Best Paper Honorable Mention at CHI 2011.

# Computers Can’t Give Credit: How Automatic Attribution Falls Short in an
Online Remixing Community

Andrés Monroy-Hernández1,2 Benjamin Mako Hill1
Jazmin Gonzalez-Rivero2 danah boyd2

1Massachusetts Institute of Technology, Cambridge, MA 02139  2Microsoft
Research, Cambridge, MA 02142<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

In this paper, we explore the role that attribution plays in shaping user
reactions to content reuse, or remixing, in a large user-generated content
community. We present two studies using data from the Scratch online community
– a social media platform where hundreds of thousands of young people share
and remix animations and video games. First, we present a quantitative
analysis that examines the effects of a technological design intervention
introducing automated attribution of remixes on users’ reactions to being
remixed. We compare this analysis to a parallel examination of “manual”
credit-giving. Second, we present a qualitative analysis of twelve in-depth,
semi-structured, interviews with Scratch participants on the subject of
remixing and attribution. Results from both studies suggest that automatic
_attribution_ done by technological systems (i.e., the listing of names of
contributors) plays a role that is distinct from, and less valuable than,
_credit_ which may superficially involve identical information but takes on
new meaning when it is given by a human remixer. We discuss the implications
of these findings for the designers of online communities and social media
platforms.

###### keywords:

remixing, attribution, credit-giving, user-generated content, online
communities

###### category:

H.5.3 Group and Organization Interfaces

††terms:  Design, Experimentation, Human Factors

##  1 Introduction

Networked information technologies have changed the way people use and reuse
creative – and frequently copyrighted – materials. This change has generated
excitement, and heated debate, among content-creators, technologists, legal
academics, and media scholars. Media theorist Lev Manovich argues that
remixing is an ancient cultural tradition (e.g., he has suggested that ancient
Rome was a “remix” of ancient Greece) but that information technologies have
accelerated these processes and made remixing more salient [14]. Sinnreich et
al. argue that “configurable culture” has been significantly transformed by
networked technologies which introduce perfect copying and allow people not
only to be inspired by extant creations but to remix the original works
themselves [25]. Legal scholars have stressed the importance of remixing in
cultural creation broadly and warned that current copyright and intellectual
property laws may hinder creativity and innovation [11, 1].

Several of the most influential scholarly explorations of remixing as a
cultural phenomenon have focused on youth’s remixing practices. For example,
work on remixing by Jenkins [10] and Ito [9] has focused on young people’s use
and re-use of media. Palfrey and Gasser have suggested that the cultural
practices of “digital native” youth have had a significant transformative
effect on our culture [17]. Throughout his book “Remix,” Lessig uses youth’s
reuse practices to support an argument against what he considers excessive
copyright legal protection [12].

Figure 1: An example Scratch program written using programming “blocks”.

Yet, despite a wide interest in remixing and authorship, researchers have only
recently engaged in empirical research on the subject [4]. Several recent
treatments have presented studies of video remixing communities [5, 24], music
remixing communities [4], collaborative video game communities [13] and social
network sites [18]. There is also another quantitative study of our empirical
setting [8] focused on characterizing the variety of responses to remixing.
These studies have tended to be general and largely descriptive examinations
of remixing practice. This work has pointed to the existence of norms [5] and
the territoriality of digital creators [26] and has considered issues of
motivation [4]. However, empirical work has yet to unpack in detail the key
social mechanisms that scholars have suggested drive behavior, norms, and
motivation in remixing communities.

Perhaps no mechanism has been more frequently cited as critical for remixing
activity than attribution and the related phenomena of plagiarism, reputation,
and status. For example, recent survey-based work has suggested that the
“authenticity and legitimacy” of creative work “are premised on the explicit
acknowledgment of the source materials or ‘original creator”’ and that such
acknowledgment is a key component of how adults assess the fairness or ethical
nature of content reuse [25]. Attribution, in this sense, can be seen as an
important way that people distinguish remixing from “theft.”

Judge and law professor Richard Posner stresses the importance of attribution
and explains that this is important even when there is no monetary benefit to
being attributed. For example, he explains that European copyright law is
based on a doctrine of “moral rights” that “entitles a writer or other artist
to be credited for his original work and this ‘attribution right’, as it is
called, would give him a legal claim against a plagiarist.” Posner also
explains that “acknowledgment” of another’s contributions to a derivative
negates any charge of plagiarism, although it may not establish originality
[20]. Attribution plays such an important role in remix culture that Creative
Commons made a requirement for attribution a component of all their licenses
after more than 97% of licensors opted to require attribution when it was
offered as a choice [2].

Young people’s perceptions of attribution and complications around copying
have also been examined. An article by Friedman describes that adolescents who
allowed “computer pirating” – the unauthorized copying of computer programs –
did so because technological affordances made it difficult for adolescents to
identify “harmful or unjust consequences of computer-mediated actions” [7]. In
a second study, psychologists Olson and Shaw have found that by five years
old, “children understand that others have ideas and dislike the copying of
these ideas” [16].

Yet, despite the fact that researchers in human computer interaction have
begun to explore the complexity of attribution and cited its importance to
remixing [13], many designers of online communities pay little attention to
issues of attribution in their designs – a fact that is reflected in user
behavior. For example, research on the use of photos from the photo sharing
site Flickr [22], as well as a number of other user-generated content
communities [23], suggests that most re-users fail to attribute re-used
content in ways that public-use licenses require. Although theory and survey
based work points to a need to design for attribution in user-generated
content communities, we still know very little about how attribution works or
how designers might go about doing so. Indeed, our study suggests that the
most obvious efforts to design for attribution are likely to be ineffective.

In this paper, we employ a mixed methods approach that combines qualitative
and quantitative analyses to explore users’ reactions to attribution and its
absence in a large remixing community. First, we introduce our empirical
setting; using qualitative data from users forums and comments, we present a
rich description of remixing and evidence to support our core proposition that
credit plays a central role in remixing in our environment. Second, we
contextualize and describe a technological intervention in our setting,
responding directly to several user suggestions, that automated the
attribution of creators of antecedent projects when content was remixed.
Third, we present a tentative quantitative analysis of the effect of this
intervention along with a parallel analysis of the practice of manual credit-
giving. We find that credit-giving, done manually, is associated with more
positive reactions but that automatic attribution by the system is not
associated with a similar effect. Fourth, we present analysis of a set of in-
depth interviews with twelve users which helps confirm, and add nuance and
depth to, our quantitative findings.

Our results suggest that young users see an important, if currently under-
appreciated and under-theorized, difference between _credit_ and
_attribution_. Credit represents more than a public reference to an “upstream”
user’s contributions. Coming from another human, credit can involve an
explicit acknowledgment, an expression of gratitude, and an expression of
deference, in a way that simple attribution can not. Our results suggest that
identical attribution information means something very different to users when
it comes from a computer, and when it comes from a human – and that users
often feel that acknowledgment is worth much less when it comes from a system.
We conclude that designers should create affordances that make it easier for
users to credit each other, rather than to merely pursue automated means of
acknowledgment.

Our study offers two distinct contributions for social scientists and for
technology designers. The first is an improved understanding of the way that
attribution and credit work in user-generated content communities. The second
is a broader contribution to the literature on design that suggests an
important limitation to technologists’ ability to support community norms and
a suggestion for how designers might create affordances. Functionality that
allows users to express information that a system might otherwise show
automatically may play an important role in successful design for social media
environments.

##  2 Scratch: A Community of Young Remixers

The Scratch online community is a free and publicly available website where
young people share their own video games, animated stories, interactive art,
and simulations [15]. Participants use the Scratch programming environment
[21], a desktop application, to create these interactive projects by putting
together images, music and sounds with programming command blocks (See Figure
1).

The Scratch website was officially announced in 2007 and, as of September
2010, has more than 600,000 user accounts who have shared 1.3 million
projects. At the time of writing, Scratch users share on average one new
project per minute. Examples of projects range from an interactive virtual
cake maker, to a simulation of an operating system, to a Pokemon-inspired
video game, to an animation about climate change, to tutorials on how to draw
cartoons. Like other user-generated content websites, such as YouTube or
Flickr, Scratch projects are displayed on a webpage (See Figure 2) where
people can interact with them, read metadata and give feedback. Visitors can
use their mouse and/or keyboard to control a video game or other type of
interactive projects or simply observe an animation play out in a web browser.
Metadata displayed next to projects includes a text-based description of the
project, the creator’s name, the number of views, downloads, “love its,”
remixes, and galleries (i.e., sets of projects) that the project belongs to.
Users can interact with projects by giving feedback in the form of tags,
comments, or clicks on the “love it“ button, and can flag a project as
“inappropriate” for review by site administrators.

Figure 2: Screenshot of a Scratch remix project highlighting automatic (the
area inside the bottom left circle) and manual (the area in the top right
circle) attribution.

Participants’ self-reported ages range primarily from 8 to 17 years-old with
12 being the median. Thirty-six percent of users self-report as female. A
large minority of users are from the United States (41%) while other countries
prominently represented include the United Kingdom, Thailand, Australia,
Canada, Brazil, South Korea, Taiwan, Colombia and Mexico. About 28% of all
users – more than 170,000 – have uploaded at least one project.

###  2.1 Remixing in Scratch

Scratch users can download any project shared on the website, open it up in
the Scratch authoring environment, learn how it was made, and “remix” it. In
Scratch, the term “remixing” refers to the creation of any new version of a
Scratch program by adding, removing or changing the programming blocks, images
or sounds. In this section we use qualitative data from the Scratch website to
provide social context for remixing and to suggest that credit plays an
important role in how users conceive of appropriate remixing practice.

Remixing in Scratch is not only technically possible, it is something that the
administrators of the website encourage and try to foster as a way for people
to learn from others and collaborate. On every project page, the Scratch
website displays a hyperlink with the text “Some rights reserved” that points
to a child-friendly interpretation of the Creative Commons Attribution-Share
Alike license under which all Scratch projects are licensed.111A copy of the
current version of the kid-friendly license is available online at
http://scratch.mit.edu/pages/license. The version available today encourages
users to give credit manually in the project notes. A strong emphasis on
credit-giving was added as a result of the findings reported here but was
absent during the period of data collection for this study. Even the name
Scratch is a reference to hip hop DJs’ practice of mixing records. A large
portion of all projects shared on the Scratch website (28%) are remixes of
other projects.

That said, remixing is not universally unproblematic in Scratch. Previous
quantitative analysis of the the Scratch community showed that Scratch
participants react both positively and negatively to the remixing of their
projects and found that of those users who viewed a remix of their project,
about one-fifth left positive comments while the same proportion of users
accused the remixer of plagiarism [8]. This ambivalent reaction to remixing is
echoed, and given additional texture, in the comments and complaints left by
users on the Scratch website and sent to Scratch administrators.

For example, even before the Scratch website was publicly announced, a number
of early adopters became upset when they found remixes of their projects on
the website. Indeed, one of the very first complaints about Scratch occurred
on the discussion forums where a 13 year-old boy asked:

> Is it allowed if someone uses your game, changes the theme, then calls it
> ‘their creation’? Because I created a game called “Paddling 1.5” and a few
> days later, a user called “julie” redid the background, and called it ‘her
> creation’ and I am really annoyed with her for taking credit for MY
> project!!222All usernames and quotes from the website were changed to
> disguise the identities of participants.

A similar complaint was sent to the website administrators by a 14-year old
boy:

> I think there should be a way to report plagiarized projects I’ve been
> seeing a lot of people’s projects taken and renamed. This member, named
> kings651, has 44 projects, and most of them are made by other people. He
> even has one that I saw my friend make so I know he actually made it.

In other cases, the disagreements over remixing were more public and involved
communication via projects and comments. For example, user koolkid15 wrote the
following message in a comment which was left is response to a remix that
shows a cat frowning:

> Hi i’m koolkid15 the original creator of luigi disco jayman41 copied me!!
> and didn’t even aknowladge me he didn’t change anything !! I wrote or drew!!
> and jayman…if your reading this think about other people!!!!

Despite the fact that Scratch was conceived, designed, and launched as a
platform for remixing, these users expressed their displeasure at remixing.
That said, none of these users complained directly about the reuse of their
project in general, but in terms of unfair “taking credit”, plagiarism, and a
lack of acknowledgment. Remixing was seen as problematic for koolkid15, for
example, because of the non-transformative nature of reuse, the lack of
acknowledgment of antecedent contributors, and the confusion about credit that
would result.

Of course, other, more positive, scenarios around remixing also played out in
Scratch. For example, jellogaliboo created a remix of Catham’s project and
wrote the following in the project notes: “i kinda copied Catham’s
”jetpackcat” game. i used the kitty, the blocks (i added and changed some),
and the fuel thingy.” Catham later posted his approval of the remix saying, “I
like what you changed about my project!” Like this example, many of these
positive experiences involved explicit credit-giving by a remixer to the
creator of the antecedent project.

##  3 Design Intervention: Automating Attribution

Several user complaints about remixing and plagiarism also included
suggestions for how Scratch’s designers might address them. For example, in
response to the forum thread mentioned in the previous section, a 16 year-old
proposed two potential design-based solutions:

> Make it so you can only download a view of how your game/story/animation
> works. Or make it so downloadable Scratch files have read only protection.
> Maybe downloaded Scratch files, after being uploaded, are marked with the
> creators name at the bottom, and then any DIFFERENT people who edit it after
> are put on the list.

Influenced by these comments, Scratch administrators came to believe that
negative responses towards remixing were often due to the fact that Scratch
users did not acknowledge the sources of their remixes. As a result, these
administrators implemented an architectural design change to the Scratch
community along the lines suggested by the user in the second half of the
quotation above.

The design change in question involved the introduction of a new technological
facility that automatically identified and labeled remixes and inserted
hyperlink pointers under each remix to the remix’s antecedent and the
antecedent’s author (see Figure 3). Two days after the introduction of this
feature, functionality was added to link to a comprehensive list of derivative
works from the pages of antecedent projects (see Figure 4).

Figure 3: Example of the automatic attribution-giving statement displayed
under remixes in the period after the design intervention. Figure 4: Example
of the link pointing to remixes of a project displayed under antecedent
projects in the period after the design intervention.

The new feature was announced in the discussion forums by an administrator of
the website and user responses were positive. User terminator99 suggested that
the change was, “Awesome.” Another user, marsUp, posted a comment saying,
“That’s a very useful feature! I like that we can do ping-pong like modding in
Scratch.” Users who did not visit the discussion forums also responded well to
the the new feature. For example, user greekPlus posted a comment on a remix
he created saying, “i remixed it for you but i do not know how to ad credit to
you for thinking of it in the first place.” A few minutes later he realized
that the remix automatically displayed the attribution and posted the a
comment saying, “never mind it did it for me. cool!”

##  4 Study 1: Human and Machine Attribution

Although initial user feedback to the automatic attribution feature was
positive, users continued to complain about remixing. In Study 1a, we present
a quantitative analysis to more fully evaluate the effect of the technological
design change described in the previous section. In Study 1b, we offer a
parallel analysis of the relationship between manual crediting-giving by users
and users’ reactions to being remixed.

Both studies build on a dataset used in previous work by Hill, Monroy-
Hernández, and Olson [8]. This dataset includes remix-pairs determined by an
algorithm using detailed project metadata tracked by the Scratch online
community. The dataset is limited in that it does not include projects whose
concepts were copied by a user who had seen another’s work but who did not
actually copy code, graphics or sound. Similarly, the dataset contains no
measure of the “originality” of projects or an indicator based on ideas that
were taken from a source outside Scratch (e.g., a user may have created a
Pacman clone which would not be considered a remix in our analysis).

The data presented here includes each coded reactions of the author of
antecedent projects (i.e., originators) on remixes of their projects shared by
other users in the site during a twelve week period after Scratch’s launch
from May 15 through October 28, 2007. Although 2,543 remixes were shared in
this period, we limit our analysis to the 932 projects (37% of the total) that
had been viewed at the time of data collection by the project originator – a
necessary prerequisite to any response. Of these 932 remixes that were viewed
by a project originator, 388 originators (42%) left comments on the remixes in
question. The remaining were coded as “silence.” Comments left by originators
were coded by two coders, blind to the hypotheses of the study and who were
found to be reliable [8], as being positive, neutral, or negative. They were
also coded as containing accusations of plagiarism (projects in which the the
originator directly accused the remixer of copying, e.g., “Hello mr
plagiarist”, “Copy-cat!”) or hinting plagiarism (projects in which the
originator implied that the remixer had copied but did not state this
explicitly, e.g., “I mostly pretty much made this whole entire game”).

Unless it also contained an explicitly negative reaction, an accusation of
plagiarism was not coded as “negative.” However, because plagiarism tends to
be viewed as negative within Scratch (as suggested by the quotations in the
previous section) and more broadly in society [20], we re-coded accusations of
plagiarism (both direct and hinting) as “negative” except, as was the case in
several comments coded as “hinting plagiarism,” when these accusations were in
comments that were also coded as positive. Previous published work using this
dataset, and subsequent robustness checks, show that our results are
substantively unchanged if we exclude these explicit charges of plagiarism
from the “negative” category or exclude only the weaker “hinting plagiarism”
accusations.

###  4.1 Study 1a: Automatic Attribution

Figure 5: Mosaic plot showing the distribution of reactions of original
creators who had viewed remixes of their project during the six week periods
before and after the introduction of automatic attribution. The proportion of
response types is shown along the y-axis. The proportion of projects viewed
with, and without, automatic attribution is shown along the x-axis. ($n=932$)

To test the effectiveness of automatic attribution, we consider the effect of
the design intervention described in the previous section. The design change
took place six weeks after the public launch of the Scratch community and at
the precise midpoint in our data collection window. The intervention affected
all projects hosted on the Scratch online community including projects shared
before the automatic attribution functionality was activated. As a result, we
classify originators’ reactions as occurring outside a technological regime of
automatic attribution when a project was both uploaded and viewed by a
project’s originator before automatic attribution functionality was activated.

A comparison of the distribution of coded comments between positive, neutral,
negative, and silent in the periods before and after the intervention suggests
that the introduction of automatic attribution had little effect on the
distribution of reaction types (See Figure 5). Although the period after the
intervention saw a larger proportion of users remaining silent and a smaller
proportion of both positive and negative comments, $\chi^{2}$ tests suggest
that there is no statistically significant difference in originator reactions
between remixes viewed before or after the introduction of automatic
attribution ($\chi^{2}=3.94;df=3;p=0.27$). As a result, we cannot conclude
that the there is any relationship between the presence, or absence, of an
automatic attribution system in Scratch and the distribution of different
types of reactions.

These results suggest that automatic attribution systems may have limited
effectiveness in communities like Scratch. Of course, our analysis is not
without important limitations. For example, the existence of an automatic
attribution regime may also affect the behavior of users preparing remixes. A
remixer might avoid making perfect copies of projects if they know that their
copies will be attributed and are more likely to be discovered.

###  4.2 Study 1b: Manual Crediting

While the introduction of an automatic attribution feature to Scratch appears
to have had a limited effect on originators responses to remixes of their
projects, the presence or absence of credit was a recurring theme in
discussions on Scratch online forums – as shown in the quotes in the previous
section – and in many of the coded reactions from the periods both before and
after the introduction of automatic attribution. Indeed, in project
descriptions or notes from the periods both before and after the change,
remixers frequently “manually” gave credit to the originators of their work.
Even after remixes were automatically attributed to originators, remixers who
did not also give credit manually – essentially producing information
redundant to what was already being displayed by the system – were criticized.

For example, after the introduction of automatic attribution functionality, a
user left the following comment on a remix of their project:

> Bryan, you need to give me Pumaboy credit for this wonderful game that I
> mostly pretty much kinda totally made this whole entire game … and that you
> need to give me some credit for it

For this user, automatic attribution by the system did not represent a
sufficient or valid form of credit-giving. In the following study, we test for
this effect of “manual” credit-giving by remixers on coded response types
using a method that parallels the analysis in Study 1a and that uses the same
dataset.

Manual crediting can happen in multiple ways. Exploratory coding of 133
randomly selected projects showed that 35 (36%) of each remix pair gave
credit. Of these 35 projects, 34 gave credit in the project description field
while 1 project only gave credit in a “credits” screen inside the game. As a
result, the authors of this study split the sample of projects used in the
Study 1a and coded each of of the user-created descriptions for the presence
or absence of explicit or manual credit-giving.

To first establish that we are examining distinct behaviors, we attempted to
establish that automatic and manual attribution do not act as substitutes for
each other. As suggested by our qualitative findings and our results in Study
1a, we found little difference in the rate of explicit credit giving between
projects created in the presence or absence of automatic attribution. Overall,
276 (about 30%) of the 932 projects in our sample offered explicit credit in
the description field of the project. Manual crediting-giving was a widespread
practice both before automatic attribution, when 31% of projects in our sample
offered explicit credit, and after, when 27% did so. The difference between
these two periods was not statistically significant
($\chi^{2}=1.41;df=1;p=0.24$). Previous work studying _Jumpcut_ , a video
remixing website, supports the idea that automatic and manual credit giving
are not interchangable phenomena. One Jumpcut user with permission to creative
derivative works commented that they, “still feel a moral obligation to people
as creators who have a moral right to be attributed (and notified) despite the
physical design which accomplishes this automatically” [5].

Figure 6: Mosaic plot showing the distribution of reactions of original
creators who had viewed remixes of their project and indicating whether they
left manual credit. The proportion of response types is shown along the
y-axis. The proportion of projects including and omitting manual credit is
shown along the x-axis. ($n=932$)

We measured effectiveness of manual credit giving using a parallel analysis to
Study 1a. As in Study 1a, we compared the distribution of originator reactions
in the presence, and absence, of manual credit-giving by remixers. We found
that negative reactions are less common in the presence of manual credit but
that this difference is very small (from 16% without manual credit to 14% with
it). However, we see that the proportion of users who react positively almost
doubles in the presence of credit-giving (from 16% with no crediting to 31% in
its presence). A graph of these results are shown in Figure 6. Tests show that
we can confidently reject the null hypothesis that these differences in the
distribution of reactions are due to random variation
($\chi^{2}=27.60;df=3;p<0.001$).

Also important to note is a difference in the number of users who are silent
after viewing a project (62% in the absence of manual credit versus 49% in its
presence). This larger proportion of commenting in general may have an
important substantive effect on the discourse and behavior on the site because
silent originators may, for obvious reasons, have a more limited effect on
attitudes toward remixing and user experience than vocal users do. As a
robustness check, we considered the reaction of only originators who left
comments ($n=388$) and found that even with a smaller sample, our result were
stronger. In the restricted sample, 41% reacted negatively when they were not
given credit. However, only 27% did so when they were credited. Similarly, 42%
of users who left comments on projects that did not give credit manually left
positive messages. Nearly two thirds of comments (61%) were positive when
credit was given. These differences, in the reduced sample that includes only
explicit reactions, were also statistically significantly different
($\chi^{2}=14.09;df=2;p<0.001$). We include the large number of silent
participants because we believe that non-response is an important type of
reaction with real effects on the community. Understanding the reasons behind
non-response and the effect of silence in response to different types of
credit giving remains an opportunity for further research.

Although not presented here due to limited space, we followed the general
model of previous work using this dataset [8] and tested logistic regression
models on dichotomous variables indicating the presence of negative and
positive reactions and found that basic relationships described above were
robust to the introduction of a control for the the intervention, to an
interaction between these two variables, and to controls for the gender and
age of originators and to the antecedent project’s complexity. Both before or
after the intervention, manual crediting resulted in more positive comments by
the originators of remixed projects. Of course, the results presented here are
uncontrolled, bivariate, relationships and we caution that these results,
while provocative, should still be viewed as largely tentative. As we show in
the subsequent qualitative analysis, attribution and credit-giving are complex
social processes. We do not claim that the preceding analyses capture it
fully.

##  5 Study 2: Interviews with participants

In order to explore the reasoning behind young people’s remixing behavior and
attitudes toward attribution as we observed it in Study 1, we engaged in a
second qualitative study and directly asked kids what role attribution and
credit plays in their moral evaluations of remixing.

Name (pseudonym) | Age | Gender |  Relationship to Scratch
---|---|---|---
Nicole | 10 | F |  She has created with hundreds of Scratch projects, mainly animations and art ones.
Kyle | 14 | M |  Casual user of Scratch, interested in math/science simulations and video games.
Amy | 15 | F |  Avid photographer, has never used Scratch.
Charles | 9 | M |  Active member of a subgroup of Scratch interested in simulation of operating systems.
Ryan | 12 | F |  Long-time member of the Scratch community. Creates complex video games.
Jon | 9 | M |  Casual user of Scratch, collaborates with Scratch friends in person.
Jake | 11 | M |  Casual user, likes making video games.
Cody | 16 | M |  Creates hip hop accessories, not active in Scratch.
Paul | 9 | M |  Creates Scratch projects with a focus on engineering and video games.
Jimena | 17 | F |  Highly technical teen with programming experience but no experience with Scratch.
Madeline | 14 | F |  Very popular animator in the Scratch community.
Susie | 10 | F |  Has created hundreds of projects including games, animations and art, but preferring art.
Table 1: Table listing details of interviewees used in Study 2. ($n=12$)

###  5.1 Methodology

We conducted twelve one-hour semi-structured interviews with kids aged 8 to 17
years old. All of the interviewees had experience using computers and had
access to the Internet at home. All the interviewees live in the United States
except for one who lives in New Zealand. The participants were recruited via
the Scratch website and during meet-ups with educators, teachers and young
Scratch users. Eight of the interviews were conducted in person, in the Boston
area, and the rest over the phone or voice over IP. The interviews were audio-
recorded and transcribed before fully analyzing them. Nine of the interviewees
were members of the Scratch community. The remaining three did not use Scratch
but were included as a way to check if people who do not use Scratch have
similar views about remixing, attribution, and credit. We found no substantive
difference between the Scratch users and non-users in their answers to
questions related to the hypothetical automatic and manual mechanism for
attribution.

Before each interview, subjects completed a survey that elicited demographic
information and posed questions about their familiarity with other
technologies and which was primarily designed to get a sense of the
interviewees’ social and technical background. Interviews were structured
around a protocol that included a set of nine fictional remixing cases
intended to elicit conversations about remixing.333Our interview protocol
including example cases is available at http://www.media.mit.edu/
andresmh/chi2011/interview.html. The cases were inspired by Sinnreich et al.’s
theoretical work and from three years of experience moderating the Scratch
community. They were designed to present cases where remixing could be
controversial but where there is no clear “correct” answer. The goal of the
cases was to offer a concrete, and common, set of dilemmas to stimulate broad
conversations about attitudes toward remixing.

The cases were presented in the form of printed screenshots of different
project pages from the Scratch website (anonymized to avoid referring to real
cases that users might have seen). The print outs were shown to the
interviewees (or discussed over the phone) while explaining each case. All the
cases included a remix and its corresponding antecedent project. The cases
varied in the presence of automatic attribution, manual credit, and the degree
of similarity between the remix and its antecedent. For example, the first
three cases were:

  1. 1.

A remix and its antecedent are identical. The project notes only describe how
to play the video game. The remix shows the automatic attribution but no
manual credit on the notes.

  2. 2.

A remix and its antecedent are different (as seen visually and in project
metadata) but one can clearly see the influence of its antecedent project. The
project notes of the remix show manual credit but no automatic attribution.
The interviewee was told to imagine the site had a glitch that prevented it
from connecting it to its antecedent.

  3. 3.

The same set of remix and antecedent projects as in (2) but this time
automatic attribution is displayed but manual credit is not.

Each of the interview logs was coded using inductive codes and grounded theory
[3]. The coded responses were analyzed based on categories related to how
interviewees answered specific questions about the distinction between
automatic attribution and manual credit.

###  5.2 Results

Confirming the results of Study 1, for users of Scratch, automatic attribution
was generally seen as insincere and insufficient. Throughout the interviews,
we found that for most of the kids, getting explicit credit from another
person was preferred over attribution given automatically by the system. When
asked why, kids often responded that knowing that another person had cared
enough to give credit was valued more than what the computer system would do
on its own. The fact that it takes some work, albeit minimal, to write an
acknowledgment statement, sends a signal of empathy, authenticity and good
intentions [6]. Amy articulated this when explaining why she preferred getting
credit from another person:

> I would like it even more if the person did it [gave credit] on their own
> accord, because it would mean that […] they weren’t trying to copy it,
> pirate it.

Similarly, Jon explained, “No [the “Based on” is not enough], because he [the
remixer] didn’t put that, it always says that.” For Jon, automatic attribution
is not authentic because it is always there and, as a result, it is clear that
is not coming from the person doing the remix.

Most of the interviewees seemed to have a clear notion of what they think a
moral remix should be. For some, it is all about making something different.
Jake for example, defines a “good” remix as, “if it has a bunch of differences
then it’s a good remix. If it has like two, then it’s bad.” In addition to the
differences between the remix and its antecedent project, for some, manual
credit is part of what makes it moral. Charles said, “[remixing] is taking
somebody else’s project and then changing a lot of it and sharing it and
giving credit.” Continuing, Charles explained:

> If Green had actually said in the project notes, “This is a remix of Red’s
> project, full credit goes to him,” then I would consider it a remix. But
> this [pointing at a remix without manual credit] is definitely a copy.

Likewise, Ryan mentions that a fictional remix was, “perfectly fine because
they gave credit in the project notes.”

Interviewees suggested that manual credit also allows users to be more
expressive. For example, Susie explained that expressiveness is the reason
that she prefers manual credit through the project notes saying, “I think the
manual one is better because you can say ‘thank you’ and things like that. The
automatic one just says ‘it’s based on.”’ Susie also notes that for her, the
project notes are a space where a creator can express her wishes in regards to
her intellectual property, independent, and even in contradiction to, the
license of the projects:

> If I do a project that has music that I really like, I often download the
> project, take the music. Unless it says in the project notes, “Do not take
> the music.”

For Susie and other users of Scratch, the project notes are a space for more
than just instructions on how to interact with one’s project; they are an
expressive space where one can communicate with an audience without having to
encumber the creative piece of work with it.

Others point at the fact that people do not pay as much attention to automatic
attribution statement as much they do to the manual credit left in project
descriptions. Jake, for example, explains that, while he agrees there is some
usefulness to having both, project notes still are more important, “because,
you know, sometimes people just like skim through a project and you don’t see
it ‘til the end.” Jake continued to say that creators that do not have both
should get a “warning.”

Even though interviewees value manual credit, they still see the usefulness of
the automatic mechanism as some sort of community-building prosthetic device –
an explanation for the positive reactions to the feature’s initial
introduction. For example, Nicole argues that while manual credit on the notes
has more value for her, the automatic attribution is useful as a backup and
because it provides a link:

> Well, I think that they should probably write in the notes that – then it
> should also say “Based on blank’s project,” just in case they forget, and
> also because it gives a link to the original project and it gives a link to
> the user so you don’t have to search for it.

A similar explanation was articulated on a comment exchange on one the
website’s galleries. A teenage girl that actively participates in Scratch
explained the pragmatic value of automatic attribution saying, “the ‘based on’
thingy, it gives a link, and we all luv links, less typing,” before
reiterating that manual credit is more valuable:

> at the beginning i thought that you don’t have to give credit when the
> “based on” thingy is in there, but i realized a lot of people don’t look at
> that, and i noticed people confused the remix with the original.

Creating a Scratch project is a complicated task. A project’s sources can be
diverse and the creator can easily forget to acknowledge some, as Paul
explains, when asked to choose between a system of manual credit or automatic
attribution:

> The thing is, it would be a lot better if they had both. Because, sometimes
> people probably just forget to do that. And then people would not know.

There are also situations where interviewees recognize what Posner calls the
“awkwardness of acknowledgment,” that is, situations where credit is not
really needed and it can be an unnecessary burden or go against the aesthetics
of the work [20]. For example, Paul mentioned that sometimes, there are some
projects in Scratch that are remixed so much – like the sample projects that
come with Scratch or some “remix chains”444Remix chains typically start with
someone sharing a project inviting others to remix (i.e. “add your animated
avatar to the park.”) – where credit is not necessary:

> There’s this one called “perfect platformer base” which a lot of people
> remix. So I don’t think that needs any credit. It’s not actually a real
> game. It’s all the levels and stuff are just demonstrations.

Since manual crediting has a higher emotional value, some kids mentioned that
conflicts over remixing could be addressed by the administrators of the site
by editing the project of the remix in question, as a way to enforce credit
without transforming it into attribution. Doing so would make it appear that a
remixer had credited an antecedent when they had not. Susie offers a
suggestion along these lines when asked about how the administrators of the
website should deal with a case of a complaint over a remix that is a parody
of someone else’s project. Susie suggested that, “I might remove the project
but I might not, you know, maybe I would edit the notes to to give credit.”
Similarly, Charles described his approach for solving conflicts if he was the
administrator of the website suggesting that, “I probably just would stay out
of the argument. I probably wouldn’t remove it [the remix], I’d just add
something in the project notes [like] ‘based on Gray’s project.”’

This phenomena of giving less value to technologically simplified social
signals is experienced in other social platforms. For example, Amy expressed
how on the social network site Facebook, she loves to get comments on her
photographs but dislikes those who do not leave comments or opt instead to
press the “I like it” button:

> I love when people comment on my pictures. Everybody sees them, because they
> tell me they have. I’m like, “Oh really? That’s great. Why didn’t you
> comment?” I don’t like it when people just “like it”, because you know they
> have something to say about it; they just don’t. It’s like, if they like it,
> then [they should] take the time to say something.

Although not designed to be a random sample, these interviews support the
proposition that both Scratch participants and other young people share a set
of norms about characteristics that determine what a “good” or moral remix is.
Among these norms, acknowledging one’s sources seems to play a central role.
However, participants also seem to share the opinion that this norm is not
satisfied through an automated process. They clearly understand the pragmatic
value of automating acknowledgment-giving, but they do not see it as a
substitute for adherence to the social norm of credit-giving. They also see it
as void of emotion and expressiveness. For Scratch users, normative
constraints are separate from architectural constraints and one cannot replace
the other. These findings support and enrich the results from our first study
and help us understand better how Scratch participants, and perhaps kids in
general, experience authorship norms and automation in online spaces.

##  6 Conclusions

Our results from Study 1a called into the question the effectiveness of
automatic attribution functionality in encouraging more positive user
reactions in Scratch. We build on these results in Study 1b to suggest that
manual crediting may do the work that Scratch’s designers had hoped automatic
attribution would. Results from the analysis of user interviews presented in
Study 2 help to answer the question of “why?” and suggest that users find
manual credit to be more authentic and more meaningful to users because it
takes more time and effort. Usually, UI improvements are designed to help
reduce the time and effort involved in using a system. But in trying to help
users by attributing automatically, Scratch’s designers misunderstood the way
that attribution as a social mechanism worked for Scratch’s users. Our
fundamental insight is that while both attribution and credit may be
important, they are distinct concepts and that credit is, socially, worth
more. A system can _attribute_ the work of a user but _credit_ , which is seen
as much more important by users and which has a greater effect on user
behavior, cannot be done automatically. Computers can attribute. Crediting,
however, takes a human.

As we suggested in our introduction, this fundamental result leads to two
distinct contributions. First, and more specifically, our analysis offers an
improved understanding of the way that attribution and credit works in user-
generated content communities over what has been available in previous work.
Our two studies suggest that scholars are correct to argue that credit plays
an important role in social media communities and offer empirical confirmation
for the important role that authenticity plays in how users conceptualize
credit. In our in-depth interviews, we explain some of the reasons why this
may be the case. Second, through our evaluation of an unsuccessful
technological design, our work offers a broader, if more preliminary,
contribution in suggesting an important limit of designers’ ability to support
community norms in social media systems. As the literature on design and
social media grows, the importance of good support for communities with
healthy norms promoting positive interactions is likely to increase. In
attempting to design for these norms, we suspect that researchers will
increasingly encounter similar challenges.

We argue that designers should approach interventions iteratively. This design
approach can be understood through the theoretical lens of the social
construction of technology [19]: designers can’t control technological
outcomes which must be built through a close relationship between designers
and users. Designers must move away from seeing their profession as providing
solutions. They must channel users, work closely with them, and iterate
together, to negotiate and achieve a set of shared goals.

The prevalence of user-generated content sites stresses the importance of how
online social spaces should deal with issues of attribution and our results
are likely to be immediately relevant to designers. For example, the Semantic
Clipboard is a tool built as a system of automatic attribution for content
reuse [22]. Developed by researchers who found a high degree of Creative
Commons license violations around the re-use of Flickr images, the tool is a
Firefox plugin that provides, “license awareness of Web media,” and enables
people to automatically, “copy [media] along with the appropriate license
metadata.” Our results suggest one way that this approach may fall short.

However, automatic attribution is not the only way that technologists can
design to acknowledge others’ contributions. Indeed, our results suggest that
there may be gains from design changes which encourage credit-giving without
simply automating attribution. For example, Scratch’s designers might present
users with a metadata field that prompts users to credit others and suggests
antecedent authors whose work the system has determined may have played a
role. This affordance might remind users to credit others, and might increase
the amount of crediting, while maintaining a human role in the process and the
extra effort that, our research has suggested, imbues manual credit giving
with its value. We suggest that in other social media communities, similar
affordances that help prompt or remind users to do things that a system might
do automatically represent a class of increasingly important design patterns
and a template for successful design interventions in support of community
norms.

##  7 Acknowledgments

This research was supported by Microsoft Research. Scratch is a project of the
Lifelong Kindergarten Group at the MIT Media Lab with financial support from
the National Science Foundation award ITR-0325828, Microsoft Corp., Intel
Foundation, Google, the MacArthur Foundation and the MIT Media Lab research
consortia.

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