posting the tweet and the week when the tweet was
posted, and between the week when the tweet was posted and a week after
posting this tweet. To estimate the effect size of significant differences, we
use Cliff’s delta with the following thresholds 35: negligible for 0 $\leq$
$|$delta$|$ $<$ 0.147, small for 0.147 $\leq$ $|$delta$|$ $<$ 0.33, medium for
0.33 $\leq$ $|$delta$|$ $<$ 0.474, and large otherwise.

To investigate the reception of tweets mentioning GitHub Sponsors profiles
(RQ2.1), we analyze the popularity of tweets that mentioned GitHub Sponsors
profiles on Twitter/X (number of likes, number of retweets, and number of
replies). Then, we compare these interactions to tweets that contain links to
other donation and crowd-funding platforms that are often used to obtain
financial support for OSS development 29: PayPal, Open Collective, and
Patreon. To ensure that the tweets obtained are related to OSS, we collect
tweets that contain links to at least one of these three platforms and GitHub
(i.e., “github.com”, except links to GitHub Sponsors) using Twitter/X API v2
in the same time period for which we collected GitHub Sponsors profile tweets.
We consider a link to point to a PayPal profile when it contains
“paypal.com/paypalme/”, Open Collective when it contains
“opencollective.com/”, and Patreon when it contains “patreon.com/”, except
Patreon posts (i.e., “patreon.com/posts/”). We exclude tweets that contain
links to “github.com/sponsors/” and at least one of these three platforms from
the 10,531 English tweets obtained. In the end, we obtained 10,440 tweets for
GitHub Sponsors, four tweets for PayPal, 88 tweets for Open Collective, and
228 tweets for Patreon. Since only four tweets contain links to PayPal, we
focus on comparisons between GitHub Sponsors, Open Collective, and Patreon.

We also conduct Mann-Whitney U tests to compare Twitter/X interactions between
GitHub Sponsors and Open Collective, and between GitHub Sponsors and Patreon.
We use Cliff’s delta with the same thresholds to estimate the effect size of
significant differences.

###  3.3. Qualitative Analysis

For our qualitative analyses, we randomly selected a statistically
representative number of tweets with a confidence level of 95% and a
confidence interval of 5 to obtain 371 tweets from the initial population of
10,531 English-language tweets.

Unsurprisingly, our initial analysis revealed differences between the dynamics
around tweets from users looking for sponsorship and those from users who made
donations. Therefore, we categorized the 371 tweets into three different
behavioral groups based on the purpose of the tweet, so we could see what kind
of tweets a user would make based on their behavior:

  * •

looking for sponsors: This tweet is posted by developers who publicized their
profiles to look for sponsors, e.g.,“My GitHub Sponsors profile is live! You
can sponsor me to support my open source work [width=1em]sparkling heart”.

  * •

sponsors: This tweet is posted by developers who sponsored others, e.g.,
“[width=1em]sparkling heartI’m sponsoring [username] because…”.

  * •

no purpose: This tweet does not have sufficient information to decide, e.g.,
“You guys make magic.”.

In the end, we identified 183 tweets from developers that were looking for
sponsors, 168 tweets from sponsors, and 20 tweets from no purpose. These 351
tweets (20 tweets from no purpose are excluded) are used for answering
RQ1.2–RQ1.4, and RQ2.2.

Four of the authors collaboratively took an initial look at a randomly
selected subset of 30 tweets from the sample of 351 tweets, discussed which
themes were present in the data and how these themes related to the research
questions, and then formalized this discussion into coding schemata. For each
aspect that entailed manual coding, a total of 30 tweets were independently
labeled by four annotators, resulting in Cohen’s kappa exceeding 0.6 for all
parts and even reaching 0.94 for RQ2.2.

Encouraged by the initial kappa agreements, the first two authors
independently coded the remaining sample of 321 tweets. Then, they
recalculated kappa agreements to assess the improvement in understanding of
the coding schemata after labeling the first 30 tweets. Finally, four authors
engaged in collaborative discussions to attain a consensus in cases of
disagreement. We attribute this stability to the fact that we had an initial
discussion about all data, that tweets are relatively short, and that this
particular team of authors has experience working together on qualitative data
analysis from previous research projects. We describe the coding schemata
related to each research question in the following paragraphs.

To investigate who mentions GitHub Sponsors profiles on Twitter/X (RQ1.2), we
analyze the relationship between the authors of the tweets and the GitHub
accounts that are linked in the tweets. Furthermore, the rationale behind
having a “user” category in the aforementioned code is rooted in the goal of
acquiring insights into what extent users benefit from a developer’s project
and are willing to voluntarily advertise the developer, thereby enabling the
developer to obtain more sponsorship. The prevalence of the code “non-
specific” in the results indicates that some users advertise for others
without a specific purpose. Since the names of accounts on Twitter/X and
GitHub do not necessarily have to be the same, we employed qualitative
analysis for this investigation. It is important to highlight that our
decision not to employ automated techniques for verifying the association
between Twitter/X and GitHub accounts was motivated by the realization that
these techniques often fail to account for certain scenarios. For example,
when a GitHub account is classified as an organization type and one of its
members posts a tweet, it should be regarded as emanating from the same user.
Consequently, we opt for a cautious approach, acknowledging the limitations of
automated techniques and acknowledging the need for context-sensitive judgment
in determining the correspondence between Twitter/X and GitHub accounts. The
four annotators independently labeled 30 tweets. Then, we calculate the kappa
agreement of our coding schemata from four annotators. The initial Cohen’s
kappa for this qualitative analysis is 0.75, which indicates ‘substantial’
agreement 47. For the remaining sample of 321 tweets, Cohen’s kappa is 0.78,
which also indicates ‘substantial’ agreement, from the first two authors.
Examples of the following codes are covered in our replication package, aiming
to facilitate the reader’s comprehension of this taxonomy.

  * •

same: The author of this tweet is the same as the GitHub account that is shown
on the GitHub Sponsors profile linked in the tweet, or the content of the
tweet implies that they are the same developer or the author belongs to the
GitHub organizational account on that GitHub Sponsors profile.

  * •

user: The tweet explicitly indicates that the author of this tweet is a user
of an open-source project that belongs to the GitHub account on the GitHub
Sponsors profile.

  * •

non-specific: There is not sufficient information to determine the
relationship between the tweet author and the GitHub account.

To understand the context of tweets mentioning GitHub Sponsors profiles
(RQ1.3), we analyze why GitHub Sponsors profiles were mentioned in tweets.
Additionally, the reason to distinguish between the “advertisement with new
information” and “advertisement with new functionality” categories in the
aforementioned coding schemata is to enable a more nuanced analysis: the
former encompasses a range of updates, including changes to users’ profile
descriptions and tier information whereas the latter is related to
functionality in the projects they are dedicated to. The four annotators
independently coded 30 tweets, achieving the initial Cohen’s kappa of 0.66 or
‘substantial’ agreement 47. The first two authors then independently labeled
the remaining sample of 321 tweets, finally reaching Cohen’s kappa of 0.84 or
‘almost perfect’ agreement. The following list shows the coding schema that
emerged from the data. Examples of the following codes are described in detail
in our replication package to help the reader understand this taxonomy.

  * •

generic advertisement: This tweet advertises the tweet author’s own GitHub
Sponsors profile (use this code if the tweet does not fit the other
advertisement categories).

  * •

donation appreciation: This tweet explicitly expresses appreciation of a
donation.

  * •

sponsor template: This tweet contains GitHub’s template for advertising one’s
own GitHub Sponsors profile: “My GitHub Sponsors profile is live! You can
sponsor me to support my open source work[width=1em]sparkling heart” with no
or minor changes.

  * •

advertisement of developer: This tweet advertises the GitHub Sponsors profile
of another personal GitHub account.

  * •

advertisement with new functionality: This tweet explicitly advertises the
author’s own GitHub Sponsors profile while mentioning new functionality of an
open-source project.

  * •

advertisement with new information: This tweet explicitly advertises the
author’s own GitHub Sponsors profile with an update.

  * •

sustainability: This tweet explicitly indicates an appreciation or need for a
donation for the sustainability of an open-source project, often associated
with terms such as “sustainable”.

  * •

advertisement with early access: This tweet explicitly advertises the author’s
own GitHub Sponsors profile with early access to features (usually accompanied
by a phrase such as “early access” and “insider”)

  * •

income: This tweet explicitly indicates the need for income to support one’s
daily life.

  * •

advertisement with event: This tweet explicitly advertises a GitHub Sponsors
profile with an event.

  * •

set example / peer pressure: This tweet explicitly motivates others in either
a positive or negative way.

  * •

advertisement of organization: This tweet advertises the GitHub Sponsors
profile of another organizational GitHub account.

  * •

donation to developer announcement: This tweet explicitly indicates that the
author of this tweet donated to the personal GitHub account in the GitHub
Sponsors profile.

  * •

donation to organization announcement: This tweet explicitly indicates that
the author of this tweet donated to the organizational GitHub account in the
GitHub Sponsors profile.

  * •

donation to developer template: This tweet contains GitHub’s template that
indicates donation to a personal GitHub account: “[width=1em]sparkling
heartI’m sponsoring [username] because…” with no or minor changes.

  * •

donation to organization template: This tweet contains GitHub’s template that
indicates donation to an organizational GitHub account: “[width=1em]sparkling
heartI’m sponsoring [username] because…” with no or minor changes.

To study when tweets related to GitHub Sponsors profiles occur in relation to
other activities on GitHub (RQ1.4), we analyze the timing of such tweets. The
four annotators independently coded 30 tweets, achieving the initial Cohen’s
kappa of 0.62 or ‘substantial’ agreement 47. For the remaining 321 tweets, the
first two authors reached Cohen’s kappa of 0.85 or ‘almost perfect’ agreement.
The following list shows the coding schemata that emerged from the data.

  * •

start: This tweet was posted when the GitHub Sponsors profile is activated
(usually accompanied by a phrase like “profile is live”).

  * •

no specific timing: This tweet was posted with no particular timing.

  * •

donation: This tweet was posted when the author of the tweet received a
donation.

  * •

update: This tweet was posted when there was an update to a GitHub project or
GitHub Sponsors profile.

  * •

reach goal: This tweet was posted in relation to reaching a goal.

  * •

release: This tweet explicitly indicates that a release of the software
project has been delivered.

  * •

event: This tweet was posted when an event has been announced.

  * •

resignation / paycut: This tweet was posted during a change in the author’s
work professional situation.

  * •

benefit: This tweet explicitly mentions a particular benefit.

  * •

activity spike: This tweet was posted to indicate the GitHub developer was
particularly active and explicitly mentions the activity spike.

To investigate the responses to tweets mentioning GitHub Sponsors profiles
(RQ2.2), we analyze the replies to tweets mentioning GitHub Sponsors profiles.
Four annotators independently annotated 30 tweets. The initial kappa agreement
is 0.94, interpreted as ‘almost perfect’ agreement 47. The first two authors
independently annotated the remaining 321 tweets, reaching the same kappa
agreement. Our coding schemata emerged from the data and is as follows. Note
that examples for these codes are described in detail in our replication
package to help the reader understand this taxonomy.

  * •

support: The response to this tweet demonstrates endorsement or encouragement
for the author, often extending beyond appreciation and indicating a
willingness to assist or advocate for the author’s cause.

  * •

appreciation of work: The respondent acknowledges and values the author’s
open-source contributions and their impact, without necessarily conveying
explicit support or a commitment to assist in further efforts.

  * •

appreciation of donation: The respondent to this tweet appreciates the
donation.

  * •

emoji only: The response to this tweet only contains emoji.

  * •

other: The response to this tweet does not fit into the categories above, or
there is no response to this tweet.

###  3.4. Causal Inference

We conduct a quasi-experiment to estimate the causal impact of GitHub Sponsors
profile mentions in tweets on the number of sponsors acquired (RQ2.3). Unlike
prior studies that have conducted quasi-experiments for causal inference in
software engineering by employing difference-in-differences 27, 48, 16 or
CausalImpact 26, we are unable to employ these methods. This is because these
methods require the values of the outcome variables in the periods before and
after the treatment, but data on the number of sponsors at a given point in
time were not available at the time we conducted our analysis.111We contacted
the GitHub team in a public forum and they responded that they would consider
making the sponsor count data publicly available; we do not provide a link to
that form because of the double-anonymous submission. Therefore, in this
analysis, we apply a statistical matching method called propensity score
matching (PSM), which attempts to estimate the effect of treatment by
constructing a control group by matching each treated unit with a non-treated
unit with similar characteristics 23. PSM predicts the probability of
belonging to the treatment and control groups based on observed predictors.
Some of the studies mentioned above used PSM to prepare data for the treatment
and control groups 27, 16.

To collect developers as potential members of a control group, we contacted
the authors of previous work 36 to obtain the list of GitHub users who had
participated in GitHub Sponsors. From 3,697 sponsored and 5,666 non-sponsored
developers collected in July 2021 for the previous study, we identified 1,930
and 4,913 developers who had not deleted their GitHub accounts and whose
GitHub Sponsors profiles do not appear in our tweet data (neither in their own
tweets nor in tweets from others). Potential members of the treatment group
are developers whose GitHub Sponsors profiles appear in the “sponsor template”
tweets, that is, “My GitHub Sponsors profile is live!”. By targeting only
“sponsor templates”, the influence of wording differences can be eliminated.
We observed that “sponsor template” appears most often after “sponsor
template”, which are free-text tweets (see Section 4.1.3). To limit developers
to those who started using GitHub Sponsors at the same period as control group
developers, we collected only those developers whose GitHub Sponsors profiles
appeared in such tweets by July 2021 and identified 568 developers.

The following are variables of developers used in the logistic regression to
estimate the propensity score for PSM.

These variables have been used in previous related studies: for example,
sponsored developers sponsor more than non-sponsored developers 36, sponsored
developers form language-specific clusters that sponsor each other 36, and the
number of followers is the most important feature for predicting long-term
contributors 4. All values were measured in August 2022.

  * •

repositories: The number of public repositories created.

  * •

sponsoring: The number of developers sponsoring.

  * •

openedPRs: The number of opened pull requests.

  * •

reviewedPRs: The number of reviewed pull requests.

  * •

followers: The number of followers.

  * •

organizations: The number of joined organizations.

  * •

language: Categorical variable for the primary programming language determined
by the method described in Section 3.2. The values are the top 10 languages
(JavaScript, Python, PHP, C#, Go, Java, TypeScript, C++, Ruby, and C) and
others (including undetermined) seen in Table 2. In regression model building,
dummy variables are prepared that take a value of 0 or 1 indicating the
absence or presence of a particular language.

Figure 1. Covariate balance before (unadjusted) and after (adjusted)
propensity score matching.

We obtained 1,094 matched developers out of 7,411 ($1,930+4,913+568$)
developers from the PSM. Figure 1 shows how the absolute mean differences have
decreased as a result of the matching, from unadjusted to adjusted (unadjusted
indicates all developers before matching, and adjusted indicates matched
developers). None of the absolute mean differences of adjusted exceeds 0.10,
which means that we obtained developer matches for the treatment and control
groups with a balanced distribution of covariates 23. This balance is a
measure of the quality of the propensity score matching and we achieved the
well-established and well-cited threshold 3, 28.

To estimate the impact of GitHub Sponsors profile mentions in tweets, a linear
regression is performed using the above variables and the variable treatment,
which takes a value of 0 or 1 that indicates the presence or absence of
general template tweets. The outcome variable is the number of sponsors
obtained by each developer, measured in August 2022. Therefore, this analysis
estimates the impact of tweeting “My GitHub Sponsors profile is live!” on the
number of sponsors as of August 2022, for early adopters starting GitHub
Sponsors and tweeting from May 2019 (GitHub Sponsors launched) through July
2021.

##  4\. Results

This section presents answers to our research questions.

###  4.1. RQ1: _How are GitHub Sponsors profiles discussed on Twitter/X?_

The results of the analysis of the characteristics, participants, context, and
timing of tweets mentioning GitHub Sponsors profiles are presented in this
section.

Table 1. Frequency of written languages of tweets that contain links to GitHub Sponsors profiles. written languages | Person | Organization
---|---|---
English | 3,074 (94%) | 479 (97%)
Japanese | 151 (5%) | 5 (1%)
Undetermined | 18 (0%) | 1 (0%)
Spanish | 8 (0%) | 1 (0%)
Other | 19 (0%) | 4 (0%)
sum | 3,270 (100%) | 496 (100%)
Table 2. Frequency of GitHub account types and primary programming languages of distinct GitHub Sponsors profiles. | programming languages | Person | Organization
---|---|---|---
| JavaScript | 816 (25%) | 89 (18%)
| Python | 333 (10%) | 47 (9%)
| PHP | 309 (9%) | 44 (9%)
| C# | 228 (7%) | 22 (4%)
| Go | 180 (6%) | 21 (4%)
| Java | 153 (5%) | 21 (4%)
| Other | 1251 (38%) | 252 (51%)
| sum | 3,270 (100%) | 496 (100%)

####  4.1.1. RQ1.1: _What are the characteristics of tweets mentioning GitHub
Sponsors profiles from organizational and personal accounts?_

We investigated the written languages, GitHub account types, and the primary
programming languages of the developers mentioned in the tweets. These
elements were categorized based on whether they originated from personal or
organizational accounts. This initial analysis serves as a foundation for our
subsequent in-depth investigation, offering an initial understanding of the
nature of these tweets.

Written languages. Table 1 presents the frequency of written languages in
tweets that contain links to GitHub Sponsors profiles. Compared to the ranks
and portions of the written languages of general tweets 22, in English GitHub
Sponsors profile tweets, both personal and organizational accounts make up a
significantly larger portion than general English tweets (51%). Japanese
tweets rank second in general tweets, comprising 5% of personal accounts and
1% of organizational accounts. Spanish also stands out as a top contributor
among the top ten languages used frequently in general tweets.

GitHub account types. As seen in Table 1 and Table 2, most GitHub Sponsors
profiles mentioned in tweets are associated with personal accounts, accounting
for 87% of 3,766 distinct GitHub Sponsors profiles. Approximately a fifth of
GitHub Sponsors profiles in tweets are associated with organizational
accounts, representing 13% of distinct GitHub Sponsors profiles in the
obtained tweets. According to GitHub’s advanced search engine 19 in August
2022, 18,129 personal GitHub accounts had activated GitHub Sponsors,
accounting for 91%. Furthermore, only 9% of all GitHub accounts (1,889) that
activated GitHub Sponsors are organizational accounts. Comparing GitHub
Sponsors profiles that were posted on Twitter/X and all GitHub Sponsors
profiles on GitHub, they tend to share a similar trend for GitHub account
types.

Programming languages. Among the 3,766 distinct GitHub Sponsors profiles
mentioned in the collected tweets, JavaScript stands out as the most prominent
language, with 25% of personal accounts, suggesting its popularity among
individual users. Conversely, its relatively lower representation in
organizational accounts (18%) may indicate a preference for other languages in
professional settings. Python, with 10% of usage among personal accounts,
appears to be a language of choice for individual developers, potentially due
to its versatility and readability. The prevalence of Python and PHP, both of
them at 9%, among organizational accounts hints at their significance in
enterprise-level development projects, as seen in Table 2. In the “other”
category of coding repositories, where many instances are labeled as “None”,
there are organizations like PJSoftCo.222https://github.com/PJSoftCo They are
a prime example of how GitHub organizations are using sponsorship funds to
invest in their organization-wide documentation. Comparing these results with
previous work 36, we find that, except for Undetermined, the top four
programming languages are exactly the same. The top ten primary programming
languages are the same on individual GitHub Sponsors and GitHub Sponsors
profiles that were posted on Twitter/X.

Table 3. Frequency of relationships between tweet authors and linked GitHub Sponsors profiles. | looking for sponsors | sponsors
---|---|---
same | 169 (48%) | -
user | - | 55 (16%)
non-specific | 14 (4%) | 113 (32%)
sum | 183 (52%) | 168 (48%)

####  4.1.2. RQ1.2: _Who mentions GitHub Sponsors profiles on Twitter/X?_

Table 3 shows the results of the coding for RQ1.2. As mentioned in Section
3.3, we separated tweets by purpose, distinguishing developers who mention
their GitHub Sponsors profiles to look for sponsors from those who are
sponsors. Developers that were looking for sponsors mentioning their own
GitHub Sponsors profiles in tweets is the most frequently occurring case,
accounting for 48% of the sample. However, it is also common that sponsors
mention GitHub Sponsors profiles of other GitHub accounts, accounting for 32%
of the sample. We observe that sponsors also explicitly mentioned GitHub
Sponsors profiles of others due to dependencies or other benefits, accounting
for 16% of the sample. In previous work 36, Shimada et al. showed that
developers sponsoring others via GitHub Sponsors due to dependencies is the
most frequent reason for sponsoring. In the context of Twitter/X, our result
partially agrees with their observations.

Table 4. Frequency of context of the GitHub Sponsors profile mentions on Twitter/X. | looking for sponsors | sponsors
---|---|---
generic advertisement | 72 (21%) | -
donation appreciation | 34 (10%) | -
sponsor template | 33 (9%) | -
advertisement of developer | 9 (3%) | 7 (2%)
advertisement with new functionality | 9 (3%) | -
advertisement with new information | 8 (2%) | -
sustainability | 5 (1%) | -
advertisement with early access | 5 (1%) | -
income | 2 (1%) | -
advertisement with event | 3 (1%) | -
set example / peer pressure | 2 (1%) | 4 (1%)
advertisement of organization | 1 (0%) | 2 (1%)
donation to developer announcement | - | 101 (29%)
donation to organization announcement | - | 33 (9%)
donation to developer template | - | 16 (5%)
donation to organization template | - | 5 (1%)
sum | 183 (52%) | 168 (48%)
Table 5. Frequency of timing of tweets that contain links to GitHub Sponsors profiles. | looking for sponsors | sponsors
---|---|---
start | 62 (18%) | -
no specific timing | 46 (13%) | 111 (32%)
donation | 35 (10%) | -
update | 20 (6%) | -
reach goal | 8 (2%) | -
release | 6 (2%) | -
event | 3 (1%) | -
resignation / paycut | 3 (1%) | -
benefit | - | 55 (16%)
activity spike | - | 2 (1%)
sum | 182 (52%) | 169 (48%)

####  4.1.3. RQ1.3: _What is the context of GitHub Sponsors profile mentions
on Twitter/X?_

In Table 4, the frequency of various GitHub Sponsors profile mentions on
Twitter/X is presented. Most sponsors mentioned GitHub Sponsors profiles on
Twitter/X in the context of donating to personal or organizational GitHub
accounts, accounting for 29% and 9%, respectively. Sponsors also mentioned
their donation to personal or organizational GitHub accounts using GitHub’s
tweet templates, accounting for 5% and 1%, respectively.

In addition to donations, developers looking for sponsors mentioned GitHub
Sponsors profiles on Twitter/X to advertise their own profile (21%) or to
advertise profiles of other personal GitHub accounts (3%). Specifically,
developers looking for sponsors advertise their own profile with updates on
the functionality of the project (3%), updates on the profile (2%), early
access features (1%), and events (1%). As with the donation, developers
looking for sponsors advertised their own profiles using GitHub’s tweet
templates, accounting for 9%. Furthermore, a few developers looking for
sponsors also posted tweets to encourage others to donate by setting an
example or applying peer pressure, accounting for 1%.

Some developers looking for sponsors use Twitter/X as a channel to express
appreciation to sponsors (10%). Furthermore, a few tweets from developers
looking for sponsors mention GitHub Sponsors profiles in the context of
sustainability of the project or the financial income of developers. In
particular, we see that several tweets are posted to share GitHub Sponsors
updates in the context of the Log4j vulnerability 13 that was exploited in
December 2021. For example, “It’s nice to see that a month after the Log4Shell
vulnerability Log4j’s maintainer has 101 GitHub Sponsors instead of 3,
including corporate accounts such as Amazon Web Services”.

####  4.1.4. RQ1.4: _When are GitHub Sponsors profiles mentioned on
Twitter/X?_

Table 5 presents the frequency of different types of timing when different
types of developers mentioned GitHub Sponsors profiles on Twitter/X.
Regardless of the different types of developers, we find that most of the
tweets (45%) do not specify an explicit explanation of the reason for the
tweet’s posting at that particular time. However, we can see that some tweets
(16%) were posted at a time when sponsors benefited from a project. Developers
that were looking for sponsors posted those tweets during the initiation of
GitHub Sponsors profiles (18%), at the time of donation (10%), or when
updating projects or profiles (6%). Furthermore, some developers that were
looking for sponsors posted tweets with GitHub Sponsors profile mentions when
they need financial resources due to changes in their work arrangements.

As seen in Table 5, we found an interesting type of timing with regard to when
GitHub Sponsors profiles were mentioned in tweets: activity spikes, i.e., a
sponsor donated due to an activity spike of a developer. Inspired by this
code, we conducted a quantitative study to analyze the correlations between
contributions of developers and GitHub Sponsors profile mentions on Twitter/X.
Table 6 presents comparisons among three periods across a set of GitHub
contribution types. Since the GitHub organization account lacks information on
activity, we excluded tweets that contain GitHub Sponsors from organizations.
Then, we focus on tweets from distinct developers that tweeted with “My GitHub
Sponsors profile is live!”. Only 810 tweets were included out of the 10,531
English tweets. The rationale behind this choice was to specifically analyze
the initial reactions and sentiments expressed by users who had just enabled
their GitHub Sponsors account. Our primary goal was to capture the immediate
activity of individuals in this specific context. Our sample is representative
of GitHub Sponsors users’ initial tweets about their GitHub Sponsors account,
but not of all tweets in our dataset. We observe that most of the mean values
for the week in which a tweet was posted are higher than the corresponding
values in the week before or after. For the contribution activities of Opening
discussion, Committing, and Creating repository, there are significant
differences between a week before posting this tweet and the week when the
tweet was posted, and between the week when the tweet was posted and a week
after posting this tweet, with at least negligible effect sizes (Committing
shows small effect size). In addition to these activities, comparing a week
before posting the tweet and the week when the tweet was posted, we find that
developers proposed significantly more pull requests, with negligible effect
size. These results indicate that when developers posted their GitHub Sponsors
profile on Twitter/X, they generally contribute more actively to OSS projects.

Summary: Of the GitHub Sponsors profiles mentioned in the tweets, 87% belong
to individual developers, whose top primary languages were JavaScript, Python,
and PHP. Such tweets were posted by the owners of the profiles or by others
who depended on the work of the developer they sponsored. Developers looking
for sponsors were more active on GitHub during the week in which tweets
linking to their GitHub Sponsors profile were posted.

Table 6. Comparisons among three periods of GitHub contributions

| Before | During | After
---|---|---|---
| mean | Q3 | effect size | mean | Q3 | effect size | mean | Q3 | effect size
Opening PR | 1.12 | 1 | 0.0929*** | 1.75 | 2 | - | 1.5 | 1 | -
Subm. PR review | 1.2 | 0 | - | 1.28 | 0 | - | 1.19 | 0 | -
Opening issue | 0.61 | 0 | - | 1.01 | 1 | - | 0.84 | 1 | -
Opening disc. | 0.03 | 0 | 0.0419*** | 0.12 | 0 | - | 0.06 | 0 | 0.0274**
Answering disc. | 0.02 | 0 | - | 0.03 | 0 | - | 0.04 | 0 | -
Committing | 13.49 | 15 | 0.181*** | 18.31 | 21 | - | 14.4 | 16.75 | 0.153***
Contr. to priv. repo. | 7.69 | 6 | - | 8.39 | 7 | - | 7.42 | 5.75 | -
Creating repo. | 0.52 | 0 | 0.0938*** | 0.71 | 1 | - | 0.5 | 0 | 0.103***
Joining org. | 0.01 | 0 | - | 0.01 | 0 | - | 0.01 | 0 | -

  * •

* p-value $<$ 0.05; ** p-value $<$ 0.01; and *** p-value $<$ 0.001. The Cliff’s delta effect size with thresholds 35 are highlighted in Negligible Small Medium Large. The hyphen (-) is used as a placeholder for cases where p-value $\geq$ 0.05 indicates there is no significant difference in the comparison or when comparing to itself.

Table 7. Frequency of response to tweets. | looking for sponsors | sponsors
---|---|---
other | 165 (47%) | 138 (39%)
appreciation of work | 9 (3%) | -
support | 7 (2%) | 3 (1%)
appreciation of donation | 2 (1%) | 25 (7%)
emoji only | - | 2 (1%)
sum | 183 (52%) | 168 (48%)
Table 8. Comparisons among donation and crowd-funding platforms in Twitter
interactions.

| like | retweet | reply | #
---|---|---|---|---
| median | effect size | median | effect size | median | effect size
Open Collective | 4 | - | 1 | 0.216*** | 0 | - | 88
Patreon | 6 | 0.265*** | 2 | 0.278*** | 0 | 0.132*** | 228
GitHub Sponsors | 3 | - | 0 | - | 0 | - | 10,440

  * •

* p-value $<$ 0.05; ** p-value $<$ 0.01; and *** p-value $<$ 0.001. The Cliff’s delta effect size with thresholds 35 are highlighted in Negligible Small Medium Large. The hyphen (-) is used as a placeholder for cases where p-value $\geq$ 0.05 indicates there is no significant difference in the comparison or when comparing to itself (GitHub Sponsors).

Table 9. Frequency of response to tweets. | looking for sponsors | sponsors
---|---|---
none | 140 (40%) | 130 (37%)
other | 27 (8%) | 9 (3%)
support | 6 (2%) | 2 (1%)
appreciation of work | 6 (2%) | -
appreciation of donation | 2 (1%) | 25 (7%)
emoji only | - | 2 (1%)
sum | 182 (52%) | 169 (48%)

###  4.2. RQ2: _What is the impact of GitHub Sponsors profile mentions on
Twitter/X?_

####  4.2.1. RQ2.1: _How are GitHub Sponsors profile mentions received on
Twitter/X?_

Table 8 presents comparisons between donation and crowd-funding platforms in
Twitter interactions. For median values, Patreon tweets received the highest
number of likes, accounting for six. Additionally, Patreon tweets were
retweeted twice, which is the highest number of retweets in terms of median
values. The median values of replies for the three platforms are zero. In
terms of likes, the p-value of Patreon vs. GitHub Sponsors is less than 0.05
and Cliff’s delta is 0.265, indicating Patreon and GitHub Sponsors have a
significant difference with a small effect size. In terms of retweets, Open
Collective and GitHub Sponsors have a significant difference (i.e., p-value
$<$ 0.05) with a small effect size (i.e., 0.147 $\leq$ $|$delta$|$ $<$ 0.33).
Comparing Patreon and GitHub Sponsors in terms of retweets, there is a
significant difference with a small effect size. In terms of replies, we find
that Patreon and GitHub Sponsors have a significant difference with a
negligible effect size (i.e., Cliff’s delta is 0.132). In conclusion, while
the number of tweets containing GitHub Sponsors profile mentions is much
larger, tweets that link to Patreon or Open Collective in the context of OSS
receive more likes and retweets.

####  4.2.2. RQ2.2: _How are GitHub Sponsors profile mentions discussed on
Twitter/X?_

Table 9 shows the results of our coding of replies to tweets that mentioned
GitHub Sponsors profiles. We can see that most tweets (86%) do not receive a
response on Twitter/X. For the remaining 14%, the majority consists of
expressions of appreciation for donations (8%). Since Twitter/X is an informal
communication channel, we observe that some responses consist only of one or
more emoji.

Table 10. Causal inference impact of GitHub Sponsors Profile Mentions in “My GitHub Sponsors Profile is Live!” Tweets. | estimate | std. error | p
---|---|---|---
treatment | 1.22 | 0.452 | 0.00681
repositories | -0.000818 | 0.00209 | 0.696
sponsoring | 1.12 | 0.194 | 1.01e-8
openedPRs | 0.000432 | 0.000457 | 0.345
reviewedPRs | 0.00301 | 0.00140 | 0.0325
followers | 0.00271 | 0.000345 | 8.42e-15
organizations | -0.0637 | 0.0870 | 0.465
language_JavaScript | -1.45 | 0.657 | 0.0279
language_Python | -1.18 | 0.856 | 0.168
language_PHP | -0.343 | 0.906 | 0.705
language_C# | 0.559 | 0.973 | 0.566
language_Go | -0.933 | 1.02 | 0.360
language_Java | -1.66 | 1.15 | 0.149
language_TypeScript | -0.692 | 1.04 | 0.505
language_C++ | -1.07 | 1.44 | 0.458
language_Ruby | -1.01 | 1.50 | 0.500
language_C | -0.659 | 1.21 | 0.586

####  4.2.3. RQ2.3: _How do GitHub Sponsors profile mentions impact
sponsorship?_

Table 10 summarizes the regression result. As seen in the coefficient estimate
of treatment, there is a statistically significant positive effect of GitHub
Sponsors profile mentions in tweets on the number of sponsors acquired. As the
average of the expected causal effect of treatment on individuals in the
treatment group, called Average Treatment Effects on the Treated (ATT), we
find that tweet mentions have an impact of increasing the number of sponsors
by 1.22. However, note that the medians, Q3, and means for the matched
treatment and control groups are {$0,2.00,2.56$} and {$0,1.00,1.30$},
respectively, indicating a skewness in the developers who obtained
sponsorship, that is, the effects are not uniform.

Summary: GitHub Sponsors profile mentions have a positive impact on the number
of sponsors acquired, increasing the number of sponsors by 1.22. On Twitter/X,
tweets mentioning GitHub Sponsors receive fewer interactions than those
mentioning Patreon or Open Collective, and most tweets do not attract replies.

##  5\. Threats to Validity

Subjective nature of coding. We conducted qualitative analyses of a
statistically representative sample of tweets. The codes we assigned to
different tweets may be inadequate due to the subjective nature of
understanding the various coding schemata. To migrate this threat, we require
kappa agreements of at least “substantial agreement” to ensure a common
understanding of the coding schemata among all four annotators. Then, we
initiated another round of coding between the first two authors for the
remaining sample. By recalculating kappa agreements, we can see the
improvement in understanding the various coding schemata. For example, Cohen’s
kappa increased from 0.62 for the first 30 tweets to 0.85 for the reaming 321
tweets in the coding of the timing of tweets. The final results are based on
the codes on which the authors, after discussion, reached a consensus and
collectively agreed.

Limitations in causal inference result. We compared developers with and
without tweets who started GitHub Sponsors in the same period and engaged in
similar activities, but we may have missed important developer characteristics
other than the metrics we measured. The result is best interpreted as an
increase in sponsors acquired through social activities on Twitter/X, rather
than simply tweeting “My GitHub Sponsors profile is live!”. In this study, we
only analyzed the impact of tweets using such a template, so the impact of
free-text tweets is unknown. In addition, since this analysis was conducted on
early adopters, it is not possible to generalize whether similar effects will
be seen in the future, so a continued analysis is needed.

Multiple GitHub Sponsors profiles in the same tweet. There is a small number
of cases where the same tweet contains multiple GitHub Sponsors profile links
to different GitHub accounts. Since these cases are rare (i.e., only five
tweets) and to avoid confusion in our analyses, we exclude these tweets from
our analyses.

Only tweets with GitHub Sponsors profiles links. Simple keyword searches would
have introduced too much noise to our large-scale analysis. To avoid false
positives, we only recovered tweets with GitHub Sponsors profile links, but we
acknowledge that other relevant tweets without links may have been omitted.

Primary programming languages of the developers. We considered the most common
primary language of the repositories to which each developer contributed as
the primary language of that developer. This means it could happen for some
users, for example, that the most common language of a developer’s contributed
repositories is Java, but the developer may only contribute the documentation
of these Java repositories, whereas committing Python code to another project.
Therefore, it is important to acknowledge this potential limitation in
accurately capturing a developer’s primary programming language through this
methodology.

The number of tweets mentioning GitHub Sponsors has a different scale of data
compared with other sponsorship platforms. We collected 10,440 tweets for
GitHub Sponsors compared to other platforms: 4 for PayPal, 88 for Open
Collective, and 228 for Patreon. It is important to recognize that this
difference in data size may affect the robustness and generalizability of our
conclusions.

External validity is concerned with our ability to generalize based on our
results. In Section 4.1.4, we used a subset of 810 tweets from a pool of
10,531 English tweets. It is crucial to acknowledge that the chosen subset may
not fully represent the broader spectrum of reactions across all types of
tweets related to GitHub Sponsors. Users who express their thoughts in
different formats or use alternative phrases may not be fully captured in our
analysis.

##  6\. Discussion

This section presents implications and future work from this study.

Implications. We categorized the practical implications for diverse groups of
individuals by offering tailored guidance and recommendations that align with
the specific concerns and interests of each stakeholder group.

  * •

Developers seeking sponsorship: our study shows that mentioning GitHub
Sponsors profiles in tweets has a positive impact on the number of sponsors
acquired. The finding that the number of sponsors acquired increased depending
on whether they tweeted, is evidence of the importance of social media and
should encourage developers to go beyond the GitHub platform in order to
attract sponsorship. Additionally, our research reveals many different types
of messages surrounding GitHub Sponsors in various contexts, providing
insights that might assist others in crafting their own effective social media
strategies for sponsorship engagement.

  * •

Developers interested in sponsoring: Within our sample, approximately half of
the participants are sponsors. This finding underscores the importance of
encouraging users who depend on OSS projects to actively promote the
developers they rely on, even if the sponsorship amount is not substantial.
Engaging in social media promotion can significantly enhance the visibility of
these developers, allowing their exceptional work to reach a wider audience
and garner increased recognition.

  * •

Companies: The relationship between companies and OSS projects is undergoing a
pivotal change, largely driven by an expanding recognition of sustainability
issues inherent in OSS. Instead of merely expressing dissatisfaction with the
lack of sustainability in these projects, our study offers evidence that a
two-pronged approach of corporate sponsorship and active social media
engagement could be an effective strategy for businesses. This strategy allows
them to constructively engage with OSS projects they rely upon, particularly
those struggling with sustainability, thereby addressing their concerns and
contributing to potential solutions.

Future Work. As our study is positioned as an early adopter study, we have not
yet obtained conclusive evidence of a significant impact of financial support
on OSS sustainability at this stage. Therefore, further investigation of the
potential impact of financial support on sustainability is needed.

We have focused on Twitter/X as the starting point for our exploration. For
future research, there is significant value in extending our analysis to
encompass posts from multiple social media platforms (e.g., Facebook, Reddit)
to gain a more comprehensive understanding of these dynamics.

As part of our investigation into RQ2.1, we found that tweets linking
exclusively to GitHub Sponsors were more common, and among those platforms,
GitHub is the only one that provides Twitter/X templates for developers
looking for sponsors and Twitter/X templates for sponsors. However, GitHub
Sponsors received fewer responses compared to tweets promoting alternative
sponsorship platforms. Since GitHub Sponsors launched 4–6 years later than
Open Collective and Patreon, so it had less time to solidify its position and
gain widespread awareness. Further research is needed to determine the
importance of the template if such a sponsorship platform provides a template
when users are trying to advertise on social media, and strategies for
increasing the response and engagement of tweets containing only links to
GitHub Sponsors.

In light of our findings regarding the skewness in the developers who obtained
sponsorship (Section 4.2.3), factors other than tweets may play an important
role in sponsor acquisition. Thus, further research is needed to explore and
identify these additional factors that contribute to the sponsor acquisition
process.

In subsequent studies, it would be valuable to investigate the specific
strategies and practices employed by organizations when leveraging social
media platforms to disseminate project updates. By examining the relationship
between these practices and the resulting engagement levels within the
community, we can gain insights into the effectiveness of such approaches and
their potential for enhancing community involvement. Moreover, it is worth
considering a more in-depth investigation into how the domain and
functionalities of open-source projects can impact and guide the dynamics of
sponsorship, such as evaluations of a project’s sustainability and its
sponsorship status 33.

##  7\. Related Work

In this section, we situate our work with respect to the literature on
donations and one potential advertising channel, Social Media.

Donation. OSS development heavily relies on volunteer contributions, as
highlighted in a recent GitHub survey 18, revealing that just 23% of
respondents contribute to open source as part of their job description.
Despite more employees being paid for contributing to OSS projects during work
hours 34, developers still perceive compensation asymmetry in OSS projects 2.
OSS projects that are distributed unequally may fail if they are mismanaged
and financial benefits are a factor in the sustainability of OSS projects 2.
Donation is one of the common ways to obtain these financial benefits 14 to
support OSS projects, in addition to Bounty 14. In a mixed-method empirical
study, Overney et al. 29 found that only a few projects (0.04–0.2%) ask for
donations, primarily using platforms like PayPal and Patreon. These projects
tend to be more active, more mature, and more popular.

Recently, Zhou et al. 50 explored donations on the Open
Collective platform that support open-source projects. They indicated the
influence of individual donors; although corporate donors tend to donate more
money than individual donors for an individual donation, the total donation
amount from individual donors is greater than corporate donors. However,
corporate collectives are more likely to receive a larger total donation
amount than individual collectives. Regarding the study on GitHub Sponsors,
Shimada et al. 36 revealed that developers typically do not have channels at
their disposal to attract sponsors and communicate with those who might be
interested in donating. Zhang et al. 49 discovered that sponsorship through
GitHub Sponsors has a short-term impact on developers’ activities. Their
survey highlighted key challenges, including the difficulty of attracting
sponsorship and the absence of corporate support.

Social Media. Social media channels are one way of communicating and
advertising in the world of developers. Different social media channels play
different roles and have different impact on OSS projects, e.g., facilitating
communication 6, 39, awareness of the status of other developers 5, 7, gaining
attention 9, 24, 44, and attracting new contributors 8, 24, 32.

Researchers studied the use of microblog services such as Twitter/X in
software development 9. The tweets of developers differ from those of the
general public in terms of length, use of URLs, and @-mentions, and software
microbloggers are more tightly knit than general microbloggers 10, 42.
Twitter/X is widely adopted in the software engineering community 6, 40. Tian
et al. 41 found that knowledge sharing, technical discussion, and software
product updates are the most frequent categories of developers’ tweets. Fang
et al. 15 proposed an approach to cross-link users on Twitter/X and GitHub;
they observed that tweeting patterns appear in tweets from different developer
roles when including GitHub links in their tweets (e.g., repository owners
prompt their projects instead of discussing specific software artifacts).

For the impact of Twitter/X, Singer et al. 37 indicated that Twitter/X can
help developers become aware of industry changes, learn, and build work
relationships in communities. Mezouar et al. 25 found that tweets from end
users can lead to early discovery of bugs in web browsers. Fang et al. 16
explored the causal effects of Twitter/X on the attraction of stars and
contributors by analyzing tweets that contain links to GitHub repositories.
They found that Twitter/X has a statistically significant and sizeable effect
to help make projects popular (i.e., stars) but only a small effect to attract
new contributors (i.e., commits). Moreover, these newly attracted contributors
showed to be more active in OSS projects when they had prior Twitter/X
interactions with the tweet authors.

##  8\. Conclusion

There are several platforms that enable contributions to open-source software
developers, but attracting sponsors in order to ensure project sustainability
remains a challenge. To understand the impact of Twitter/X on helping OSS
developers attract sponsors, we conducted quantitative and qualitative
analyses of more than 10,000 tweets containing links to GitHub Sponsors
profiles. We find that such tweets have a significant positive effect on the
acquisition of sponsors, and that developers contribute more OSS work than
usual to attract potential sponsors during the week in which they posted
tweets that link to their own GitHub Sponsors profile.

Open-source developers who maintain an active presence on social media can
attract donations that help sustain their projects. Our findings suggest that
social media channels and donation channels are linked in the social
programmer ecosystem and will continue to grow in importance for the
sustainability of open source software.

## Data Availability

The replication package includes scripts and data set, which is available at
https://doi.org/10.5281/zenodo.10461383 and https://github.com/NAIST-
SE/GHSponsorsX

## ACKNOWLEDGEMENTS

This work was supported by JSPS KAKENHI Grant Numbers JP20H00587 and
JP20H05706, JSPS Grant-in-Aid for JSPS Fellows JP23KJ1589, and JST PRESTO
Grant Number JPMJPR22P6.

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