# On viewing SpaceX Starlink through the Social Media Lens

Aryan Taneja, Debopam Bhattacherjee, Saikat Guha, Venkata N. Padmanabhan
Microsoft Research - India

###### Abstract.

Multiple low-Earth orbit satellite constellations, aimed at beaming broadband
connectivity from space, are currently under active deployment. While such
space-based Internet is set to augment, globally, today’s terrestrial
connectivity, and has managed to generate significant hype, it has been
largely difficult for the community to measure, quantify, or understand the
nuances of these offerings in the absence of a global measurement
infrastructure – the research community has mostly resorted to simulators,
emulators, and limited measurements till now. In this paper, we identify an
opportunity to use the social media ‘lens’ to complement such measurements and
mine user-centric insights on the evolving ecosystem at scale.

To illustrate the broader opportunity here, we focus on SpaceX Starlink – a
mega-constellation that is set to eventually include tens of thousands of Low-
Earth Orbit (LEO) satellites providing Internet connectivity from space.
Tapping social media, we present our analyses on learning about Starlink’s
network events even weeks before any public announcements on these, how some
large-scale events have significantly impacted user sentiment, and how users’
perception of Starlink performance has evolved over time. We discuss how the
methodology presented here could enable a better, holistic understanding of
these networks.

##  1\. Introduction

Low-Earth Orbit (LEO) satellite networks like SpaceX Starlink (starlink_40K,
), OneWeb (oneweb, ), Amazon’s Kuiper (amazon_news, ), Telesat’s Lightspeed
(telesat, ), and others are set to revolutionize the landscape of global
connectivity. Multiple of these LEO satellite constellations are currently
under deployment and SpaceX has already started offering Starlink Internet
services in $58$ countries (beta_500K, ) as of June, $2023$. They already have
more than $4$,$000$ (starlink_count_new, ) satellites deployed and their long-
term plan (starlink_40K, ) is to deploy $40K+$ LEO satellites. Recent advances
in launching (reusable_boosters, ; 10x_launch_cost_reduction, ) and satellite
design (satellite_size, ) have enabled such large-scale deployments consisting
of thousands of satellites. While such deployments have managed to generate
significant hype (leo_popular_1, ; leo_popular_2, ; leo_popular_3, ;
leo_popular_4, ; leo_popular_5, ), there is still a serious dearth of tools
that could measure these networks and quantify their performance at scale.
Although the first steps have been taken to simulate (kassing2020exploring, )
and emulate (lai2023starrynet, ) LEO networks and there have been reports on
limited network measurement experiments (starlink_perf_firstlook, ;
apnic_leo_cc, ), the broader community, which includes potential customers,
ISPs (read competitors), researchers, and Internet enthusiasts, is still in
search of a platform that gives a holistic picture of this ‘space’. In this
work, we explore if relevant information readily available on various social
media could be mined to fill this gap in understanding LEO networks.

Social platforms like Facebook, Reddit, LinkedIn, Instagram, and even newer
ones like Discord have seen large increases (facebook_stat, ; discord_stat, )
in user base and online communities over the last decade. Reddit, which is a
leading platform for topic-based discussion, now hosts more than $100$,$000$
active communities and generates more than $350$ million posts a year
(reddit_stat, ). Such tremendous growth of social media has also driven, to a
large extent, a lot of active research on sentiment analysis and opinion
mining. As more users share their personal experiences and express their
feelings online, such language capabilities could be leveraged to distill
subjective product feedback across online forums (liu2012sentiment, ;
anto2016product, ). While on one hand, the language capabilities have improved
significantly (devlin2018bert, ; dai2019transformer, ; yang2019xlnet, ;
liu2019roberta, ; qi2020stanza, ; azure_acs, ; google_NL_AI, ) in the last few
years, on the other hand, these tools have also been applied to diverse
sentiment analysis and opinion mining use cases – predict election results and
box-office collections of movies, understand attitude towards vaccination,
detect hate speech, etc. Computer vision has seen comparable advances, and
today we have document scanning and parsing (OCR) capabilities on our mobile
phones (google_lens, ; ms_lens, ). We use these new-age language and vision
capabilities and other techniques to analyze users’ perception of SpaceX
Starlink on a popular subreddit r/Starlink(starlink_subreddit, ). We believe
our techniques could be generalized across LEO providers and social platforms
to gather useful insights into the LEO broadband space.

Figure 1. Activity on r/Starlink. The left $y$-axis corresponds to both the
number of upvotes and comments.

Following the footsteps of past work on mining social platforms to quantify
network performance and demands (qiu2010listen, ; hsu2011using, ;
yang2016estimating, ), and network and service failures
(motoyama2010measuring, ; takeshita2015early, ), we demonstrate here the scope
of building a framework that enables the community around the ‘LEO’ space to
understand these networks better. r/Starlink has managed to draw significant
participation from enthusiasts, early adopters, and others. As, Fig. 1 shows,
there are $372$ posts per week on average with the peak reaching $1$,$326$
posts/week in Feb’$21$. The number of upvotes and comments, which are strong
signals of user activity, are $8$,$190$ and $5$,$702$ per week on average
respectively. While the initial flurry of enthusiasm (first half of $2021$)
has subsided, the subreddit continues to see significant user engagement. We
analyze the publicly available posts on the subreddit channel and gather
aggregated insights on users’ perception of Starlink service. We gather data
using standard social network APIs and use standard language and computer
vision tools, coupled with a set of custom heuristics, to extract structured
data on network performance from publicly shared screenshots, to generate word
clouds of $n$-grams, and to quantify user sentiment. Armed with this data, we
explore the following $3$ representational use cases in this paper:

  * •

We analyze how user sentiment follows related events and public announcements
– this could be used by competing ISPs and the research community to evaluate
the large-scale impact of changes and events – both technical and otherwise.
Also, such sentiment insights could equip potential Starlink customers to take
informed decisions while subscribing to broadband services. We also show how
specific events like Starlink network outages could be detected from public
discussions on social media leveraging a mix of language and sentiment
analysis tools.

  * •

Using publicly available information and a combination of language tools, we
could identify when roaming was enabled as a service on the Starlink network,
weeks before any public announcement on this from Starlink. For competing
ISPs, it is important to detect such signals early and act upon them.

  * •

We analyze how bandwidth on the Starlink network varies relatively over time
with more launches and users, and how that drives user sentiment. Such
insights could help the broad community to understand how customer expectation
changes as Starlink services evolve.

We believe our analyses make the case for coming up with a framework
encompassing multiple social platforms (Reddit is just one of them) and LEO
broadband providers (Starlink is just one of them) that help augment the
currently limited LEO measurement landscape. While here we present early-stage
analyses, this framework has the potential to offer a complementary user-
centric view of the LEO broadband space even in the presence of large-scale
LEO measurement platforms in the future.

##  2\. Methodology

In this section, we briefly discuss the APIs and language and vision tools we
use to collect, extract, and analyze publicly available information on the
Reddit social platform. We also describe the data we analyze.

We used Reddit (reddit_api, ) and pushshift.io (pushshift_api, ) APIs to get
all the post and comment data available on r/Starlink for the entire period
between Jan’$21$ and Dec’$22$ ($24$ months). We used the Python Reddit API
Wrapper (PRAW) (praw, ) to access the Reddit APIs. While gathering the data,
we cleaned it to get rid of user IDs and content that have been explicitly
requested to be removed by users. The former step makes sure that our work
does not raise any ethical concerns while the latter step conforms to data
privacy guidelines. We only collected data available publicly on the Reddit
platform.

We use the following language and computer vision tools for the analyses
presented in this paper:

  * •

Azure’s Cognitive Services (azure_acs, ): We use sentiment analysis and
opinion mining APIs to quantify the sentiment of posts and comments. We also
use ACS’ optical character recognition (OCR) service to extract network
measurement information from screenshots of speed-test reports shared by
Redditors (Reddit users) on the platform.

  * •

Natural Language Toolkit (NLTK) (nltk, ): We use the libraries to filter out
stop-words, extract $n$-grams, and create word clouds of posts and comments.

Rather than reinventing the wheel, we rely on the already available language
and vision tools. Our focus is on demonstrating how the capabilities of these
tools could be leveraged to understand users’ perceptions of networks better.
The accuracy of our analyses depends on the accuracy of these tools.

Figure 2. (a) Simple and (b) table-structured screenshots (standard negative
of the original) of speed-test reports from Ookla, posted on r/Starlink. Boxes
with red borders mark data that could be extracted.

Nevertheless, we built a set of heuristics to extract network measurement
metrics from the OCR output of semi-structured speed-test reports across all
test providers. Fig. 2 show two such templates from Ookla. Our relative pixel-
distance-based heuristics could extract downlink and uplink speeds and latency
(also jitter and packet loss rates, when available) along with the
corresponding units, date and time information, network provider name, server
location, etc. While we do not use speed-test location information in our
current analyses, we could have used the server location as a proxy for client
location, depending on the use case, as test providers like Ookla (present at
$1$,$000+$ locations (ookla_server_sel, )) usually pick a ‘closest’-on-the-
network server for the measurements. The heuristics could parse all the
different speed-test templates, including more complex table-structured ones
(having multiple sub-reports together, like in Fig. 2), which we could collect
over the entire period. We discuss the related results in §5.

While the readers already have a rough idea of the data by now, let us briefly
list down the inputs to our analysis framework for clarity:

  * •

Reddit posts: We gather post text, embedded URLs, photos, and videos,
submission timestamp, post ID, and post metadata (number of comments and
upvotes).

  * •

Comments: We collect comment text, embedded URLs, photos, and videos,
submission timestamp, comment ID, and metadata. We regenerate the comment tree
for a post leveraging parent IDs in the comment metadata.

  * •

Speed-test screenshots: Many people post screenshots of speed-tests (across
providers) on online forums. We gather these screenshots for further analysis
while getting post data.

##  3\. Events drive sentiment

Redditors’ sentiments on r/Starlink are influenced by events. We
systematically identify peaks of strong sentiment and automate the discovery
of related events of interest.

Figure 3. Although r/Starlink is an active subreddit, (a) only a few sentiment
peaks are observed. We pick the top $3$ peaks and tie them to actual events
that led to these peaks. (b) shows the word cloud for the $3^{rd}$ highest
peak observed during and after a large-scale service outage.

For each day between Jan’$21$ and Dec’$22$, we analyze the sentiment of
individual post content (text) using Azure’s Cognitive Services. The sentiment
analysis service assigns $3$ different scores – positive, negative, and
neutral – to each piece of text, which add up to $1$. We count the number of
posts with strong positive ($\geq$$0.7$) and negative ($\geq$$0.7$) scores per
day and plot them in Fig. 3.

Our framework could discover sentiment peaks (day-wise bins), generate word
clouds (using NLTK) across all posts and comments over a day for those peaks,
and discover relevant news articles by searching online for the keywords (top
$3$ uni-grams) appended with ‘Starlink’ for the custom date. This pipeline
enables the framework to annotate sentiment peaks with events that drive them.

The top $3$ sentiment peaks, as shown in Fig. 3, correspond to events of $3$
distinct flavors. On $9^{th}$ Feb’$21$, Redditors showed strong positive
sentiment towards Starlink opening up pre-ordering of user terminals in the
US, Canada, and UK (starlink_preorder, ). On $24^{th}$ Nov’$21$, SpaceX’s
email (starlink_delay, ) to pre-order customers on delay in terminal delivery
led to a negative sentiment peak. Fig. 3 shows the word cloud corresponding to
the third highest peak ($22^{nd}$ Apr’$22$) which is driven by negative
sentiment. The $3^{rd}$ most common word in the generated word cloud is
‘outage’. Interestingly, we could not find any relevant news though for this
date, although Redditors from $14$ different countries (including $\sim$$190$
reports from the US) confirmed an outage online.

On further exploration, we could detect more such sentiment peaks which
correspond to similar service outages, many of which were reported by the
media (starlink_outage1, ; starlink_outage2, ; starlink_outage3, ). Across
such outages, negative sentiment (number of strongly negative posts and
comments on outage as a fraction of all related posts and comments) varies by
as much as $70\%$ depending on the nature of the outage. While some outages
lasted for tens of minutes to hours, the one event that generated the most
negative sentiment was on $10^{th}$ Mar’$21$ – during the early beta-testing
phase. Redditors reported (reddit_outage_thread_10Mar21, ) multiple short
outages resulting in frequent call drops throughout the day, thus adding to
their annoyance.

Figure 4. While a few larger outages sparked a lot of discussions on
r/Starlink, outages with smaller impacts are quite frequent. Threads with
positive or neutral sentiments have been filtered out.

Figure 5. (a) Popularity of posts has a long tail with a few posts attracting
many comments and up-votes. For Feb’$21$, the second most popular post
(published on $23^{rd}$ Feb) has (b) roaming and roaming enabled as the most
common uni-gram and bi-gram respectively.

While the above approach (step $1$) is aimed at identifying sentiment peaks
tied to various events, both networking and otherwise, we dive deeper and
focus more on network service outages next (step $2$). We created a library of
uni/bi-grams which are common in ‘outage’ posts and comments identified
following the above procedure. We manually verified that keywords in this
library are meaningful in the specific context and got rid of outliers. We
then identified threads with such keywords and negative sentiments (i.e., we
filtered out threads with such keywords but positive or neutral sentiments)
assuming outages to be ‘negative’ events. Fig. 4 plots the day-wise
frequencies of these keywords in such negative sentiment threads. $7^{th}$
Jan’$22$ and $30^{th}$ Aug’$22$ have the largest spikes of such keywords and
correspond to large reported outages (starlink_outage7Jan22, ;
starlink_outage30Aug22, ). But more interestingly, there are numerous shorter
peaks over time which correspond to local transient outages. Interestingly, in
Fig. 3 we did not observe any sentiment peaks for the above $2$ dates when
there were global outages because Redditors were also discussing other non-
networking positive events that coincided with these network outages. The
$2$-step process (leveraging specific keywords library and sentiment analysis)
helps us zoom in on the particular event of interest – network outage in this
case.

OOkla’s Downdetector (downdetector, ) also uses sentiment data from Twitter
alongside direct reports, albeit the detailed methodology is not publicly
available. Our framework, on the other hand, leverages a 2-step process, as
detailed above, that could be used to analyze both networking and non-
networking events of interest. Social platform-based methods could detect
transient outages (probably due to network updates, misconfigurations, etc.)
which might go undetected/unreported otherwise. In these initial days of LEO
broadband offerings, understanding such transient downtimes is crucial in
fixing performance bottlenecks and misconfigurations.

IODA (ioda_outage, ) relies on active probing and BGP data to detect Internet
outages. While the methodology works well for the terrestrial Internet, it
would be interesting to revisit the same in the context of LEO networks – LEO
terminals in a region might transiently have no satellites within the field of
view and experience temporary downtime. We keep a deeper comparison with IODA
for future work.

##  4\. Know before we know!

What are some of the most popular topics being discussed on r/Starlink and
what could we infer from them? For each month, we identify the list of popular
posts and explore the topics of discussion. To do this, we rely on $2$ metrics
– the number of upvotes and the number of comments per post. Fig. 5 plots the
corresponding CDFs for the month of Feb’$22$. We observe that both the CDFs
have long tails – we pick popular posts which lie at the intersection of the
$99^{th}$ percentiles of both the CDFs. For Feb’$22$, there are $1$,$898$
posts in total, and $8$ of them are identified to be popular based on the
definition above.

The most popular post is on the Ukraine war and the role of SpaceX Starlink in
providing connectivity. We rather focus on the second most popular post which
has $500+$ upvotes and $100+$ comments. We use NLTK to remove common stop
words and create a word cloud of uni-grams for the post and the comments on
the post. Fig. 5 shows that the most common uni-gram being discussed is
‘roaming’. As a next step, we generate a similar word cloud but this time with
the bi-grams. The most common bi-gram is ‘roaming enabled’. We also analyze
the sentiment scores of the specific post and the comments, and found them to
be primarily positive, which increases our confidence on the fact that
Starlink roaming might have been enabled in early February, $2022$.

Interestingly, while Redditors discussed Starlink roaming being functional on
$23^{rd}$ Feb’$22$ already (we could also identify a smaller peak on the same
topic on $15^{th}$ Feb), SpaceX officially notified (starlink_portability, )
Starlink subscribers on portability (roaming) mode only in May, $2022$ – $3$
months down the line. Elon Musk, CEO of SpaceX, tweeted earlier (musk_roaming,
) on $3^{rd}$ March on mobile roaming being enabled, still $\sim$$2$ weeks
after we could detect the earliest signal from r/Starlink. So, our framework
could automatically detect that roaming have been enabled on the Starlink
network way before any public announcements from SpaceX on the same.

Our methodology above could be used for different use cases. For example, ISPs
could use such a tool to keep track of competition and be on the forefront of
innovation. Also, while exploring the subreddit manually, we observed posts
(reddit_link1, ; reddit_link2, ; reddit_link3, ; reddit_link4, ) which discuss
deep technical aspects. An extension to our methodology could allow ISPs to
pinpoint such public discussions and learn from community expertise. As social
networks get all the more ingrained in our lives, and with more people
becoming comfortable discussing ideas and reporting facts online, ISPs could
probably benefit from tapping into the public whiteboard discussions on social
platforms.

Also, at these early stages of LEO broadband, automatic identification of such
events will allow potential customers, researchers, and Internet enthusiasts
to keep track of all the features and their evolution over time. A potential
customer in a remote area who ‘needs’ the roaming capabilities might benefit
from such early information on the same. We also observed Redditors discussing
(orion1, ; orion2, ; orion3, ; orion4, ; orion5, ; orion6, ; orion7, )
performance bottlenecks (and opportunities) of applications, like video
conferencing, over LEO broadband. These social media insights could allow
application providers to pinpoint issues over these new networks and fix them
early toward offering improved experience.

##  5\. Following the shifting fulcrum

Social media users share photos and screenshots while posting online.
Redditors on r/Starlink often share screenshots (or links to them) of network
performance test reports to spark discussions. We gather all such test report
screenshots across test providers like Ookla (ookla_speedtest, ), Fast
(powered by Netflix) (fast_speedtest, ), Starlink itself, and others, and
extract uplink speed, downlink speed, latency information, etc. using Azure’s
Optical Character Recognition (OCR) (azure_ocr, ). After applying a set of
heuristics, as discussed in §2, to rule out false positives, we could identify
$\sim$$1$,$750$ reports of Starlink speed-tests being shared publicly between
Jan, $2021$ and Dec, $2022$. In this section, we focus on the evolution of
‘observed’ downlink speed during this period and users’ perceptions of the
same.

Figure 6. Observed downlink speeds on Starlink evolve with more launches and
customers. User sentiment largely follows observed speed. Number of Starlink
users, as and when available as public information, has been used to annotate
the plot.

Demand vs. supply Fig. 6 shows the change in observed downlink speeds with
time. For each month, we plot the median speeds across all shared screenshots
of Starlink speed tests. We annotate the observed speeds with the number of
Starlink launches (starlink_launches, ) and also the reported number of
Starlink users (whenever public information is available) (beta_10K, ;
beta_69K, ; beta_90K, ; beta_140K, ; beta_250K, ; beta_400K, ; beta_500K, )
during a month. We also plot the monthly median downlink speeds with $95\%$
and $90\%$ of the monthly speed data picked uniformly at random – the plots
closely follow each other showing that the observed medians are considerably
stable.

We observe that, between Jan and Sep’$21$, the median downlink speeds
increased in general. SpaceX launched ($\sim$$60$ satellites per launch) $14$
times during this period and the number of users increased from $10K$ (in Feb)
to $90K$ (in Aug). Further, between Sep’$21$ and Dec’$22$, there has been an
almost steady decrease in observed speeds although SpaceX launched batches of
Starlink satellites $32$ times. Note, however, that the number of reported
Starlink users increased from $90K$ to $1M$ (and beyond) during the same
period, resulting in more than $1000\%$ rise in downlink demand, assuming a
linear increase in demand with users. Note that between Nov’$21$ and Jan’$22$,
there were $4$ Starlink launches while the number of users increased by only
$5K$ (announcement by Starlink on delay in terminal delivery in Nov’$21$, as
shown in Fig. 3). This resulted in slight improvements in observed bandwidth
in Jan’$22$. In March, April, and June’$21$, observed downlink speeds improved
as new satellites were deployed steadily. But it is not always a
straightforward reflection of deployment as more Starlink users are also added
continually, as is evident in some other cases. On the contrary, if there
aren’t any new launches, and new users are added, the observed speeds
decrease. Between Jun and Aug’$21$, $21K$ new users started using Starlink
with no new launches happening. This is reflected in the sharp decrease in
median speeds during the period. Beyond Sep’$21$, reported bandwidths have
decreased almost steadily given the large increase in demand as Starlink
service expanded to various countries across the globe.

The broad observation here is that more Starlink launches do not always result
in higher observed bandwidth, although the aggregate bandwidth of the Starlink
system should be increasing with the addition of satellites. It is a complex
calculus involving both supply and demand, and it is always a race to add more
satellites and cater to the ever-increasing aggregate bandwidth demands.

We did not quantify any bias inherent in such social media-based estimates.
Note that unbiased absolute values of download speeds are not critical for our
analyses; we rather needed relative changes in download speeds and
corresponding sentiments (discussed next) from one month to the next. As part
of future work, any bias arising due to demographics in a social community
will hopefully reduce as we span across more social platforms. We discuss this
in further depth under §7.

The wheel of time With time, the perception of users on network performance
changes. In Fig. 6 we also plot (green, dashed) the strong positive sentiment
of users on downlink speeds. To do this, we analyze the sentiment of posts
(text content) that share Starlink speed-test reports using Azure’s Cognitive
Services. We identify posts with strong positive ($\geq$$0.7$) or negative
($\geq$$0.7$) scores, and define the normalized strong positive sentiment
score ($Pos$) as the number of strong positive posts over sum of the number of
strong positive and negative posts in a month thus filtering out edge cases
when identifying the sentiment is hard.

We observe that $Pos$ broadly follows the observed downlink speed trends, but
there are interesting exceptions. For instance, while downlink speed is higher
in Dec’$21$ than Apr’$21$, $Pos$ is drastically lower for Dec’$21$. We believe
this is because user sentiment is, in general, a reflection of both short-term
and long-term conditioning – users get acclimatized to their current network
conditions and give negative sentiment for any degradation in network
conditions even if such conditions are better than the past. The exact inverse
of this trend is also visible – the downlink speeds decrease between Mar’$22$
and Dec’$22$ while the $Pos$ improves over time. This demonstrates users
getting conditioned to lower speeds, but not necessarily attachment and
loyalty to the ISP. While we observed users frequently discussing application
performance over LEO networks on social platforms, we keep such an in-depth
and finer-granularity study of observed bandwidth versus user sentiment for
future work.

##  6\. Broader implications

While we have focused on SpaceX Starlink and the corresponding subreddit
r/Starlink in this preliminary study, our methodology and framework could span
multiple online social communities and LEO providers to collect interesting
insights in the absence of large-scale measurement platforms. Even beyond LEO,
such a framework allows us to revisit our perception of Internet measurement.

User-centric For relatively new offerings like LEO broadband Internet, it is
critical to assess user perception and engagement to gauge the market demand
and dynamics. How do the customers react and express themselves online in
response to broad events such as infrastructure upgradation, outage, peering
and partnership announcements, etc.? An NLP and vision services-enabled
framework like this could potentially tap into the large corpus of user
feedback, discussions, and debate publicly available on various online social
platforms.

Complementarity to measurement testbeds While platforms like PlanetLab
(planetlab, ) and M-Lab (MLab, ) capture Internet measurements at scale over
years, LEO broadband networks with all the uniqueness and orbital dynamics
also need to be measured at such global scales. But even if such an LEO
measurement platform comes into existence, insights from social media
capturing user perception and sentiment could complement such measurements,
offering a more holistic view of these networks at the early stages of
deployment.

Active user engagement One could take a step further and engage actively with
users on social platforms. It should be straightforward to share back the
insights at both aggregate and granular levels with users. For example, when a
user uploads a speed test screenshot, the framework could employ bots that
allow the user to compare their experience with that of others. A user can opt
out of such a service at will and could also request the framework to delete
their data. If a user rather wants to opt in, there could be different levels
of user engagement – allowing the framework to collect more data (e.g.
physical coordinates of user terminals) and run more speed tests. Enthusiastic
users could also be willing to share some of their resources for running
arbitrary network experiments on a slice. A global LEO measurement testbed
would need spatiotemporal diversity, given the unique dynamicity and geometry
of the constellations, and benefit from such large-scale voluntary
participation.

##  7\. Discussions

AI for user sentiment Generative AI models (gpt3, ; llama, ; openai2023gpt4, )
could effectively summarize and quantify user feedback and sentiment while
removing harmful/biased content. Custom AI models that could understand the
user pulse on social media at scale could augment the insights generated using
cutting-edge vision and sentiment analysis tools.

The social network behavioral bias: We did not analyze the bias that might
occur due to a divergence between the experience of users and what they choose
to report on social networks. For example, a user might be running network
speed tests multiple times and reporting only the better/worse ones. Another
bias might arise due to users in a public group being over-enthusiastic about
the topic. For example, in the case of Reddit, discussions on r/Starlink could
have a stronger negative sentiment toward Starlink’s competitors. Also, bias
might arise due to the socio-demographics of users active on a particular
social platform (hargittai2020potential, ). As part of future work, we plan to
expand across multiple social platforms and communities and combine social
media data with other sources of information to quantify/mitigate this bias,
if existent.

Gaming the framework: Different stakeholders including users and ISPs could
game the framework. For example, automated bots on Reddit could publish posts
with strong sentiments to skew the aggregate analyses. We could rely on
ongoing research on detecting bot-generated text and bot behavior
(alothali2018detecting, ; schuchard2019bot, ; shi2019detecting, ;
beskow2018bot, ) to tackle this problem. AI/ML-based techniques
(luceri2020detecting, ; al2022online, ) to detect large-scale trolls on social
media could also be plugged in to make the framework more robust to such
gaming. One problem though is that on a specific social community, like
r/Starlink, the number of interactions is not too high volume, thus making it
vulnerable to gaming. Nevertheless, gathering data across multiple platforms
and communities might address this issue.

Tackling ethical and privacy concerns: While we do not use user identification
information in this work, one might extend the system to engage more closely
with users, as discussed in §6. In such cases, we should carefully consider
the ethical and privacy concerns that might arise while dealing with user-
level data. Data should be anonymized, and user consent should precede active
user engagement. One should also have a mechanism in place to permanently
delete user data upon request, conforming to privacy regulations such as GDPR
(gdpr, ), CCPA (ccpa, ), etc.

The more the better: In this preliminary study, we only analyze data from a
single platform (Reddit) and for a single ISP (Starlink). The framework could
be extended to also analyze other social platforms such as Facebook, Twitter,
Discord, etc., and other ISPs. The more data and the more diverse group
culture explored, the easier it would be to identify sentiment peaks and
important network (and non-network) events. Also, more data across groups with
related but slightly different agendas might reduce the behavioral bias
discussed above.

Location-based analysis (LEO specific) More data on speed-test reports across
test providers and social platforms could allow us to analyze the impact of
constellations’ geometry, Earth’s shape, cell congestion, interference with
GEO satellites, etc. on Starlink performance. This might require users to
engage more with the framework and share explicit location information.

##  8\. Related work

Recent breakthrough advances in natural language processing (devlin2018bert, ;
dai2019transformer, ; yang2019xlnet, ; liu2019roberta, ; qi2020stanza, ;
azure_acs, ; google_NL_AI, ; openai2023gpt4, ) and computer vision
(girshick2014rich, ; redmon2016you, ; he2017mask, ; brock2018large, ;
karras2019style, ) have made text and image scanning, parsing, and
comprehension capabilities (luan2019general, ; huang2019icdar2019, ;
liu2019graph, ) ubiquitous among other things. Sentiment analysis, which is
used for detecting sentiments underlying text, has been exhaustively applied
to a multitude of use cases – understanding product feedback and preferences
(liu2012sentiment, ; anto2016product, ), and predicting trends and outcomes of
large-scale events (matalon2021using, ; karalevicius2018using, ;
bermingham2011using, ) leveraging the corpus of publicly available user
interactions on online social platforms. Leveraging social media to understand
networks better is not a completely new space – sentiment data have been used
to understand/detect mobile network performance (qiu2010listen, ;
hsu2011using, ) and demands (yang2016estimating, ), service availability and
failures (motoyama2010measuring, ; takeshita2015early, ), and attacks and
security vulnerabilities (al2013leveraging, ; ritter2015weakly, ;
sabottke2015vulnerability, ; khandpur2017crowdsourcing, ;
chambers2018detecting, ; shu2018understanding, ). Motivated by this large body
of past work, we explore a similar methodology to understand SpaceX Starlink
service leveraging crowdsourced information available on social media.

While many measurement platforms (planetlab, ; MLab, ; ripe_atlas, ;
ookla_speedtest, ; fast_speedtest, ) exist that gather Internet performance
metrics at scale, we did not find a measurement platform yet targeted to
analyze the nuances of LEO dynamics – temporal changes in latency and
bandwidth independent of the congestion, impact of the constellation geometry,
weather, and interference with other similar services, etc. While the research
community has already engaged in simulations (kassing2020exploring, ;
bhattacherjee2019network, ; handley2018delay, ) and limited measurements
(starlink_perf_firstlook, ; leo_geo_aus, ) of these networks, measurements at
scale are still unavailable. A recent work (starlink_browserside, ) on
gathering LEO measurements leverages a custom-built browser plugin – our work
is complementary to this work in the absence of large-scale LEO measurement
platforms. Even if large-scale LEO measurement platforms become available in
the future, our framework has the potential to complement such platforms by
offering a user-centric view of the services. A more recent work
(pan2023measuring, ) uses Reddit to “crosscheck” Starlink measurement
insights, but we rather consume public data on the r/Starlink subreddit as the
primary source of performance and user sentiment measurements.

##  9\. Conclusion

We leverage recent advances in language and vision capabilities to mine social
media and understand users’ perceptions of SpaceX Starlink network performance
and events. This framework could complement LEO broadband measurement tools by
offering a user-centric view of these networks. Our framework could detect
important network events days/weeks before public announcements, detect
sentiment peaks and related events (like outages), and understand the evolving
perception of bandwidth on the SpaceX Starlink network.

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