# Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry
Embeddings

Deep Chatterjee†
Massachusetts Institute of Technology
Cambridge, MA, USA &Philip C. Harris∗
Institute for Artificial Intelligence and Fundamental Interactions (IAIFI)
Massachusetts Institute of Technology
Cambridge, MA, USA &Maanas Goel
University of Pennsylvania
Philadelphia, PA, USA
&Malina Desai
Massachusetts Institute of Technology
Cambridge, MA, USA
&Michael W. Coughlin
University of Minnesota
Minneapolis, MN, USA
&Erik Katsavounidis
Massachusetts Institute of Technology
Cambridge, MA, USA

###### Abstract

Likelihood-free inference is quickly emerging as a powerful tool to perform
fast/effective parameter estimation. We demonstrate a technique of optimizing
likelihood-free inference to make it even faster by marginalizing symmetries
in a physical problem. In this approach, physical symmetries, for example,
time-translation are learned using joint-embedding via self-supervised
learning with symmetry data augmentations. Subsequently, parameter inference
is performed using a normalizing flow where the embedding network is used to
summarize the data before conditioning the parameters. We present this
approach on two simple physical problems and we show faster convergence in a
smaller number of parameters compared to a normalizing flow that does not use
a pre-trained symmetry-informed representation.

##  1 Introduction

${}^{\dagger}$${}^{\dagger}$footnotetext<EMAIL_ADDRESS><EMAIL_ADDRESS>
Parameter estimation is fundamental to experimental science. Traditional
approaches involve stochastic sampling, like markov-chain monte carlo (MCMC)
or nested sampling, to obtain posterior probability densities and confidence
intervals on parameters. However, with the increase in number and flavors of
analyses, complexity of experiments, and the demand for near real-time
inference, stochastic sampling is turning out to be a challenge both in terms
of time and required compute. Likelihood-free, or simulation-based inference
(LFI/SBI) has emerged as a potential breakthrough in this regard (see Cranmer
et al. (2020); Papamakarios et al. (2021) for a review). Simulations to
generate data, $\mathbf{d}$, corresponding to physical parameters,
$\mathbf{\Theta}$, exists for many experiments. The idea of LFI is to learn
$p(\mathbf{\Theta},\mathbf{d})$, $p(\mathbf{\Theta}|\mathbf{d})$, or
$p(\mathbf{d}|\mathbf{\Theta})$ from the pairs
$\\{\mathbf{\Theta}_{i},\mathbf{d}_{i}\\}$ as desired (see Sec. 2.1 of Alsing
et al. (2019) for a nice description). In this work, we focus on the
construction of the posterior, $p(\mathbf{\Theta}|\mathbf{d})$.

A critical aspect of the application of LFI arises from the difficulty in
performing high dimensional fits. Often times, posterior extraction requires a
large amount of training samples, and large neural network to ensure a fully
accurate description of the model. As an example, current state of the art
networks for gravitational-wave (GW) parameter estimation require over 100
million parameters to ensure an accurate description of the GW parameters (Dax
et al., 2021). One potential way to mitigate the size of LFI models is to
project out parameters that are known to be invariant to the desired intrinsic
parameters. For example, in the case of pulse-like time-series, the time of
arrival may not be as important as the physical system/process that produces
the pulse. However, a conventional application of LFI, unaware of this
symmetry, considers data corresponding to time translated signals as _unique_
and requires that the arrival time be determined, or marginalized over, with
the other desired physical parameters. This necessitates a larger model and
expensive compute to train a model that learns to marginalize over time (or
some other variables, $\boldsymbol{\lambda}$), despite the fact that the
desired parameter inference is time-(or $\boldsymbol{\lambda}$-) invariant. If
the interest is limited to the physical, intrinsic parameters, this is
superfluous.

In this paper, we utilize self-supervision to project away the invariant
parameters so as to reduce the LFI to the extraction of only the intrinsic
parameters. Here, we marginalize over this symmetry by learning a _similarity_
embedding through self-supervised learning (SSL). In the instance of time
translation invariance we rely on SSL to build a reduced representation where
instances of data with different arrival times are represented as similar
values in an embedded space. We train a representation network by minimizing
the VICReg similarity loss (Bardes et al., 2022). A similar technique was used
recently to summarize cosmological data (Akhmetzhanova et al., 2023). SSL has
also been applied to learn symmetries in partial different equations (PDEs)
(Mialon et al., 2023). There are other techniques, not involving SSL to
summarize data for LFI on timeseries e.g. YuleNet (Rodrigues and Gramfort,
2020). Once we have constructed the SSL space, the subsequent LFI is
conditioned on the embedded space. We demonstrate this scheme considering two
simple physical systems – a damped harmonic oscillator (subsequently called
SHO) that is started at different times, and a sine-gaussian pulse
(subsequently called SG) that has an arbitrary time of arrival in a window. We
show that we are able to obtain posteriors which are accurate and require a
significantly smaller number of training parameters compared to the case when
the data is not represented, or similarity pre-training is not performed. We
point out that while _signals_ with identical physical parameters, translated
in time is an exact symmetry, the corresponding _data_ is not due to the
presence of noise. This motivates “similar” representation as opposed to
“same.”

##  2 Marginalizing Time-translation in Physical problems

Figure 1: Left: Example signal and data of a damped harmonic oscillator (SHO)
with representative values of $\mathbf{\Theta}=\\{\omega_{0},\beta\\}$. The
data is shown by the solid dots to represent sampling a time-series. Right: An
example of a sine-gaussian (SG) pulse with parameters,
$\mathbf{\Theta}=\\{f_{0},\tau\\}$. In either case, different times of
arrival, shown in faded curves, do not affect the inference of parameters.

Consider a SHO described by physical parameters,
$\mathbf{\Theta}=\\{\omega_{0},\beta\\}$, the natural frequency and damping
coefficient respectively. We would like to infer on the parameters given a
time-series, $\mathbf{d}$, with SHO amplitudes as shown in the left panel of
Fig. 1; white noise is additionally added. If the oscillator is started at a
different time, represented by the shifted solid lines in the figure, the
inference on $\omega_{0}$ and $\beta$ remains the same. The same applies in
the context of inferring properties of a SG pulse, represented in the right
panel of Fig. 1, where the parameters of interest are the central frequency,
$f_{0}$, and width, $\tau$. In the standard scheme of LFI, the parameters are
sampled from prior distribution, $\mathbf{\Theta}\sim p(\mathbf{\Theta})$,
following which the data, $\mathbf{d}$, is generated, and the model is
provided $\\{\mathbf{\Theta}_{i},\mathbf{d}_{i}\\}$ pairs. This, however,
leaves learning nuisance parameters, like time shifts, and marginalizing over
them, up to the model, which could be expensive in terms of trainable
parameters and compute.

###  2.1 Representing time-shifted data

Figure 2: SHO (left) and SG (right) data representations. The signals have
intrinsic parameters denoted by the delta function, but have augmented time
shifts sampled uniformly from a window. We observe that the SSL construction
leads to separation based on intrinsic parameters where the variations in the
time of arrival is reduced to a substantially smaller region that the
intrinsic parameters, approaching the time-project space.

We project the time shifted data instances into a 3D embedded space via a
neural network, $\mathbf{\Gamma}\equiv h\circ f$, such that the data at
different times, with the same parameters $\mathbf{\Theta}$, is mapped to
similar regions in this embedded space. We follow the scheme of self-
supervised learning as follows:

  * •

We consider two batches – in the first case the data, $\mathbf{d}$, has a
fixed reference time of arrival, in the second, the data,
$\mathbf{d}^{\prime}$, is augmented by shifting start time by a suitable time-
shift prior.

  * •

In our implementation, $f$ is a 1D convolutional resnet followed by a fully
connected contraction layer to represent each batch in 3 dimensions i.e.
$\mathbf{\gamma}\in\mathbb{R}^{3}$. The representation is then passed through
a fully-connected expander network, $h$, to output $x\in\mathbb{R}^{12}$ i.e.,

$\mathbf{\gamma}=f(\mathbf{d});\;\mathbf{\gamma}^{\prime}=f(\mathbf{d}^{\prime});\;x=h(\mathbf{\gamma});\;x^{\prime}=h(\mathbf{\gamma}^{\prime}).$
(1)

Ideally, the dimensionality of the representation is determined based on a
systematic hyper-parameter tuning. However, in this case, our choices
dimensions was based on performance from trials on a handful of cases.

  * •

We use the VICReg loss between the expanded outputs to minimize regularized
variance and covariance of the embedded batches individually, and the
invariance between the batches using mean-squared error. The net loss is a
weighted sum of the three terms,

$\displaystyle\mathcal{L}_{\mathrm{VICReg}}(x,x^{\prime})=\lambda_{1}\;\mathrm{MSE}(x,x^{\prime})+\lambda_{2}\;\left[\sqrt{\mathrm{Var}(x)+\epsilon}+\sqrt{\mathrm{Var}(x^{\prime})+\epsilon}\right]+$
$\displaystyle\hskip 142.26378pt\lambda_{3}\;\left[C(x)+C(x^{\prime})\right].$
(2)

Here $C$ is the squared sum of off-diagonal elements of the covariance matrix,
scaled by the number of dimensions (see Eq. 4 of Bardes et al. (2022)), and
$\lambda_{1,2,3}$ are the relative weighting of the three terms that can be
adjusted during training. For this work we found that increasing the
reconstruction and variance weights compared to the covariance term in the
initial epochs, followed by setting the weights equal helped with the
convergence.

  * •

Once trained, data from same physical parameters are represented as clusters
embedded in this space irrespective of the time of arrival. This is shown in
Fig. 2, where different physical parameters are represented as roughly
separated clusters irrespective of their time of arrival. We freeze part of
the network $f$ at this point.aaaHere, we freeze the convolutional layers of
$f$, and left the fully connected layers free to train in the next step.

##  3 LFI with Normalizing Flows

Figure 3: Top: We show consistency between posteriors from a NF with a pre-
trained $\mathbf{\Gamma}$, a baseline NF without any data summary, and results
from nested sampling. The $\pm$ uncertainties in the individual panels are
quoted based on the nested sampling result. Bottom: Loss curves (black dashed
lines for validation) for the different cases. The NF with a pre-trained
$\mathbf{\Gamma}$ converges in a smaller number of trainable parameters, and
sometimes more rapidly, compared to other cases.

Figure 4: Left: Results from 1000 tests data instances for which posteriors
where drawn. It plots fraction of times the true parameters of the SHO model
lie in credible interval. In each case 3000 posterior samples are drawn from
the trained model. The gray bands denote the 1-, 2-, 3-$\sigma$ confidence
intervals in decreasing order of opacity. Right: Same plot as the left for the
SG model and testing set.

Next, we perform the conventional LFI by training a normalizing flow (NF)
(Rezende and Mohamed, 2016). We use a base 2D standard normal distribution
with affine autoregressive transforms i.e. a masked autoregressive flow (MAF)
Papamakarios et al. (2018).bbb We use the implementation from nflows library
with minor changes. We condition the normalizing flow with the representation
network, $f$. Hence, the pairs provided during training are
$\\{\mathbf{\Theta}_{i},f(\mathbf{d}_{i})\\}\equiv\\{\mathbf{\Theta}_{i},\boldsymbol{\gamma}_{i}\\}$.
The network is trained by maximizing the log-likelihood. In Fig. 3 we show an
example of posterior samples from testing data for both SHO and SG models. We
compare the same with posteriors samples obtained by training a baseline NF
which does not have any such representation network, and also with results
from nested sampling.cccWe use the bilby inference library with dynesty
sampler. We compare the widths of the posterior obtained using our trained
model and stochastic sampling and show that they are consistent. We also
compute the Cramer-Rao bound on the parameter uncertainty by expanding the
likelihood to quadratic order in the argument of the exponential. We show that
the results obtained from the trained flow are consistent with CRB for the SG
and SHO models (see Appendix A.1). In Fig. 4 we show the result of 1000 test
data instances for which posteriors were drawn from the trained model. The
plot highlights the consistency between the confidence intervals and the
fraction of times the true parameters lie inside the confidence intervals. The
diagonal trend in the figure, along with the consistency of individual
posteriors mentioned in Appendix A.1 show consistency across the entire
testing dataset. The gray bands on both panels denote the 1-, 2-, 3-$\sigma$
confidence intervals.

Comparing number of trainable parameters – the baseline NF model, without any
data summary, has $\mathcal{O}(10^{6})$ while the same for the NF with pre-
trained $\mathbf{\Gamma}$ is significantly lower i.e. $\mathcal{O}(10^{5})$.
For a batch size of $10^{3}$, a forward pass involves $\sim 1.1\times 10^{8}$
multiply–accumulate operations (MACs) for the NF with the representation,
while $\sim 4.1\times 10^{8}$ MACs for the baseline NF. This may translate to
longer training times, for example, timing 100 epochs a NVIDIA GeForce GTX
1080 Ti GPU with SGD optimizer takes $\sim 20$ minutes for the NF with
representation while $\sim 80$ minutes for the baseline NF. We found 100
epochs of the similarity pre-training, which takes $\sim 10$ minutes, was
sufficient to reduce the individual loss terms in Eq. (2). We found that
freezing all trainable parameters of representation network parameters at this
stage led to slightly larger posterior widths compared to nested sampling.
However, letting the network further train with the flow, at least leaving the
fully connected layers of the representation network unfrozen, led to similar
performance as nested sampling. Therefore, the prescription presented here
generally implies smaller networks and more efficient memory and compute
requirements.

##  4 Application in Physics and Astronomy

While in this work, we have considered projecting out the time of arrival as a
specific example of a symmetry that does not impact the measurement of other
parameters, it should be noted that the technique presented here could be used
in general to marginalize over parameters that are not of interest. Many
problems physics and astronomy exhibit a set of _intrinsic_ vs. _extrinsic_
parameters. For example, in GW data analysis, the intrinsic parameters include
the masses and spins of merging binary black holes, while extrinsic parameters
involve distance, inclination, sky-location etc. Likewise, a kilonova
lightcurve is intrinsically characterized by the ejecta mass and lanthanide
fraction etc., and extrinsically by redshift, extinction, time of arrival etc.
If the interest is only limited to a subset of parameters, or to only
intrinsic properties, a representation using self-supervised learning can be
used for efficient LFI.

This work is supported by NSF HDR Institute Grant PHYS-2117997, “Accelerated
AI Algorithms for Data-Driven Discovery.”

##  Appendix A Appendix

###  A.1 Analytic approximation of the posterior

The signal models are,

$\displaystyle y_{i}\equiv
y(t_{i})=\begin{cases}\exp\left(-\beta\omega_{0}t_{i}\right)\;\cos\left(\omega_{0}t_{i}\sqrt{1-\beta^{2}}\right)&\text{for
SHO}\\\ \exp\left(-\frac{t_{i}^{2}}{\tau^{2}}\right)\;\sin(2\pi
f_{0}t_{i}),&\text{for SG}.\end{cases}$ (3)

Assuming white noise drawn from $\mathcal{N}(0,\sigma)$, (with $\sigma$ known)
the likelihood is,

$\displaystyle\mathcal{L}\propto\exp\left[-\frac{1}{2}\sum_{i}\left(\frac{y_{i}-\hat{y}_{i}}{\sigma}\right)^{2}\right],$
(4)

where $\hat{y}$ is the truth i.e. signal corresponding to true parameters
$\\{\hat{\omega}_{0},\hat{\beta}\\}$ ($\\{\hat{f}_{0},\hat{\tau}\\}$) for SHO
(SG). Expanding the argument of the exponential to quadratic order in
$\\{{\omega_{0}},\beta\\}$ (or $\\{{f}_{0},{\tau}\\}$) around the true value,
the likelihood is approximated by gaussian with widths,

$\displaystyle\Delta\omega_{0}$
$\displaystyle\approx\frac{\sigma}{\sqrt{\sum_{i}^{N}t_{i}^{2}e^{-2\hat{\beta}\hat{\omega}_{0}t_{i}}\left[\sqrt{1-\hat{\beta}^{2}}\sin\left(\hat{\omega}_{0}t_{i}\sqrt{1-\hat{\beta}^{2}}\right)+\hat{\beta}\cos\left(\hat{\omega}_{0}t_{i}\sqrt{1-\hat{\beta}^{2}}\right)\right]^{2}}},$
(5a) $\displaystyle\Delta\beta$
$\displaystyle\approx\frac{\sigma}{\sqrt{\sum_{i}^{N}\hat{\omega}_{0}^{2}t_{i}^{2}e^{-2\hat{\beta}\hat{\omega}_{0}t_{i}}\left[\frac{\hat{\beta}\sin\left(\hat{\omega}_{0}t_{i}\sqrt{1-\hat{\beta}^{2}}\right)}{\sqrt{1-\hat{\beta}^{2}}}-\cos\left(\hat{\omega}_{0}t_{i}\sqrt{1-\hat{\beta}^{2}}\right)\right]^{2}}},$
(5b) $\displaystyle\Delta\tau$
$\displaystyle\approx\frac{\sigma\hat{\tau}^{3}}{\sqrt{\sum_{i}^{N}4\;t_{i}^{4}\exp(-2t^{2}_{i}/\hat{\tau}^{2})\sin^{2}(2\pi\hat{f}_{0}t_{i})}},$
(5c) $\displaystyle\Delta f$
$\displaystyle\approx\frac{\sigma}{\sqrt{\sum_{i}^{N}4\pi^{2}\;t_{i}^{2}\exp(-2t^{2}_{i}/\hat{\tau}^{2})\cos^{2}(2\pi\hat{f}_{0}t_{i})}}.$
(5d)

By assuming flat priors, the posterior is proportional to the likelihood,
having widths approximately given by Eqs. (5). For the examples in Fig. 3, the
value of $\sigma=0.4$ in either case. The true SG parameters are
$\\{\hat{f}_{0}=0.7,\hat{\tau}=0.3\\}$; for SHO they are
$\\{\hat{\omega}_{0}=1.5,\hat{\beta}=0.2\\}$. From Eqs. (5), we get $\Delta\
f_{0}\approx 0.006\;\Delta\tau\approx 0.2$ for SG,and $\Delta\omega_{0}\approx
0.04\;\Delta\beta\approx 0.03$ for SHO models, satisfying Cramer-Rao bound for
the one-sigma widths shown in Fig. 3.

###  A.2 Code Availability

The codes used in the analysis, including trained weights to reproduce figures
in the draft, are provided publicly and tagged. Online rendering of notebooks
are available here: https://github.com/ML4GW/summer-
projects-2023/tree/neurips-2023/symmetry-informed-flows.

## References

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  * Bardes et al. [2022] A. Bardes, J. Ponce, and Y. LeCun.  Vicreg: Variance-invariance-covariance regularization for self-supervised learning, 2022.
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# Impact of Contextual Factors on Snapchat
Public Sharing

Hana Habib  Carnegie Mellon University<EMAIL_ADDRESS>,  Neil Shah  Snap Inc.
<EMAIL_ADDRESS>and  Rajan Vaish  Snap Inc<EMAIL_ADDRESS>
(2019)

###### Abstract.

Public sharing is integral to online platforms. This includes the popular
multimedia messaging application Snapchat, on which public sharing is
relatively new and unexplored in prior research. In mobile-first applications,
sharing contexts are dynamic. However, it is unclear how context impacts
users’ sharing decisions. As platforms increasingly rely on user-generated
content, it is important to also broadly understand user motivations and
considerations in public sharing. We explored these aspects of content sharing
through a survey of 1,515 Snapchat users. Our results indicate that users
primarily have intrinsic motivations for publicly sharing Snaps, such as to
share an experience with the world, but also have considerations related to
audience and sensitivity of content. Additionally, we found that Snaps shared
publicly were contextually different from those privately shared. Our findings
suggest that content sharing systems can be designed to support sharing
motivations, yet also be sensitive to private contexts.

††copyright: acmlicensed††doi: 10.1145/3290605.3300256††isbn:
978-1-4503-5970-2/19/05††conference: CHI Conference on Human Factors in
Computing Systems Proceedings; May 4–9, 2019; Glasgow, Scotland UK††booktitle:
CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019),
May 4–9, 2019, Glasgow, Scotland UK††journalyear: 2019††price: 15.00††ccs:
Human-centered computing Social content sharing††ccs: Human-centered computing
User studies††ccs: Human-centered computing Social media

##  1\. Introduction

Online sociotechnical platforms, such as Facebook, Twitter, and Snapchat, are
driven by content that is created and shared by their users. This content
enables people to explore the world and learn from one another. User decisions
to publicly share content online are likely nuanced and determined by many
factors, including context. The rise of handheld devices enables people to
share content whenever and wherever. This means that their context, such as
where they are and what they are doing, is constantly changing. Prior research
has found that user motivations for sharing content on public platforms range
from wanting to share knowledge with others to desiring to earn rewards
(Gretzel, Ulrike and Yoo, Kyung Hyan and Purifoy, Melanie, 2007; Coleman et
al., 2009). However, people also have a wide range of concerns in sharing
content, especially in public settings. For example, sharing personal photos
and videos could reveal sensitive information, including where an individual
lives. Such concerns have been widely studied through sharing decisions on
social media websites (e.g., (Lampinen et al., 2009; Ahern et al., 2007;
Johnson et al., 2012)).

In this paper, we study Snapchat, a highly popular multimedia messaging
application which launched in 2011. The app first started as a means for users
to directly exchange photos, called Snaps, that disappear after they are
viewed by the recipient (Spiegel, 2017). Snapchat now has the _My Story_ and
_Our Story_ features that allow users to share ephemeral photos and videos
with a broader audience. While Snaps shared to _My Story_ can, by default,
only be accessed by a user’s Snapchat friends, those shared to _Our Story_ are
anonymous but viewable by anyone using Snapchat (Snapchat Support, 2018b, c).

In prior work, Snapchat has not been studied as extensively as other content
sharing platforms, such as Facebook and Twitter. As the landscape of content
sharing changes, it is important to continuously assess both user motivations
and considerations in sharing content, especially for unique ecosystems such
as Snapchat. In contrast to many other sharing platforms, content production
on Snapchat occurs solely via a handheld device, and thus the context in which
users share content is dynamic. Additionally, Snapchat is used primarily to
exchange multimedia content which may have a different level of privacy
sensitivity than text content. Lastly, with the default settings, Snapchat
users can choose between three audience types for each of their Snaps: one or
more Snapchat friends, all of their Snapchat friends via _My Story_ , or the
entire Snapchat user base via _Our Story_. Recent work has found that both
ephemerality of content and audience control impact the perceived
appropriateness of content sharing (Rashidi et al., 2018). Recent work has
demonstrated that both the ephemerality of content and audience control can
impact a user’s decision to share. Thus user considerations in publicly
sharing to _Our Story_ may differ from those found by prior work which studied
other public sharing platforms (Ahern et al., 2007; Fiesler et al., 2017;
Sleeper et al., 2013).

To extend the literature on Snapchat, and social sharing behavior more
generally, we conducted an online survey of over 1,500 Snapchat users. Our
study explored two key research questions:

  1. (1)

What role does context play in sharing decisions?

  2. (2)

What are users’ motivations and considerations when publicly sharing content?

In our survey, we examined whether Snaps shared to _My Story_ differ
contextually from those shared to _Our Story_ , how context relates to
participants’ comfort levels with past and future sharing of public content,
and the reasons participants chose to share or not share to _Our Story_ in the
past. Specifically, we considered the role of four “primary context types”
defined by Dey et al.: identity, activity, location, and time (Dey and Abowd,
1999). As Figure 1 demonstrates, these four attributes can be used to capture
the context of any Snap.

Figure 1.  The context of this Snap can be captured by describing various
attributes of the user, the scene, the location, and the time the Snap was
taken. The Snap on the left is an example of a public context, while the Snap
on the right may be considered a more private context.

We found that, overall, each contextual factor we examined played a role in
public sharing behavior on Snapchat. With respect to motivations, participants
primarily stated intrinsic reasons for sharing to _Our Story_ , such as the
desire to share an experience with the world or have fun. Considerations in
public sharing included the audience and content of the Snap. Regarding
identity, those who identified as male or a racial minority were more likely
to have shared to _Our Story_ in the past. Additionally, we found that the
activity captured by Snaps shared to _My Story_ was significantly different
than those shared to _Our Story_. For example, proportionally four times as
many Snaps shared to _Our Story_ were taken while attending an event.
Similarly, users differentiated between public and private locations, as the
majority of _My Story_ Snaps were reported to be taken at home. Related to
time, participants reported that they would be more comfortable publicly
sharing Snaps to _Our Story_ that were taken in the afternoon or evening,
compared to morning or late night.

Overall, our study highlights that the contextual factors we examined
(identity, activity, location, and time) do play a role in public sharing
decisions on Snapchat. Our findings related to the impact of context lend to
implications for the design of content sharing platforms. System designs that
are contextually aware may increase users’ comfort with their sharing
decisions and be more respectful of their privacy.

##  2\. Background & Related Work

In this section, we present an overview of prior work that has studied sharing
considerations and decision making. We additionally highlight prior research
related to Snapchat use, as well as details about Snapchat’s _Our Story_ and
_My Story_ features, which were central to our survey.

###  2.1. Sharing Considerations & Decision Making

Prior research has studied public sharing of content on several online
platforms. Ahern et al. analyzed 36,000 photos on the photo-sharing mobile app
ZoneTag, and found that the decision to share a photo privately or publicly
was sensitive to the location at which a photo was taken. Additionally,
participants in their study expressed concerns about revealing granular
location details, further highlighting the privacy sensitivity related to the
location component of context. The study also found that photos of a person
were less likely to be shared publicly, compared to those of other content.
The authors identified four themes of “consideration” in participants’ sharing
decisions: personal security and privacy, presentation of one’s identity,
social disclosure, and convenience for the intended audience (Ahern et al.,
2007).

In a study comparing public and private content sharing, Fiesler et al.
conducted a regression analysis of 11,000 Facebook posts. The authors found
that demographics of the individual sharing the post, rather than the content
of the post, were predictive of a post being shared publicly. Most
participants in the study did not change privacy settings per post, and
instead had posts that were all private or all public (Fiesler et al., 2017).
As this study only focused on text content, our study differs by analyzing
whether similar patterns occur for photo and video content.

Prior work has also explored social media usage among teenagers, a major group
of Snapchat users (Anderson and Jiang, 2018). In an ethnographic study,
Marwick and boyd found that despite being active sharers, teens are concerned
about privacy in their online sharing. Generally, teens desire privacy from
their parents and other adults, and use social media as a space to be with
peers. Though many participants were found to follow a “public-by-default”
norm, some used privacy protective behaviors such as blocking others’ access
to certain personal content or separating social circles based on online
platforms (Marwick and boyd, 2014). Our study explores sharing considerations,
beyond privacy, of Snapchat users, who are primarily teenagers and young
adults (Spiegel, 2017).

Despite the privacy risks associated with revealing details about oneself on
user-contribution based platforms, people are motivated to share for a variety
of reasons. Primarily, contributors to platforms, such as TripAdvisor, Yelp,
and Google Maps, are driven by altruism and the desire to help others (Khanna,
Ayush, 2011; Gretzel, Ulrike and Yoo, Kyung Hyan and Purifoy, Melanie, 2007;
Ma and Chan, 2014; Coleman et al., 2009; Parikh et al., 2014). Other
motivations are related to intrinsic factors, such as having an extroverted
personality or seeking intellectual stimulation (Gretzel, Ulrike and Yoo,
Kyung Hyan and Purifoy, Melanie, 2007; Coleman et al., 2009). Extrinsic
motivations, especially when contribution provides an immediate reward, can
also foster user content contribution (Coleman et al., 2009). We examine
whether Snapchat users who decide to publicly share content report similar
motivations.

Across different disciplines, there is a large body of research that has
studied the role of context in different forms of decision making. For
example, Papadakis et al. explored how contextual factors related to an
organization impacts strategic decision making (Papadakis et al., 1998).
Additionally, Lamborn et al. studied the relationship between community
context, family decision making, and adolescent adjustment (Lamborn et al.,
1996). Building on Dey et al.’s definition of context (Dey and Abowd, 1999),
our work looks at the impact of context on user content sharing decisions.

###  2.2. Norms of Snapchat Use

At the time of Snap Inc.’s initial public offering (IPO) in Feburary 2017,
Snapchat had over 158 million users. Prior research has found that Snapchat is
predominantly used as a messaging platform for exchanging fun, everyday
content, such as selfies, that users do not necessarily want to archive
(Roesner et al., 2014; Piwek and Joinson, 2016; Utz et al., 2015; Bayer et
al., 2016; Xu et al., 2016). Others use the app for following interests, such
as sports teams (Billings et al., 2017). Prior work has reported that users on
Snapchat typically interact with a small set of close friends (Bayer et al.,
2016; Xu et al., 2016; Vaterlaus et al., 2016; Piwek and Joinson, 2016). As
such, numerous studies have observed that Snapchat users find the app
important for nurturing close friendships (Xu et al., 2016; Katz and Crocker,
2015; Vaterlaus et al., 2016; Piwek and Joinson, 2016).

Bayer et al. found that users differentiate their interactions on Snapchat
from those on other platforms, associating them with an increased trust in
their audience, ephemerality, and less self-curation of content due to the
lack of expected content archival (Bayer et al., 2016). This is in contrast to
norms on social media platforms with archived sharing and public feeds, which,
as noted by Uski and Lampinen, discourage over-sharing of everyday content,
yet expect users to maintain natural and authentic profiles, and lead to
active profile management (Uski and Lampinen, 2016).

Snapchat users have also developed norms related to usage of the app.
Participants in a study conducted by Katz and Crocker believed that Snaps of
normal, mundane activities are better suited for specific, personalized
recipients rather than a large group of people. Moreover, participants viewed
Snapchat as a game in which they must outdo their friends in their responses
to the Snaps they receive (Katz and Crocker, 2015).

Roesner et al. found that despite not using the app to send sensitive content,
their participants did report exercising privacy-protective behaviors. For
example, 47% of their participants reported to have adjusted the timer on a
Snap’s display time based on its content or recipient (Roesner et al., 2014).
As common on social media platforms (Sleeper et al., 2013), participants also
practiced self-censorship, reporting that they would not take a Snap of
sensitive material, such as offensive or illegal content (Roesner et al.,
2014).

Most prior work studying Snapchat has explored Snaps directly exchanged
between people or a group of people. Our study contributes an understanding of
the Snaps shared to _My Story_ and _Our Story_ , features through which users
share a Snap with a broader audience. We also analyze contextual factors that
may contribute to concerns related to public sharing on Snapchat.

###  2.3. Snapchat’s My Story & Our Story

In 2013, Snapchat introduced the _My Story_ feature which allows users to
share a series of Snaps taken throughout the day to all of their Snapchat
friends (Spiegel, 2017). In line with the app’s emphasis on ephemeral sharing,
Snaps shared to _My Story_ are only viewable for 24 hours (Snapchat Support,
2018b). The default setting for _My Story_ is a user’s Snapchat friends, but a
user can make it public to anyone on Snapchat, or viewable to just a custom
list of Snapchat friends. _Our Story_ is a curated selection of Snaps from a
location or event that are displayed through other features on the app such as
_Discover_ and _Snap Map_ , or on third-party platforms (Snapchat Support,
2018c). A first iteration of the _Our Story_ feature was introduced to
Snapchat in 2014. Snaps shared on _Our Story_ are anonymous by default, but
users can manually de-anonymize themselves by adding their Snapcode or
Snapchat username to the Snap. Additionally, Snaps shared to _Our Story_ are
publicly viewable, either through the Snapchat mobile application or via a web
interface. In contrast to other features on the app, Snaps shared to _Our
Story_ may be viewable for longer than 24 hours (Snapchat Support, 2018c).

Snapchat users have developed different usage patterns for Snaps they directly
share with other users and those they share to either _My Story_ or _Our
Story_. An interview study conducted by McRoberts et al. found that Snaps
shared to _My Story_ were used to craft narratives, and to share content that
was seen as out of the ordinary. Thus, selfies were largely seen as
inappropriate for _My Story_. Participants also reported that the feature
allowed them to create content quickly, and delay their decision to save it
(McRoberts et al., 2017). Juhász and Hochmair found that Snapchat users are
more likely to share Snaps to _Our Story_ that are taken during the weekend
and in highly trafficked areas, such as tourist hotspots or downtowns (Juhász
and Hochmair, 2018). In contrast, Snaps directly sent to another person or
group of people have been found to primarily be taken at home (Piwek and
Joinson, 2016).

Our study complements this prior work by providing additional detail about the
contexts in which Snapchat users share to _My Story_ and _Our Story_.
Furthermore, our study explores public versus private sharing on Snapchat by
comparing the Snaps our participants last shared to these features. We analyze
contextual factors, such as location and content, which may impact comfort
with public sharing on Snapchat.

##  3\. Survey

Our study consisted of a survey deployed online. Prior to data collection, our
study was reviewed by the legal and privacy engineering teams at Snap.

###  3.1. Context Description of Snaps

Relating to our research question about the role of context in sharing
behaviors, our survey inquired about different contextual factors surrounding
the Snaps participants shared in the past or could possibly share in the
future. We based our definition of context on four “primary context types”
identified by Dey et al. for the field of context-aware computing: identity,
activity, location, and time (Dey and Abowd, 1999). We incorporated these
factors into our survey as:

  * •

Identity – the Snapchat user’s identity, distilled as:

    1. (1)

demographics: age, gender, race, and education

    2. (2)

privacy sensitivity: measured by parts of the Internet Users’ Information
Privacy Concerns (IUIPC) scale (Malhotra et al., 2004)

    3. (3)

personality: measured by the Ten-Item Personality Measure (TIPI) (Gosling et
al., 2003)

  * •

Activity – the scene captured by the Snap, defined as:

    1. (1)

user status: what the user was doing when he or she shared to _Our Story_
(categorized as one of 15 common activities observed in _Our Story_ posts), or
another activity defined by the participant

    2. (2)

subject: the primary focus of the Snap as one of nine categories based off of
labels used to organize the ImageNet database,111ImageNet Database:
http://image-net.org/explore or another content label defined by the
participant

    3. (3)

camera: whether a Snap was taken with the smartphone’s front-facing (“selfie”)
or rear-facing camera

  * •

Location – where Snap was taken as categorized by the highest levels of the
Foursquare Venue Category Hierarchy222Foursquare Venue Category Hierarchy:
https://developer.foursquare.com/docs/resources/categories, or a location
defined by the participant

  * •

Time – the time of day the Snap was taken, as well as whether it was taken on
a weekday or weekend

As user motivations and considerations were likely to be nuanced, our survey
also included two open-ended questions about participants’ past sharing to
_Our Story_ and _My Story_.

###  3.2. Survey Structure

In this section, we provide an overview of the 56 survey questions contained
in the full survey and their display logic.

####  3.2.1. Screening Questions

We confirmed that respondents were actual Snapchat users by asking
participants whether they were aware of several Snapchat features (including a
non-existent feature, “Snapchat Plus”). They were then asked what color the
“send” button is after taking a Snap. Participants who indicated being aware
of the fabricated “Snapchat Plus” feature or selected the wrong color were not
allowed to continue to the full survey. Participants then answered whether
they had ever used the _Our Story_ and _My Story_ features. Those who
indicated that they had never used either feature were also not allowed to
continue, as we wished to study Snapchat users who do not only use the
application for directly exchanging Snaps with other users.

####  3.2.2. Snapchat Usage

After answering the screening questions, participants were asked how
frequently they used different Snapchat features in the past week. All
participants were also asked their Snapchat score, which serves as an
indicator of how active a user is (Snapchat Support, 2018a), and their
audience setting for _My Story_ , which can be set to the default “friends
only,” “public”, or a custom list of Snapchat friends that a user selects.

####  3.2.3. Our Story Understanding

To gauge participant understanding of how _Our Story_ works, we asked
participants who they thought could view the Snaps they posted to _Our Story_.
Additionally, we asked _Our Story_ users whether they had ever de-anonymized
their _Our Story_ Snaps by adding their Snapcode or Snapchat username, and if
so, why.

####  3.2.4. Past Sharing to Our Story

Previous _Our Story_ users were asked about the context (activity, location,
and time) of the last Snap they shared to _Our Story_ , and their comfort
level with their decision to share this Snap on a five-point Likert scale from
“very uncomfortable” to “very comfortable.” To better understand user
motivations for public sharing, we also asked a free-response question asking
why they chose to share this Snap to _Our Story_. Participants were then asked
whether they also shared this Snap to _My Story_.

####  3.2.5. Past Sharing to My Story

We then asked participants similar questions about the context of their last
Snap shared to _My Story_ but not to _Our Story_ , if the participant had
indicated previously using _My Story_. This allowed us to examine contextual
differences between Snaps shared to those features. To clarify any
misunderstandings about _Our Story_ , participants were provided a short
description based off of Snapchat’s support page for the feature (Snapchat
Support, 2018c). They were then asked to indicate their level of comfort on a
five-point Likert scale with potentially publicly sharing this Snap via _Our
Story_. We also asked participants to describe why they chose not to share
this Snap to _Our Story_ , and any concerns they may have had in publicly
sharing this Snap.

####  3.2.6. Potential Sharing to Our Story

Next, we showed all participants a description of _Our Story_ , since we
assumed not all participants would be aware of or familiar with the feature.
To better understand which contexts participants considered less suited for
public sharing, we asked them to provide their comfort level with publicly
sharing Snaps for different user statuses, subjects, locations, and times. For
each, participants indicated their comfort level on a five-point Likert scale
from “very uncomfortable” to “very comfortable.” Participants were then asked
their potential level of comfort with sharing to _Our Story_ if the Snaps
displayed their username or Snapcode.

####  3.2.7. Identity

Finally, participants responded to identity related questions. These included
demographic questions about age, gender, race, and education level.
Participants also answered ten questions related to control, awareness, and
collection of personal information from the Internet Users’ Information
Privacy Concerns (IUIPC) scale (Malhotra et al., 2004), and the Ten Item
Personality Measure (TIPI) (Gosling et al., 2003).

###  3.3. Survey Data Collection & Analysis

In total, we collected 1,515 valid responses to our online survey. Our
findings report on both statistical comparisons and thematic analysis of our
data.

####  3.3.1. Data Collection

We collected our survey data from an online panel of participants recruited
through Qualtrics,333Qualtrics: https://www.qualtrics.com/ at the rate of
$7.00 per participant. To participate in our survey, respondents were required
to be residents of the United States that were over the age of 13, and were
predetermined by Qualtrics to be Snapchat users. Participants were further
screened by our survey and were required to have previously used either the
_My Story_ or _Our Story_ feature on Snapchat. Responses from a total of 1,691
people were collected between July 3 and July 14, 2018. In our checks for data
quality, we excluded 144 responses that reported usage of _Our Story_
inconsistently (e.g., participants who reported that they had never used _Our
Story_ but had used it in the past week). An additional 32 participants were
excluded for providing open-ended responses that signaled that they were not
completing the survey with their full attention. As such, we analyzed
responses provided by 1,515 individuals.

####  3.3.2. Data Analysis

For our statistical comparisons, we used a significance level of
$\alpha=0.05$. We only report statistical results for which we observed at
least a small effect (associations greater than 0.10), which is a widely
recognized threshold for statistical reporting (Cohen, 1988). Effect sizes for
categorical comparisons are presented as Cramer’s $V$ or $\phi$ (for two-by-
two contingency tables). Both of these measures are reported on a scale from 0
to 1.

We collaboratively developed codebooks for systematically analyzing responses
to the two open-ended questions that directly asked about motivations and
concerns in sharing to _Our Story_. Two coders individually reviewed 20% of
all responses, achieving an inter-coder reliability of $\kappa=0.70$, and
resolved all conflicts in the coding. The remaining responses were then
single-coded. For the other, less nuanced open-ended questions, one researcher
read through the responses and extracted the most common themes.

##  4\. Participant Summary

The demographics of our survey sample reflects that of the Snapchat userbase,
as the gender balance leaned slightly female and those under 35 years old
comprised over 60% of the population (Spiegel, 2017). We analyzed the Snapchat
scores of participants who reported a number in the range 1 to 1,000,000, and
observed that the majority of our participants were moderately active in their
use of Snapchat. In general, our participants were somewhat privacy-sensitive,
averaging 5.41, 5.17, and 4.90 (out of 7.00) on the IUIPC measures for
control, awareness, and collection, respectively. Based off of participants’
self-reported values, both the mean and median of each TIPI personality
measure fell between 3.50 and 4.00 (neither agree nor disagree). Additional
demographic details of our survey population are presented in Table 1.

Gender | Age | Race | Education | Snapchat Score
---|---|---|---|---
Female | $56.90\%$ | 13-17 | $19.67\%$ | American Indian | $0.92\%$ |  $<$ High school | $4.62\%$ | Min | $1.00$
Male | $42.51\%$ | 18-20 | $14.39\%$ | Asian | $4.36\%$ | High school | $29.90\%$ | Max | $893,034.00$
Other | $0.53\%$ | 21-24 | $9.57\%$ | Black | $17.03\%$ | Some college | $27.72\%$ | Mean | $37,906.19$
No answer | $0.07\%$ | 25-34 | $16.96\%$ | Hispanic/Latino | $14.32\%$ | Associates | $8.71\%$ | First quartile | $1,801.75$
|  | $\geq 35$ | $6.86\%$ | Mixed | $2.24\%$ | Bachelors | $16.50\%$ | Median | $10,000.00$
|  | No answer | $32.54\%$ | Pacific Islander | $0.26\%$ | Professional | $7.52\%$ | Third quartile | $37,002.25$
|  |  |  | White | $60.20\%$ | Doctorate | $2.51\%$ | Standard dev. | $80,388.11$
|  |  |  | No answer | $0.66\%$ | No answer | $2.51\%$ |  |

Table 1. Demographics of 1,515 survey participants included in our analysis.
Our survey population was reflective of the overall demographics of Snapchat
users in terms of age and gender.

##  5\. Results

In this section, we first provide an overview of Snapchat usage among our
participants. Next, we present findings related to each contextual attribute
we analyzed. We then describe the motivations and considerations for public
sharing reported by our participants.

###  5.1. Snapchat Usage Patterns

A majority of our participants reported being aware of _Our Story_ , and
having a private audience for their _My Story_. Almost of fifth of
participants who previously shared to _Our Story_ were uncomfortable or very
uncomfortable with their decision to publicly share their last _Our Story_
Snap.

####  5.1.1. Our Story Usage

Out of our 1,515 participants, 1,032 (68.12%) reported that they were aware
that the _Our Story_ feature existed. Of these participants, 60.72% reported
ever sharing to it in the past, while 49.42% reported sharing to it in the
past week. Participants who were not aware that _Our Story_ existed were still
included in our analysis of _My Story_ contexts and potential sharing to _Our
Story_ , as we wanted the perspective of non-public sharing Snapchat users to
be represented. We observed that _Our Story_ was used less than other features
of the application, as 94.19% of our survey population reported directly
exchanging Snaps with another Snapchat user and 93.27% shared to _My Story_ in
the past week. Additionally, 1,320 participants reported viewing public Snaps
shared by people other than their Snapchat friends in the past week.

####  5.1.2. My Story Settings

The majority of our 1,515 participants reported having a private _My Story_
audience, with 58.48% retaining the default friends-only setting and 9.64%
specifying a custom list of friends. Over a quarter (28.18%) of participants
reported that their setting for _My Story_ was set to public. Of these
participants, 30.91% reported that they had never used _Our Story_. In later
results, we only analyze _My Story_ Snaps shared by participants who reported
having a private _My Story_ , to better understand the difference between
private and public contexts.

####  5.1.3. Comfort with Past Sharing to Stories

We analyzed participants’ reported comfort levels in their past sharing to _My
Story_ and _Our Story_ to gauge how comfortable our participants generally are
with public sharing. In reflecting on their past sharing to _My Story_ ,
31.14% of participants whose _My Story_ was not public reported that they
would be either uncomfortable or very uncomfortable publicly sharing the last
Snap they shared to the feature. Almost a fifth (19.84%) of participants who
shared to _Our Story_ and were aware that their Snaps were publicly viewable
reported that they were either uncomfortable or very uncomfortable with their
decision to publicly share their last _Our Story_ Snaps. In the following
sections, we analyze whether comfort levels with this past sharing is
correlated with contextual attributes.

###  5.2. Impact of Identity on Public Sharing

We framed the contextual factor identity as participants’ demographics (age,
race, age, and education level), privacy sensitivity, and personality. We
conducted statistical comparisons to examine the relationship between these
attributes, usage of _Our Story_ , and comfort with past sharing decisions.

####  5.2.1. Demographic Differences in Our Story Use

Since awareness and usage are related to a person, and not other aspects of
context, we correlated these factors with participants’ demographics. For
categorical data, including demographics, we conducted Pearson’s chi-squared
tests to determine the independence of two nominal variables. We observed
significant demographic differences in the usage of _Our Story_ related to
gender, race, and age. Between males and females, awareness of _Our Story_
significantly differed with less than a small effect, but males were
significantly more likely to have reported ever sharing to _Our Story_
($p<0.001,\phi=0.12$), and to have reported sharing to it in the past week
($p<0.001,\phi=0.14$). About half (51.09%) of male participants reported
sharing to _Our Story_ in the past week, compared to 36.89% of female
participants. However, sharing of direct Snaps and to _My Story_ did not
significantly differ between males and females.

Similarly, though there was no observed difference in awareness of _Our Story_
between racial groups, participants who identified as a racial minority were
more likely to report having ever used _Our Story_ ($p<0.001,\phi=0.11$), and
using it in the past week ($p<0.001,\phi=0.13$). Black or African American
identifying participants had the largest percentage of _Our Story_ sharers,
with 60.47% reporting to have used the feature, compared to 46.38% of
participants who identified as white or Caucasian. Usage of other Snapchat
features was not found to significantly differ with race.

Awareness that _Our Story_ existed did significantly differ by age though
($p<0.001,V=0.21$). Over three quarters (75.50%) of those younger than 18
years old reported being aware of _Our Story_ , compared to 45.19% of those
aged 35 and over. Usage of _Our Story_ also significantly differed between age
groups ($p=0.03,V=0.10$), with 57.38% of those between 13 and 17 reporting to
have ever used it, compared to 40.38% of those 35 or older. While _My Story_
activity within the past week did not significantly differ by age, younger
people were significantly more likely to report exchanging direct Snaps with
other users ($p=0.03,V=0.10$).

Participants who held at least a Bachelors degree did not significantly differ
from less educated participants in their usage of Snapchat features.
Additionally, participants’ reported usage of _Our Story_ was independent of
their reported privacy sensitivity and personality measures.

####  5.2.2. Comfort with Past Sharing Correlated with Personality

We analyzed how different identity attributes impacted comfort level with past
sharing decisions for those who were aware that _Our Story_ Snaps are publicly
viewable. Using the non-parametric correlation Kendall’s $\tau_{b}$, we found
that the extroversion ($p=0.006,\tau_{b}=0.11$) and openness measures
($p=0.003,\tau_{b}=0.12$) on the TIPI were found to be slightly positively
correlated with participants’ comfort with their decision to publicly share
their last _Our Story_ Snap. However, all three IUIPC privacy sensitivity
measures were not found to be significantly correlated. Participants’ reported
discomfort with potentially sharing their last _My Story_ to _Our Story_ had
significant, but negligibly small correlations with all five personality
measures, as well the IUIPC awareness and collection measures for privacy
sensitivity. We found through Wilcoxon-Mann-Whitney and Kruskal-Wallis tests
that participants’ comfort level in both past or potential _Our Story_ sharing
was independent from their demographics.

####  5.2.3. Views on Attribution

About a third of participants (33.27%) reported that they would be comfortable
or very comfortable with their Snapchat username being displayed with their
_Our Story_ Snaps, while 44.55% reported that they would be uncomfortable or
very uncomfortable. Of those who had previously shared to _Our Story_ , 30.01%
reported that they had added their Snapcode or Snapchat username to at least
one Snap they had shared to _Our Story_. In their open-ended survey responses,
participants reported the desire to get more Snapchat friends or followers and
meeting new people as their primary reasons for doing so. A few participants
also reported that they added attribution to their _Our Story_ content to
promote their business or build brand awareness.

####  5.2.4. Summary

Participants who identified as males or racial minorities reported using _Our
Story_ more frequently than others. Additionally, comfort with public sharing
was found to correlate with participants’ extroversion and openness.

###  5.3. Impact of Activity on Public Sharing

The second aspect of context that we explored was activity, which in our
survey we framed as what the user was doing at the time they shared the Snap,
the subject of the Snap, and whether the Snap was taken with the front or
rear-facing camera. Overall, we observed that the activity captured by the
Snap did impact participants’ sharing decisions.

####  5.3.1. Users Differentiate Private and Public Activities

Table 2 summarizes the activity context of Snaps participants privately shared
to _My Story_ , as well as those knowingly publicly shared to _Our Story_. In
terms of user status ($p<0.001,V=0.29$) and content ($p<0.001,V=0.28$), there
was a significant difference between the Snaps shared to the two features.
Comparatively, a larger percentage of _My Story_ Snaps (19.03% versus 9.78%)
were reported to be shared while hanging out with family, while a larger
percentage of _Our Story_ Snaps (22.28% versus 5.33%) were reported to be
taken while attending an event. Furthermore, a higher percentage of _My Story_
Snaps were of the user (32.55% versus 19.07%) or someone they know (20.16%
versus 11.72%), while a larger percentage of _Our Story_ Snaps (33.51% versus
12.39%) were of an activity. Though a larger percentage of _Our Story_ Snaps
were reportedly taken with the rear-facing camera, the camera used had a
negligible effect as to which story the Snap was shared.

User Status | Content | Camera
---|---|---
| My Story | Our Story |  | My Story | Our Story |  | My Story | Our Story
Hanging out with family | 19.03% | 9.78% | Me | 32.55% | 19.07% | Front-facing | 52.16% | 41.85%
Hanging out with friends | 14.89% | 12.23% | Someone I know | 20.16% | 11.72% | Rear-facing | 47.84% | 58.15%
Watching tv or movie | 8.38% | 5.71% | An activity | 12.39% | 33.51% |  |  |
Eating/preparing food | 6.90% | 5.43% | An animal | 10.42% | 5.72% |  |  |
Hanging out with an animal | 6.90% | 3.53% | Food/beverage | 8.75% | 12.53% |  |  |
Something outdoors | 5.52% | 3.80% | Something in nature | 6.88% | 10.35% |  |  |
Attending an event | 5.33% | 22.28% | An object | 6.19% | 5.45% |  |  |
Something for school/work | 4.73% | 5.98% | Someone I don’t know | 1.18% | 1.36% |  |  |
Getting ready to go out | 4.64% | 5.71% | Other | 1.47% | 0.27% |  |  |
Walking/driving around | 4.34% | 3.53% |  |  |  |  |  |
Traveling | 3.94% | 7.07% |  |  |  |  |  |
Celebrating a holiday | 3.55% | 4.89% |  |  |  |  |  |
Something active | 3.25% | 2.17% |  |  |  |  |  |
Sightseeing | 3.06% | 3.80% |  |  |  |  |  |
Something at home | 3.06% | 1.63% |  |  |  |  |  |
Shopping | 2.27% | 2.44% |  |  |  |  |  |
Other | 0.20% | 0.27% |  |  |  |  |  |

Table 2. Activity summary of Snaps last shared exclusively to _My Story_ and
those last shared to _Our Story_. The user status and content of _My Story_
Snaps were found to be significantly different from _Our Story_ Snaps.

####  5.3.2. Activity Impacts Comfort with Potential Sharing

A Friedman’s test revealed that participants reported that they would be less
likely to share Snaps to _Our Story_ in some user statuses or featuring some
subjects. Follow-up pairwise Wilcoxon Rank Sum tests, adjusted with the
Bonferroni correction for multiple comparisons, showed that participants
reported that they would be significantly less comfortable sharing Snaps taken
while hanging out with family, compared to all other user statuses
($all~{}p\leq 0.001$). Less than half of participants reported that they would
be comfortable or very comfortable sharing a Snap to _Our Story_ in this user
status. Additionally participants reported that they would be significantly
less comfortable sharing Snaps of themselves and other people to _Our Story_ ,
compared to other subject types ($all~{}p<0.001$). Only 28.45% of participants
reported that they would be comfortable or very comfortable with sharing Snaps
of a stranger to _Our Story_ , while less than half of participants reported
that they would be comfortable or very comfortable in sharing Snaps of
themselves or someone they knew. These differences can be seen in Figure 2.

Figure 2.  Participants’ reported comfort with potentially sharing Snaps to
_Our Story_ with different contexts. User status, content, location, and time
of day were found to significantly impact participants’ comfort with public
sharing.

####  5.3.3. Comfort with Past Sharing Independent of Activity

Kruskal-Wallis and Wilcoxon-Mann-Whitney tests revealed that participants’
reported comfort level with sharing their past _Our Story_ Snap was
independent of the subject of the Snap, user status, as well as the type of
camera used. Similarly, participants’ reported discomfort with potentially
sharing their last _My Story_ Snap to _Our Story_ was also independent of user
status and camera. However, the subject of the Snap was found to be correlated
with participants’ comfort with this type of sharing ($p=0.02,V=0.10$). The
subjects participants reported being least comfortable with sharing to _Our
Story_ were Snaps of someone they know and those of someone they do not know.
Only 27.32% and 33.33% of participants who shared _My Story_ Snaps with those
subjects, respectively, reported that they would be comfortable or very
comfortable sharing the Snap to _Our Story_.

####  5.3.4. Summary

Participants reported being less comfortable sharing Snaps of people, compared
to other subjects, which is also represented in the Snaps participants last
shared to _My Story_ and _Our Story_. Similarly, some user statuses, such as
hanging out with family, were considered more private.

###  5.4. Impact of Location on Public Sharing

The third contextual factor we studied was location. In our study, this was
the location a Snap was or could be taken.

####  5.4.1. Users Differentiate Private and Public Locations

Table 3 summarizes the location context of Snaps privately shared to _My
Story_ and knowingly publicly shared to _Our Story_ , which was found to be
significantly different ($p<0.001,V=0.38$). A larger proportion of _My Story_
Snaps were taken at home (50.49% versus 28.26%), while a larger proportion
(12.77% versus 2.85%) of _Our Story_ Snaps were taken at an event.

Location
---
| My Story | Our Story
My home | 50.49% | 28.26%
Outdoors/sports venue | 13.29% | 17.39%
Someone else’s home | 7.87% | 4.35%
Restaurant/coffee shop | 5.12% | 4.89%
My workplace | 4.92% | 3.80%
Means of transport | 4.63% | 4.35%
Nightlife spot | 2.95% | 4.08%
Event | 2.85% | 12.77%
University campus | 2.76% | 9.51%
Entertainment venue | 2.76% | 7.34%
Store/business | 2.36% | 3.26%
Other | 0.00% | 0.00%

Table 3. Location summary of Snaps last shared to _My Story_ and _Our Story_.
The distribution of locations was found to significantly differ between the
two features.

####  5.4.2. Location Impacts Comfort with Potential Sharing

The location of a Snap also appeared to significantly impact how comfortable
participants would be in sharing a Snap to _Our Story_. Participants reported
they would be significantly less comfortable sharing Snaps taken at their
workplace, their homes, or someone else’s homes, compared to other locations
($all~{}p<0.001$). As highlighted in Figure 2, only 32.01% reported that they
would be comfortable or very comfortable sharing a Snap to _Our Story_ while
at their workplace. Additionally, 43.63% and 38.28 of participants reported
they would be so in sharing a Snap that was taken at their or someone else’s
home, respectively.

####  5.4.3. Comfort with Past Sharing Independent of Location

Location was found to be independent of how comfortable a participant reported
being with their decision to publicly share their last _Our Story_ Snap.
Similarly, participants’ comfort level with potentially sharing their last _My
Story_ Snap to _Our Story_ was also independent of the location of the Snap.

####  5.4.4. Summary

Participants reported being less comfortable publicly sharing Snaps taken at a
home or workplace, compared to other locations, which is also reflected in the
Snaps participants last shared to _My Story_ and _Our Story_.

###  5.5. Impact of Time on Public Sharing

We also explored the impact of time, another component of context, on public
sharing. In our survey we asked participants about the day of week and time of
day a Snap was shared, or possibly could be shared in the future.

####  5.5.1. Our Story Usage Varies Throughout the Week

Table 4 provides a time context summary of the Snaps participants last shared
to _My Story_ and _Our Story_. Over two-thirds of (67.78%) of _My Story_ Snaps
were taken during a weekday, while the majority of _Our Story_ Snaps (52.17%)
were taken during a weekend, which was also a significant difference
($p<0.001,\phi=0.18$). This is in line with Juhász and Hochmair’s observation
that _Our Story_ Snaps are more likely to be shared on a weekend than a
weekday (Juhász and Hochmair, 2018). However, the time of day a Snap was
shared was found to be independent of the feature used to share the Snap.

Time of Day | Day of Week
---|---
| My Story | Our Story |  | My Story | Our Story
Morning | 11.39% | 8.70% | Weekday | 67.78% | 47.83%
Afternoon | 40.77% | 46.47% | Weekend | 32.22% | 52.17%
Evening | 35.85% | 35.33% |  |  |
Late night | 11.98% | 9.51% |  |  |

Table 4. Time summary of Snaps last shared exclusively to _My Story_ and those
last shared to _Our Story_.

####  5.5.2. Time of Day Impacts Comfort with Potential Sharing

Participants reported that they would be significantly less comfortable with
sharing a Snap to _Our Story_ if it was taken during the morning or late
night, compared to the afternoon and evening ($all~{}p<0.003$). As seen in
Figure 2, less than half of participants (45.74%) reported that they would be
comfortable or very comfortable sharing an _Our Story_ Snap in the morning,
while 56.24% reported they would be so sharing to _Our Story_ in the evening.
Whether a Snap would be shared on a weekend or weekday had a significant, but
negligibly small effect, on participants’ comfort level with sharing to _Our
Story_.

####  5.5.3. Comfort with Past Sharing Independent of Time

The time of day and day of week a Snap was taken was not found to have a
significant effect on participants’ comfort level in having shared it to _Our
Story_. They were also found to be independent of a participants’ comfort
level with potentially sharing the last Snap they shared to _My Story_ to _Our
Story_.

####  5.5.4. Summary

While time was not found to be correlated with participants’ comfort with
their past sharing, participants did report that they would be more
comfortable publicly sharing Snaps in the afternoon and evening, compared to
the morning and evening.

###  5.6. Sharing Motivations and Considerations

In our survey, we learned about sharing motivations by asking participants to
explain why they chose to share their last _Our Story_ Snap. We also learned
about participants’ considerations in public sharing by asking participants
why they chose not to share their last _My Story_ Snap also to _Our Story_.

####  5.6.1. Motivations

Participants’ motivations for sharing largely could be classified as
intrinsic, extrinsic, or altruistic. The majority (57.88%) of participants
provided an intrinsic reason for sharing their Snap to _Our Story_. Within
intrinsic motivations, explanations such as thinking that the content was
funny or interesting, wanting to have fun, and desires to share an experience
with the world were the most frequently reported. Examples of such intrinsic
reasons include “The kitten is super cute” and “To let everyone know I was at
my dream college.”

Another 13.05% of participants reported an extrinsic reason for sharing to
_Our Story_. These included motivations such as wanting to contribute to a
Snapchat topic, desire to show off one’s life, or getting more views or
followers. Some participants expressed that they wanted to build a personal or
business brand. A small percentage of participants (6.07%) described
altruistic motivations, meaning there was something about the activity that
led participants to share to _Our Story_. Such motivations included reasons
that considered benefits to the audience, such as “It was funny and wanted
other people to laugh too,” or “So that others could see how good the concert
was.” The remainder of participants who had previously shared a Snap to _Our
Story_ did not articulate a particular motivation for sharing their Snap,
reporting reasons like accidentally selecting the option in the interface.

####  5.6.2. Considerations

In their explanations for deciding not to share their _My Story_ Snap to _Our
Story_ , participants’ considerations primarily were related to audience,
content, and privacy and security. Most prominent were reasons that focused on
the audience of the Snap, cited by 19.26% of participants who had previously
shared a _My Story_ Snap. The majority of these participants (60.96%) reported
that they were uncomfortable with people they did not know having access to
the content. As one participant stated, “I honestly don’t want random people
seeing my random pictures.” The remainder of these participants reported that
they intended the Snap to be viewed by just their friends or family, which
could imply either that the content would be irrelevant to a public audience
or there was discomfort associated with strangers having access to the
content.

Another 18.66% of participants reported choosing not to publicly share their
_My Story_ Snap due to the content of the Snap. Of these participants, 42.09%
thought the content was too personal. For example, one participant explained,
“I’m pregnant and it showed my baby bump.” Another 41.01% felt that the
content was irrelevant for _Our Story_ or would be uninteresting to the
general public. The remaining 16.91% had reservations sharing their Snap to
_Our Story_ because it contained a child.

The next most common set of sharing considerations, reported in 8.86% of
responses, were privacy and security related. The majority of these
participants (70.95%) expressed a general desire to maintain privacy. As one
participant stated, “I like to keep things private, not share things for
anyone and everyone to see.” Some participants (18.24%) stated explicit
security threats, such as stalkers. A small group of these participants
(10.81%) explicitly mentioned that they did not want to reveal their location,
including both general concerns about strangers being able to see where they
are and concerns about revealing a private location. As an example, one
participant stated, “It was at my home and I didn’t want to share the
location.”

The remaining explanations provided did not highlight any particular
considerations the participant had in their sharing decisions. Instead, these
responses mentioned unfamiliarity with how _Our Story_ works, or their norms
of use on the app.

##  6\. Discussion

Our results suggest that contextual factors, such as the identity of the user,
activity captured, location, and time, all have an impact on content sharing
decisions on Snapchat. Sharing decisions were also found to be influenced by
intrinsic factors and the audience and content of the Snap being shared. A
summary of our results is presented in Table 5.

RQ1: What role does context play in sharing decisions?
---
Identity |  Males and racial minorities were more likely to have used _Our Story_ in the past. Comfort with past sharing was positively correlated with extroversion and openness.
Activity |  Participants reported being least comfortable with publicly sharing Snaps while hanging out with family, and Snaps of themselves and other people.
Location |  Participants reported being least comfortable with publicly sharing Snaps taken at their workplace or in a home.
Time |  Participants reported being least comfortable with publicly sharing Snaps in the morning and late night.
RQ2: What are users’ motivations and considerations in
publicly sharing content?
Motivations |  Participants’ motivations for public sharing were largely intrinsic. Other motivations were extrinsic or altruistic.
Considerations |  Participants’ considerations in public sharing were primarily related to audience, content, and privacy and security.

Table 5. Summary of results for our two research questions.

###  6.1. Limitations

Various aspects of our study design could have influenced our results. Our
study population was limited to those residing in the United States. Thus, the
motivations and considerations we report for Snapchat public sharing may not
generalize to populations in other countries. Similarly, the contextual
factors we explored may have a different impact on other populations.
Additional investigations, such as interviews with Snapchat users, may provide
further insight into our survey findings. However, we believe our study
provides an important initial understanding of decision-making in a context-
dynamic setting, such as Snapchat.

###  6.2. Audience Decisions in Content Sharing

The private-by-default nature of Snapchat is reflected by the contexts in
which our participants chose to share to _My Story_ and not to _Our Story_.
For example, the majority of _My Story_ Snaps were reported to be shared from
participants’ homes. We also observed that users may be proactively preventing
sharing regrets on Snapchat. None of the contextual factors we examined were
significantly correlated with participants’ comfort with their decision to
share their last _Our Story_ Snap. This may be because audience decisions on
Snapchat are made on a per-post basis, unlike many other platforms. Our
results, and those of prior work, suggest that this type audience control
interface may lead users to be more audience-aware compared to other content
sharing interfaces. Similar to our findings, Ahern et al. found that audience-
related considerations, as well as those related to privacy and security and
social disclosure, were salient in sharing decisions on ZoneTag, a photo-
sharing application in which audience decisions were also made on a per-post
basis (Ahern et al., 2007). In contrast, Bernstein et al. has shown that users
of platforms like Facebook, where audience decisions are not made on a per-
post basis, underestimate the number of people who can see their content
(Bernstein et al., 2013).

Moreover, for text posts on Facebook, Fiesler et al. found that the gender and
age of an individual, rather than the subject content of the post, were
predictive in whether or not a post was publicly shared (Fiesler et al.,
2017). In our study, we found that both demographics and content also played a
role in audience decision-making on Snapchat. In particular, participants
reported that they would be least comfortable publicly sharing Snaps of
themselves or other people, compared to other content types. Furthermore,
participants’ personality and the content of the Snap were the only
significant contextual factors in participants’ comfort level with potentially
sharing their last _My Story_ Snap to _Our Story_. This suggests that
multimedia content, such as Snaps, may be more privacy sensitive than text
content.

###  6.3. Design Implications

Content sharing platforms, beyond Snapchat, can incorporate our findings
related to context to both better support user motivations for public sharing,
as well as to address different user considerations.

####  6.3.1. Supporting User Motivations

We found that user motivations for sharing content publicly were largely tied
to a users’ identity, as over half of those who shared a Snap to _Our Story_
mentioned intrinsic reasons for doing so. Though less prominent, a sizable
percentage of participants mentioned extrinsic reasons for sharing their
Snaps. Our findings suggest that both intrinsic and extrinsic motivations can
be supported by the design of content sharing platforms.

For example, system features that encourage users to express themselves or
share their experience with the world would support users’ intrinsic desires
to share content. Additionally, these mechanisms could reassure users that
their contributions to the platforms are valued. On platforms where anonymity
is the default setting for public sharing, allowing users share their identity
in the form of attribution of their content would be another means of
supporting intrinsic motivations. This enables users to form a persona and
connect with each other more easily, which could lead to an increase in
overall engagement on the platform.

To build upon users’ intrinsic motivations for sharing, content sharing
platforms could additionally incorporate extrinsic motivators. One example is
social proof, a concept from psychology that describes how the behaviors and
decisions of others influence our own (Aronson et al., 2005; Amblee and Bui,
2011). On content sharing platforms, users may be more motivated to share
content if they are shown that other users, such as their friends, are also
doing so.

####  6.3.2. Determining Content Relevance

Our findings highlight that users have a variety of considerations when
sharing publicly. In our survey, those related to the content of Snaps were
common, with many participants reporting that they were unsure whether their
Snap would be interesting to the public. This suggests that social platforms
can proactively aid users in determining the relevance of content for a public
audience by highlighting topics that are likely to engage or be relevant to
other users. Platforms could also utilize contextual factors, such as location
or time, to suggest which sharing channels may be most appropriate for the
content. Such decision-making aids may provide a higher level of confidence to
users that their content is relevant to the community, and increase the
likelihood of their continued contribution.

####  6.3.3. Respecting User Privacy

On platforms such as Snapchat, where users typically exchange content with a
small group of people, the content being shared may be considered highly
personal. In our survey, many participants stated this reason for their choice
not to publicly share their last _My Story_ Snap, which likely reflects user
behavior in other content sharing settings. It is important for the default
settings of these platforms to be privacy-protective so that users can focus
on their interactions, instead of the sharing interface. For example, users
should explicitly indicate when they want the content that they share to be
publicly accessible. Making unexpected changes to these defaults may lead some
users to feel that their privacy has been violated.

System designs can also incorporate context to better respect user privacy.
Our findings indicate that users generally consider some locations and times
to be more private than others. As such, prompts for public sharing could be
designed to be respectful of these private contexts. For example, users may be
more receptive to a prompt to publicly share content if they are outdoors on a
weekend afternoon, compared to if they are at home on a Tuesday night. Taking
context into account in this way could make such prompts more effective.

##  7\. Conclusion

We conducted a survey to explore the role of context in sharing decisions on
Snapchat, as well as users’ general motivations and considerations when
publicly sharing content. We observed that some contexts, such as Snaps of
people or Snaps taken inside a home, were more likely to be privately shared
to _My Story_ , and were associated with less comfort with publicly sharing to
_Our Story_. Additionally, participants’ motivations for public sharing were
largely intrinsic, while considerations centered around the audience and
content of the Snap. As such, our findings can inform the design of context-
aware applications to better support user decision-making and preferences.

## Acknowledgments

The authors would like to thank Aletta Hiemstra for her help with qualitative
data analysis and copy editing.

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