Are We Watching More Ads Than Content? Analyzing YouTube Sponsor Data

not the one one who feels that YouTube sponsor segments have develop into longer and extra frequent just lately. Generally, I watch movies that appear to be attempting to promote me one thing each couple of seconds.

, it positive is annoying to be bombarded by adverts. 

On this weblog submit, I’ll discover these sponsor segments, utilizing information from a well-liked browser extension referred to as SponsorBlock, to determine if the perceived improve in adverts truly did occur and likewise to quantify what number of adverts I’m watching.

I’ll stroll you thru my evaluation, offering code snippets in Sql, DuckDB, and pandas. All of the code is accessible on my GitHub, and for the reason that dataset is open, I may also train you the best way to obtain it, so as to comply with alongside and play with the information your self.

These are the questions I will probably be attempting to reply on this evaluation:

• Have sponsor segments elevated over time?
• Which channels have the best share of sponsor time per video?
• What’s the density of sponsor segments all through a video?

To get to those solutions, we must cowl a lot floor. That is the agenda for this submit:

Let’s get this began!

SponsorBlock is an extension that permits you to skip advert segments in movies, much like the way you skip Netflix intros. It’s extremely correct, as I don’t bear in mind seeing one fallacious section since I began utilizing it round a month in the past, and I watch numerous smaller non-English creators.

You is perhaps asking your self how the extension is aware of which components of the video are sponsors, and, imagine it or not, the reply is thru crowdsourcing!

Customers submit the timestamps for the advert segments, and different customers vote if it’s correct or not. For the common consumer, who isn’t contributing in any respect, the one factor you need to do is to press Enter to skip the advert.

Okay, now that you understand what SponsorBlock is, let’s speak in regards to the information. 

Cleansing the Knowledge

If you wish to comply with alongside, you may obtain a replica of the information utilizing this SponsorBlock Mirror (it’d take you fairly a couple of minutes to obtain all of it). The database schema may be seen here, though most of it gained’t be helpful for this challenge.

As one may anticipate, their database schema is made for the extension to work correctly, and never for some man to principally leech from an enormous group effort to search out what share of adverts his favourite creator runs. For this, some work will have to be finished to scrub and mannequin the information.

The one two tables which might be essential for this evaluation are:

• sponsorTimes.csv : That is crucial desk, containing the startTime and endTime of all crowdsourced sponsor segments. The CSV is round 5GB.
• videoInfo.csv : Accommodates the video title, publication date, and channel ID related to every video.

Earlier than we get into it, these are all of the libraries I ended up utilizing. I’ll clarify the much less apparent ones as we go.

pandas
duckdb
requests
requests-cache
python-dotenv
seaborn
matplotlib
numpy

Step one, then, is to load the information. Surprisingly, this was already a bit difficult, as I used to be getting numerous errors parsing some rows of the CSV. These had been the settings I discovered to work for almost all of the rows:

import duckdb
import os

# Connect with an in-memory DuckDB occasion
con = duckdb.join(database=':reminiscence:')

sponsor_times = con.read_csv(
    "sb-mirror/sponsorTimes.csv",
    header=True,
    columns={
        "videoID": "VARCHAR",
        "startTime": "DOUBLE",
        "endTime": "DOUBLE",
        "votes": "INTEGER",
        "locked": "INTEGER",
        "incorrectVotes": "INTEGER",
        "UUID": "VARCHAR",
        "userID": "VARCHAR",
        "timeSubmitted": "DOUBLE",
        "views": "INTEGER",
        "class": "VARCHAR",
        "actionType": "VARCHAR",
        "service": "VARCHAR",
        "videoDuration": "DOUBLE",
        "hidden": "INTEGER",
        "popularity": "DOUBLE",
        "shadowHidden": "INTEGER",
        "hashedVideoID": "VARCHAR",
        "userAgent": "VARCHAR",
        "description": "VARCHAR",
    },
    ignore_errors=True,
    quotechar="",
)

video_info = con.read_csv(
    "sb-mirror/videoInfo.csv",
    header=True,
    columns={
        "videoID": "VARCHAR",
        "channelID": "VARCHAR",
        "title": "VARCHAR",
        "revealed": "DOUBLE",
    },
    ignore_errors=True,
    quotechar=None,
)

# Ignore warnings
import warnings
warnings.filterwarnings('ignore')

Here’s what a pattern of the information appears like:

con.sql("SELECT videoID, startTime, endTime, votes, locked, class FROM sponsor_times LIMIT 5")

con.sql("SELECT * FROM video_info LIMIT 5")

Understanding the information within the sponsorTimes desk is ridiculously essential, in any other case, the cleansing course of gained’t make any sense.

Every row represents a user-submitted timestamp for a sponsored section. Since a number of customers can submit segments for a similar video, the dataset comprises duplicate and probably incorrect entries, which is able to have to be handled throughout cleansing.

To seek out incorrect segments, I’ll use the votes and the locked column, because the latter one represents segments that had been confirmed to be appropriate. 

One other essential column is the class. There are a bunch of classes like Intro, Outro, Filler, and so on. For this evaluation, I’ll solely work with Sponsor and Self-Promo.

I began by making use of some filters:

CREATE TABLE filtered AS
SELECT
    *
FROM sponsor_times
WHERE class IN ('sponsor', 'selfpromo') AND (votes > 0 OR locked=1)

Filtering for locked segments or segments with greater than 0 votes was an enormous resolution. This diminished the dataset by an enormous share, however doing so made the information very dependable. For instance, earlier than doing this, all the High 50 channels with the best share of adverts had been simply spam, random channels that ran 99.9% of adverts.

With this finished, the subsequent step is to get a dataset the place every sponsor section exhibits up solely as soon as. For instance, a video with a sponsor section initially and one other on the finish ought to have solely two rows of knowledge.

That is very a lot not the case to date, since in a single video we will have a number of user-submitted entries for every section. To do that, I’ll use window capabilities to establish if two or extra rows of knowledge signify the identical section. 

The primary window operate compares the startTime of 1 row with the endTime of the earlier. If these values don’t overlap, it means they’re entries for separate segments, in any other case they’re repeated entries for a similar section. 

CREATE TABLE new_segments AS
SELECT
    -- Coalesce to TRUE to take care of the primary row of each window
    -- because the values are NULL, however it ought to depend as a brand new section.
    COALESCE(startTime > LAG(endTime) 
      OVER (PARTITION BY videoID ORDER BY startTime), true) 
      AS new_ad_segment,
    *
FROM filtered

The new_ad_segment column is TRUE each time a row represents a brand new section of a video. The primary two rows, as their timestamps overlap, are correctly marked as the identical section.

Subsequent up, the second window operate will label every advert section by quantity:

CREATE TABLE ad_segments AS
SELECT
    SUM(new_ad_segment) 
      OVER (PARTITION BY videoID ORDER BY startTime)
      AS ad_segment,
    *
FROM new_segments

Lastly, now that every section is correctly numbered, it’s simple to get the section that’s both locked or has the best quantity of votes.

CREATE TABLE unique_segments AS
SELECT DISTINCT ON (videoID, ad_segment)
    *
FROM ad_segments
ORDER BY videoID, ad_segment, locked DESC, votes DESC

That’s it! Now this desk has one row for every distinctive advert section, and I can begin exploring the information.

If these queries really feel difficult, and also you want a refresher on window capabilities, take a look at this blog submit that can train you all you could find out about them! The final instance lined within the weblog submit is sort of precisely the method I used right here.

Exploring and Enhancing the Knowledge

Lastly, the dataset is nice sufficient to start out exploring. The very first thing I did was to get a way of the dimensions of the information:

• 36.0k Distinctive Channels
• 552.6k Distinctive Movies
• 673.8k Distinctive Sponsor Segments, for a mean of 1.22 segments per video

As talked about earlier, filtering by segments that had been both locked or had not less than 1 upvote, diminished the dataset massively, by round 80%. However that is the value I needed to pay to have information that I may work with.

To verify if there’s nothing instantly fallacious with the information, I gathered the channels which have probably the most quantity of movies:

CREATE TABLE top_5_channels AS 
SELECT
    channelID,
    depend(DISTINCT unique_segments.videoID) AS video_count
FROM
    unique_segments
    LEFT JOIN video_info ON unique_segments.videoID = video_info.videoID 
WHERE
    channelID IS NOT NULL
    -- Some channel IDs are clean
    AND channelID != '""'
GROUP BY
    channelID
ORDER BY
    video_count DESC
LIMIT 5

The quantity of movies per channel appears lifelike… However that is horrible to work with. I don’t need to go to my browser and lookup channel IDs each time I need to know the identify of a channel.

To repair this, I created a small script with capabilities to get these values from the YouTube API in Python. I’m utilizing the library requests_cache to ensure I gained’t be repeating API calls and depleting the API limits.

import requests
import requests_cache
from dotenv import load_dotenv
import os

load_dotenv()
API_KEY = os.getenv("YT_API_KEY")

# Cache responses indefinitely
requests_cache.install_cache("youtube_cache", expire_after=None)

def get_channel_name(channel_id: str) -> str:
    url = (
        f"https://www.googleapis.com/youtube/v3/channels"
        f"?half=snippet&id={channel_id}&key={API_KEY}"
    )
    response = requests.get(url)
    information = response.json()

    attempt:
        return information.get("objects", [])[0].get("snippet", {}).get("title", "")
    besides (IndexError, AttributeError):
        return ""

In addition to this, I additionally created very related capabilities to get the nation and thumbnail of every channel, which will probably be helpful later. In the event you’re within the code, verify the GitHub repo.

On my DuckDB code, I’m now capable of register this Python operate and name them inside SQL! I simply have to be very cautious to all the time use them on aggregated and filtered information, in any other case, I can say bye-bye to my API quota.

# This the script created above
from youtube_api import get_channel_name

# Strive registering the operate, ignore if already exists
attempt:
    con.create_function('get_channel_name', get_channel_name, [str], str)
besides Exception as e:
    print(f"Skipping operate registration (presumably already exists): {e}")

# Get the channel names
channel_names = con.sql("""
    choose
        channelID,
        get_channel_name(channelID) as channel_name,
        video_count
    from top_5_channels
""")

A lot better! I seemed up two channels that I’m accustomed to on YouTube for a fast sanity verify. Linus Tech Ideas has a complete of seven.2k movies uploaded, with 2.3k current on this dataset. Players Nexus has 3k movies, with 700 within the dataset. Appears to be like ok for me!

The very last thing to do, earlier than transferring over to truly answering the query I set myself to reply, is to have an thought of the common length of movies. 

This matches my expectations, for probably the most half. I’m nonetheless a bit stunned by the quantity of 20-40-minute movies, as for a few years the “meta” was to have movies of 10 minutes to maximise YouTube’s personal adverts. 

Additionally, I believed these buckets of video durations used within the earlier graph had been fairly consultant of how I take into consideration video lengths, so I will probably be sticking with them for the subsequent sections.

For reference, that is the pandas code used to create these buckets.

video_lengths = con.sql("""
  SELECT DISTINCT ON (videoID)
      videoID,
      videoDuration
  FROM
      unique_segments
  WHERE
      videoID IS NOT NULL
      AND videoDuration > 0
"""
).df()

# Outline customized bins, in minutes
bins = [0, 3, 7, 12, 20, 40, 90, 180, 600, 9999999] 
labels = ["0-3", "3-7", "7-12", "12-20", "20-40", "40-90", "90-180", "180-600", "600+"]

# Assign every video to a bucket (trasnform length to min)
video_lengths["duration_bucket"] = pd.lower(video_lengths["videoDuration"] / 60, bins=bins, labels=labels, proper=False)

The large query. It will show if I’m being paranoid or not about everybody attempting to promote me one thing always. I’ll begin, although, by answering a less complicated query, which is the share of sponsors for various video durations.

My expectation is that shorter movies have the next share of their runtime from sponsors compared to longer movies. Let’s verify if that is truly the case.

CREATE TABLE video_total_ads AS
SELECT
    videoID,
    MAX(videoDuration) AS videoDuration,
    SUM(endTime - startTime) AS total_ad_duration,
    SUM(endTime - startTime) / 60 AS ad_minutes,
    SUM(endTime - startTime) / MAX(videoDuration) AS ad_percentage,
    MAX(videoDuration) / 60 AS video_duration_minutes
FROM
    unique_segments
WHERE
    videoDuration > 0
    AND videoDuration < 5400
    AND videoID IS NOT NULL
GROUP BY
    videoID

To maintain the visualization easy, I’m making use of related buckets, however solely as much as 90 minutes.

# Outline length buckets (in minutes, as much as 90min)
bins = [0, 3, 7, 12, 20, 30, 40, 60, 90]    
labels = ["0-3", "3-7", "7-12", "12-20", "20-30", "30-40", "40-60", "60-90"]

video_total_ads = video_total_ads.df()

# Apply the buckets once more
video_total_ads["duration_bucket"] = pd.lower(video_total_ads["videoDuration"] / 60, bins=bins, labels=labels, proper=False)

# Group by bucket and sum advert instances and complete durations
bucket_data = video_total_ads.groupby("duration_bucket")[["ad_minutes", "videoDuration"]].sum()

# Convert to share of complete video time
bucket_data["ad_percentage"] = (bucket_data["ad_minutes"] / (bucket_data["videoDuration"] / 60)) * 100
bucket_data["video_percentage"] = 100 - bucket_data["ad_percentage"]

As anticipated, for those who’re watching shorter-form content material on YouTube, then round 10% of it’s sponsored! Movies of 12–20 min in length have 6.5% of sponsors, whereas 20–30 min have solely 4.8%.

To maneuver ahead to the year-by-year evaluation I want to affix the sponsor instances with the videoInfo desk.

CREATE TABLE video_total_ads_joined AS
SELECT
    *
FROM
    video_total_ads
LEFT JOIN video_info ON video_total_ads.videoID = video_info.videoID

Subsequent, let’s simply verify what number of movies we’ve got per 12 months:

SELECT
    *,
    to_timestamp(NULLIF (revealed, 0)) AS published_date,
    extract(12 months FROM to_timestamp(NULLIF (revealed, 0))) AS published_year
FROM
    video_total_ads

Not good, not good in any respect. I’m not precisely positive why however there are numerous movies that didn’t have the timestamp recorded. It appears that evidently solely in 2021 and 2022 movies had been reliably saved with their revealed date.

I do have some concepts on how I can enhance this dataset with different public information, however it’s a really time-consuming course of and I’ll go away this for a future weblog submit. I don’t intend to accept a solution primarily based on restricted information, however for now, I must make do with what I’ve.

I selected to maintain the evaluation between the years 2018 and 2023, on condition that these years had extra information factors.

# Limiting the years as for these right here I've an honest quantity of knowledge.
start_year = 2018
end_year = 2023

plot_df = (
    video_total_ads_joined.df()
    .question(f"published_year >= {start_year} and published_year <= {end_year}")
    .groupby(["published_year", "duration_bucket"], as_index=False)
    [["ad_minutes", "video_duration_minutes"]]
    .sum()
)

# Calculate ad_percentage & content_percentage
plot_df["ad_percentage"] = (
    plot_df["ad_minutes"] / plot_df["video_duration_minutes"] * 100
)
plot_df["content_percentage"] = 100 - plot_df["ad_percentage"]

There’s a steep improve in advert share, particularly from 2020 to 2021, however afterward, it plateaus, particularly for longer movies. This makes numerous sense since throughout these years on-line commercial grew lots as folks spent increasingly more time at residence. 

For shorter movies, there does appear to be a rise from 2022 to 2023. However as the information is restricted, and I don’t have information for 2024, I can’t get a conclusive reply to this. 

Subsequent up, let’s transfer into questions that don’t rely upon the publishing date, this fashion I can work with a bigger portion of the dataset.

This can be a enjoyable one for me, as I ponder if the channels I actively watch are those that run probably the most adverts. 

Persevering with from the desk created beforehand, I can simply group the advert and video quantity by channel:

CREATE TABLE ad_percentage_per_channel AS
SELECT
    channelID,
    sum(ad_minutes) AS channel_total_ad_minutes,
    sum(videoDuration) / 60 AS channel_total_video_minutes
FROM
    video_total_ads_joined
GROUP BY
    channelID

I made a decision to filter for channels that had not less than half-hour of movies within the information, as a manner of eliminating outliers.

SELECT
    channelID,
    channel_total_video_minutes,
    channel_total_ad_minutes,
    channel_ad_percentage
FROM
    ad_percentage_per_channel
WHERE
    -- No less than half-hour of video
    channel_total_video_minutes > 1800
    AND channelID IS NOT NULL
ORDER BY
    channel_ad_percentage DESC
LIMIT 50

As shortly talked about earlier, I additionally created some capabilities to get the nation and thumbnail of channels. This allowed me to create this visualization.

I’m undecided if this stunned me or not. Among the channels on this checklist I watch very ceaselessly, particularly Gaveta (#31), a Brazilian YouTuber who covers films and movie modifying.

I additionally know that each he and Hall Crew (#32) do numerous self-sponsor, selling their very own content material and merchandise, so possibly that is additionally the case for different channels! 

In any case, the information appears good, and the odds appear to match my guide checks and private expertise.

I might like to know if channels that you just watch had been current on this checklist, and if it stunned you or not!

If you wish to see the High 150 Creators, subscribe to my free newsletter, as I will probably be publishing the complete checklist in addition to extra details about this evaluation in there!

Have you ever ever considered at which level of the video adverts work finest? Folks in all probability simply skip sponsor segments positioned initially, and simply transfer on and shut the video for these positioned on the finish.

From private expertise, I really feel that I’m extra prone to watch an advert if it performs across the center of a video, however I don’t suppose that is what creators do usually.

My aim, then, is to create a heatmap that exhibits the density of adverts throughout a video runtime. Doing this was surprisingly not apparent, and the answer that I discovered was so intelligent that it kinda blew my thoughts. Let me present you.

That is the information wanted for this evaluation. One row per advert, with the timestamp when every section begins and ends:

Step one is to normalize the intervals, e.g., I don’t care that an advert began at 63s, what I need to know is that if it began at 1% of the video runtime or 50% of the video runtime.

CREATE TABLE ad_intervals AS
SELECT
    videoID,
    startTime,
    endTime,
    videoDuration,
    startTime / videoDuration AS start_fraction,
    endTime / videoDuration AS end_fraction
FROM
    unique_segments
WHERE
    -- Simply to ensure we do not have unhealthy information
    videoID IS NOT NULL
    AND startTime >= 0
    AND endTime <= videoDuration
    AND startTime < endTime
    -- Lower than 40h
    AND videoDuration < 144000

Nice, now all intervals are comparable, however the issue is way from solved.

I need you to suppose, how would you clear up this? If I requested you “At 10% runtime out of all movies, what number of adverts are working?”

I don’t imagine that that is an apparent drawback to resolve. My first intuition was to create a bunch of buckets, after which, for every row, I might ask “Is there an advert working at 1% of the runtime? What about at 2%? And so forth…”

This appeared like a horrible thought, although. I wouldn’t have the ability to do it in SQL, and the code to resolve it might be extremely messy. In the long run, the implementation of the answer I discovered was remarkably easy, utilizing the Sweep Line Algorithm, which is an algorithm that’s usually utilized in programming interviews and puzzles.

I’ll present you ways I solved it however don’t fear for those who don’t perceive what is going on. I’ll share different sources so that you can study extra about it in a while.

The very first thing to do is to remodel every interval (startTime, endTime) into two occasions, one that can depend as +1 when the advert begins, and one other that can depend as -1 when the advert finishes. Afterward, simply order the dataset by the “begin time”.

CREATE TABLE ad_events AS
WITH unioned as (
  -- That is crucial step.
  SELECT
      videoID,
      start_fraction as fraction,
      1 as delta
  FROM ad_intervals
  UNION ALL
  SELECT
      videoID,
      end_fraction as fraction,
      -1 as delta
  FROM ad_intervals
), ordered AS (
  SELECT
      videoID,
      fraction,
      delta
  FROM ad_events
  ORDER BY fraction, delta
)
SELECT * FROM ordered

Now it’s already a lot simpler to see the trail ahead! All I’ve to do is use a working sum on the delta column, after which, at any level of the dataset, I can know what number of adverts are working! 

For instance, if from 0s to 10s three adverts began, however two of these additionally completed, I might have a delta of +3 after which -2, which implies that there’s just one advert at present working!

Going ahead, and to simplify the information a bit, I first around the fractions to 4 decimal factors and mixture them. This isn’t needed, however having too many rows was an issue when attempting to plot the information. Lastly, I divide the quantity of working adverts by the full quantity of movies, to have it as a share.

CREATE TABLE ad_counter AS 
WITH rounded_and_grouped AS (
  SELECT
      ROUND(fraction, 4) as fraction,
      SUM(delta) as delta
  FROM ad_events
  GROUP BY ROUND(fraction, 4)
  ORDER BY fraction
), running_sum AS (
  SELECT
      fraction,
      SUM(delta) OVER (ORDER BY fraction) as ad_counter
  FROM rounded_and_grouped
), density AS (
  SELECT
      fraction,
      ad_counter,
      ad_counter / (SELECT COUNT(DISTINCT videoID) FROM unique_segments_filtered) as density
  FROM running_sum
)
SELECT * FROM density

With this information not solely do I do know that initially of the movies (0.0% fraction), there are 69987 movies working adverts, this additionally represents 17% of all movies within the dataset.

Now I can lastly plot it as a heatmap:

As anticipated, the bumps on the extremities present that it’s far more frequent for channels to run adverts initially and finish of the video. It’s additionally fascinating that there’s a plateau across the center of the video, however then a drop, because the second half of the video is usually extra ad-free.

What I discovered humorous is that it’s apparently frequent for some movies to start out immediately with an advert. I couldn’t image this, so I manually checked 10 movies and it’s truly true… I’m undecided how consultant it’s, however many of the ones that I opened had been gaming-related and in Russian, they usually began straight with adverts!

Earlier than we transfer on to the conclusions, what did you consider the answer to this drawback? I used to be stunned at how easy was doing this with the Sweep Line trick. If you wish to know extra about it, I just lately revealed a blog post protecting some SQL Patterns, and the final one is strictly this drawback! Simply repackaged within the context of counting concurrent conferences.

Conclusion

I actually loved doing this evaluation for the reason that information feels very private to me, particularly as a result of I’ve been hooked on YouTube currently. I additionally really feel that the solutions I discovered had been fairly passable, not less than for probably the most half. To complete it off, let’s do a final recap!

Have Sponsor Segments Elevated Over the Years?

There was a transparent improve from 2020 to 2021. This was an impact that occurred all through all digital media and it’s clearly proven on this information. In newer years, I can’t say whether or not there was a rise or not, as I don’t have sufficient information to be assured. 

Which Channels Have the Highest Share of Sponsor Time Per Video?

I obtained to create a really convincing checklist of the High 50 channels that run the best quantity of adverts. And I found that a few of my favourite creators are those that spend probably the most period of time attempting to promote me one thing!

What’s the density of sponsor segments all through a video?

As anticipated, most individuals run adverts initially and the top of movies. In addition to this, numerous creators run adverts across the center of the video, making the second half barely extra ad-free. 

Additionally, there are YouTubers who instantly begin a video with adverts, which I believe it’s a loopy technique. 

Different Learnings and Subsequent Steps

I appreciated how clear the information was in displaying the share of adverts in several video sizes. Now I do know that I’m in all probability spending 5–6% of my time on YouTube watching adverts if I’m not skipping them since I largely watch movies which might be 10–20 min.

I’m nonetheless not totally comfortable although with the year-by-year evaluation. I’ve already seemed into different information and downloaded greater than 100 GB of YouTube metadata datasets. I’m assured that I can use it, along with the YouTube API, to fill some gaps and get a extra convincing reply to my query.

Visualization Code

You may need observed that I didn’t present snippets to plot the charts proven right here. This was on objective to make the weblog submit extra readable, as matplotlib code occupies numerous area.

You will discover all of the code in my GitHub repo, that manner you may copy my charts if you wish to.

That’s it for this one! I actually hope you loved studying this weblog submit and discovered one thing new!

In the event you’re interested in fascinating subjects that didn’t make it into this submit, or get pleasure from studying about information, subscribe to my free newsletter on Substack. I publish each time I’ve one thing genuinely fascinating to share.

Need to join straight or have questions? Attain out anytime at mtrentz.com.

All photos and animations by the creator until acknowledged in any other case.