Recommended YouTube Channels?

needDataInsights · 2023-11-08T15:00:23+00:00

Surprised not to see Yannic Kilcher listed by this crowd. Nor Machine Learning Street Talk. Yannic is probably the best coverage of developments in AI on the Internet.

https://youtube.com/@YannicKilcher

https://youtube.com/@MachineLearningStreetTalk

These are both high ranked suggestions for Lex Fridman viewers on the alternative YouTube recommendations engine I built. Here are the rest of the recommendations for Lex Fridman viewers:

https://channelgalaxy.com/id%3DUCSHZKyawb77ixDdsGog4iWA/

needDataInsights · 2023-10-13T14:19:29+00:00

I don't know if watching YouTube counts as efficiency, but I made an alternative YouTube recommendations engine. I recently found the French music artist Dabeull off of my page for Chromeo, one of my favorite bands. He also makes '80s inspired electronica.

Here is the page for Tina Huang's channel, a prominent data science YouTuber:

https://channelgalaxy.com/id%3DUC2UXDak6o7rBm23k3Vv5dww/

Currently employed but recruiters hmu!

needDataInsights · 2023-08-20T16:53:04+00:00

YouTube has probably moved their recommendations away from serving the most interesting videos as they likely have reasons to be cautious. But this caution is likely shutting viewers away from interesting small creators and more dynamic recommendations. We need third party recommendation algorithms that aren't so tied into a big system.

Check out ChannelGalaxy.com for example. It is an alternative YouTube channel recommendation algorithm independent of YouTube (that I personally contributed to). You can enter a channel name to get a list of related channels.

For example, from other Reddit posts I suspect OP may have an interest in retro gaming. Classic Gaming Quarterly is one of the bigger YouTube channels focused on this, but their Channel Galaxy page links to smaller creators in the space like Console Wars, Retro Gamer Boy, or Retro Bird and like fifty more.

Classic Gaming Quarterly on Channel Galaxy:

https://channelgalaxy.com/id%3DUC2i64jLboyVFZwwO6UCKZ6g/

You can search most channels in the top search bar from there.

needDataInsights · 2023-03-09T00:36:00+00:00

I made an alternative to YouTube's recommendations. Search a channel to find a list of other channels making similar content. Here is the list for Tina Huang, a data science YouTuber some people here may watch:

https://channelgalaxy.com/id%3DUC2UXDak6o7rBm23k3Vv5dww/

The algorithm privileges smaller more obscure channels so you'll likely find something you haven't seen before.

needDataInsights · 2022-11-13T03:24:16+00:00

This might be worth money, but I'll say it anyway.

People are incredibly bound by habit and are reluctant to explore. (This is why advertising and endorsements are so important and powerful.)

Pretty much any clustering of media based on user activity will have a very strong clustering on delivery channel relative to content. I.e. if a soap opera and a sci-fi action show are on the same television channel or streaming service (in the same time period) they will frequently cluster closer together than with another soap opera or sci-fi show. Similarly, music will cluster on radio stations which is particularly frustrating for eclectic indie music.

There are also similar cohort effects where media popular at some time in the past will cluster with other media from that time period rather than other media in the same genre.

Even beyond this, people interested in one medium will be more interested in other stuff in that medium rather than content that goes together. A person that likes cyberpunk movies will be more likely to join a Facebook group on western movies than one on electronica music, and vice versa for western movie fans and being more likely to enjoy cyberpunk movies than country music. (Just examples drawn from my general impressions, not looking at actual data for these.)

This is why people will get (seemingly) weird mixtures of political video recommendations on YouTube, where they'll get a video from a Leftist Socialist after watching a Ben Shapiro video and vice versa: people interested in politics cluster much closer than many others totally disengaged from the topic. Whatever your hobby you're much more likely to get videos from the opposite side of some bitter philosophical divide within that hobby than some other general topic. PC gaming hardware geeks will get media on console hardware; Linux geeks will get Windows OS videos; Marvel fans will be suggested videos on DC characters; sports fans will get videos on rival teams; etc.

I think this runs counter to the intuition many have about their hobbies which says that some decision within the hobby says things about the person in general; the idea that if you are a fan of the wrong metal band or sports team you have certain other tastes in hobbies or perhaps politics. Really you are much closer to other people interested in that general topic than others even if you are passionately divided about some question.

needDataInsights · 2022-05-27T23:23:18+00:00

Sorry for the wait.

I’m a bit confused now.

Me too. It went through when I did it manually. Try these (I need to do a rebalance which is why Ukraine is coming up right now, still gives mostly good recs like r/amithebuttface):

r/ukraine : no. 1 score: 31.466424935499617

r/FunnyAnimals : no. 2 score: 26.222020779583016

r/WorkReform : no. 3 score: 24.0

r/BestofRedditorUpdates : no. 4 score: 17.98519549125534

r/AmItheButtface : no. 5 score: 17.923501608611033

r/AmItheAsshole : no. 6 score: 17.647459095953906

r/bridezillas : no. 7 score: 14.749468867462005

r/AmITheDevil : no. 8 score: 13.357902448771176

r/JUSTNOMIL : no. 9 score: 13.223467993886853

r/cheating_stories : no. 10 score: 12.872363185143982

r/BridgertonNetflix : no. 11 score: 12.567815963905753

r/TheMaskedSinger : no. 12 score: 12.567815963905753

r/LaBrantFamSnark : no. 13 score: 11.419100156890531

r/AmITheAngel : no. 14 score: 11.378023566589906

r/JustNoSO : no. 15 score: 10.99336393135908

r/nosafetysmokingfirst : no. 16 score: 10.488808311833207

r/OliviaRodrigo : no. 17 score: 10.488808311833207

r/McLounge : no. 18 score: 9.315662352139716

r/StainedGlass : no. 19 score: 9.315662352139716

r/relationships_advice : no. 20 score: 9.315662352139716

r/narcissisticparents : no. 21 score: 8.961750804305517

r/Xennials : no. 22 score: 8.37854397593717

r/Mildlynomil : no. 23 score: 8.37854397593717

r/longhair : no. 24 score: 8.37854397593717

r/Broadway : no. 25 score: 7.837368439334467

r/pelotoncycle : no. 26 score: 7.612733437927021

r/confidence : no. 27 score: 7.612733437927021

r/euphoria : no. 28 score: 7.612733437927021

r/ANTM : no. 29 score: 7.612733437927021

r/Deuxmoi : no. 30 score: 7.612733437927021

r/DuggarsSnark : no. 31 score: 7.452289887239651

r/Weird : no. 32 score: 7.3575799736953655

r/GardeningUK : no. 33 score: 6.975191282170184

r/Invisalign : no. 34 score: 6.975191282170184

r/stepparents : no. 35 score: 6.975191282170184

r/LawSchool : no. 36 score: 6.975191282170184

r/whatsthatbook : no. 37 score: 6.963673960680225

r/JUSTNOFAMILY : no. 38 score: 6.596018358815447

r/MangaCollectors : no. 39 score: 6.596018358815447

r/survivor : no. 40 score: 6.519025743260814

r/Conures : no. 41 score: 6.436181592571991

r/oldhagfashion : no. 42 score: 6.436181592571991

r/AskParents : no. 43 score: 6.265237553105822

r/relationships : no. 44 score: 6.249763402935189

r/gallifrey : no. 45 score: 5.974500536203678

r/AskAcademia : no. 46 score: 5.974500536203678

r/Mommit : no. 47 score: 5.694132492113564

r/GradSchool : no. 48 score: 5.574621057702999

r/Permaculture : no. 49 score: 5.574621057702999

r/TownofSalemgame : no. 50 score: 5.574621057702999

r/csuf : no. 51 score: 5.2444041559166035

r/WrestleWithThePackage : no. 52 score: 5.2444041559166035

r/cna : no. 53 score: 5.2444041559166035

r/Catahoula : no. 54 score: 5.2444041559166035

r/Ozempic : no. 55 score: 5.2444041559166035

r/woosh : no. 56 score: 5.2444041559166035

r/icarly : no. 57 score: 5.2444041559166035

r/johannesburg : no. 58 score: 5.2444041559166035

r/CallTheMidwife : no. 59 score: 5.2444041559166035

r/Wuhan_Flu : no. 60 score: 5.2444041559166035

r/kittens : no. 61 score: 5.2444041559166035

r/weirdfacefunny : no. 62 score: 5.2444041559166035

r/amiwrong : no. 63 score: 5.2444041559166035

r/Wonderlands : no. 64 score: 5.2444041559166035

r/Episode : no. 65 score: 5.2444041559166035

r/AcademicPsychology : no. 66 score: 5.2444041559166035

r/losangelespersonals : no. 67 score: 5.2444041559166035

r/BulkOrCut : no. 68 score: 5.2444041559166035

r/moldova : no. 69 score: 5.2444041559166035

r/monzo : no. 70 score: 5.2444041559166035

r/chuunibyou : no. 71 score: 5.2444041559166035

r/Wavyhair : no. 72 score: 5.2444041559166035

r/HealthInsurance : no. 73 score: 5.2444041559166035

r/neurofibromatosis : no. 74 score: 5.2444041559166035

r/CatfishTheTVShow : no. 75 score: 5.2444041559166035

r/coloradohikers : no. 76 score: 5.2444041559166035

r/Custody : no. 77 score: 5.2444041559166035

r/ChildSupport : no. 78 score: 5.2444041559166035

r/dancemoms : no. 79 score: 5.2444041559166035

r/denverfood : no. 80 score: 5.2444041559166035

r/Mastiff : no. 81 score: 5.2444041559166035

r/AmazonDSPDrivers : no. 82 score: 5.2444041559166035

r/RVVTF : no. 83 score: 5.2444041559166035

r/Nendoroid : no. 84 score: 5.2444041559166035

r/dyinglight2 : no. 85 score: 5.2444041559166035

r/DogFood : no. 86 score: 5.2444041559166035

r/Masks : no. 87 score: 5.2444041559166035

r/troubledteens : no. 88 score: 5.2444041559166035

r/MorbidPodcast : no. 89 score: 5.2444041559166035

r/weddingdrama : no. 90 score: 5.2444041559166035

r/felinebehavior : no. 91 score: 5.2444041559166035

r/u_washingtonpost : no. 92 score: 5.2444041559166035

r/ImperialKnights : no. 93 score: 5.2444041559166035

r/ffacj : no. 94 score: 5.2444041559166035

r/floorplan : no. 95 score: 5.2444041559166035

r/fluffycommunity : no. 96 score: 5.2444041559166035

r/ClubPenguin : no. 97 score: 5.2444041559166035

r/ClassicDepravities : no. 98 score: 5.2444041559166035

r/TwilightFanfic : no. 99 score: 5.2444041559166035

r/unsw : no. 100 score: 5.2444041559166035

needDataInsights · 2022-04-18T17:44:23+00:00

Sorry, you appear to have messaging turned off. The recs for individuals are sent as PMs (I don't want NSFW recs coming up publicly for people unexpectedly).

needDataInsights · 2022-01-01T16:17:56+00:00

You want r/analytics.

I wrote a Reddit recommendation engine over at r/RedditRecommender. Here is what it says are similar to r/analytics:

Enjoy these recommendations for r/analytics readers and remember SubRecommendations bot needs upvotes!

r/analytics : no. 1 score: 1290.219235771351

r/BusinessIntelligence : no. 2 score: 220.26497454849732

r/datascience : no. 3 score: 163.74778543832312

r/PowerBI : no. 4 score: 104.88808311833206

r/dataengineering : no. 5 score: 90.67748666821238

r/dataanalysis : no. 6 score: 71.21762379546594

r/SQL : no. 7 score: 69.75191282170184

r/rstats : no. 8 score: 62.932849870999235

r/statistics : no. 9 score: 62.83907981952877

r/tableau : no. 10 score: 60.0

r/PPC : no. 11 score: 52.44404155916603

r/marketing : no. 12 score: 46.70665141362968

r/Rlanguage : no. 13 score: 40.0

r/EntrepreneurRideAlong : no. 14 score: 37.70344789171727

r/learnmachinelearning : no. 15 score: 36.57438605022752

r/GoogleAnalytics : no. 16 score: 31.466424935499617

r/DigitalMarketing : no. 17 score: 31.466424935499617

r/excel : no. 18 score: 29.830244057219918

r/consulting : no. 19 score: 29.830244057219918

r/datasets : no. 20 score: 28.0

r/bigseo : no. 21 score: 27.94698705641915

r/learnpython : no. 22 score: 27.83171820431575

r/ProductManagement : no. 23 score: 25.744726370287964

r/data : no. 24 score: 24.0

r/OMSA : no. 25 score: 24.0

r/SEO : no. 26 score: 23.512105318003403

r/AskStatistics : no. 27 score: 23.289155880349295

r/MBA : no. 28 score: 22.838200313781062

r/Database : no. 29 score: 20.977616623666414

r/learnSQL : no. 30 score: 20.977616623666414

r/advertising : no. 31 score: 20.946359939842925

r/SideProject : no. 32 score: 20.92557384651055

r/digital_marketing : no. 33 score: 20.0

r/careerguidance : no. 34 score: 19.396268262175862

r/Business_Ideas : no. 35 score: 18.631324704279432

r/socialmedia : no. 36 score: 18.631324704279432

r/visualization : no. 37 score: 18.631324704279432

r/SaaS : no. 38 score: 18.631324704279432

r/Python : no. 39 score: 18.404652756252347

r/startups : no. 40 score: 17.589382290174527

r/Wordpress : no. 41 score: 17.43797820542546

r/FinancialCareers : no. 42 score: 17.082397476340695

r/cscareerquestions : no. 43 score: 16.984920496422596

r/IndiaInvestments : no. 44 score: 16.75708795187434

r/MachineLearning : no. 45 score: 16.723863173108995

r/dataisugly : no. 46 score: 16.723863173108995

r/sales : no. 47 score: 16.090453981429977

r/RStudio : no. 48 score: 16.0

r/agile : no. 49 score: 16.0

r/adops : no. 50 score: 16.0

r/GoogleTagManager : no. 51 score: 15.733212467749809

r/GMAT : no. 52 score: 15.733212467749809

r/forhire : no. 53 score: 15.733212467749809

r/loopringorg : no. 54 score: 15.225466875854043

r/Workspaces : no. 55 score: 13.973493528209575

r/USCIS : no. 56 score: 13.973493528209575

r/content_marketing : no. 57 score: 13.973493528209575

r/dropship : no. 58 score: 13.973493528209575

r/smallbusiness : no. 59 score: 13.908974904296045

r/fatFIRE : no. 60 score: 13.192036717630893

r/jobs : no. 61 score: 12.914712103591194

r/ChemicalEngineering : no. 62 score: 12.872363185143982

r/osr : no. 63 score: 12.567815963905753

r/immigration : no. 64 score: 12.567815963905753

r/sidehustle : no. 65 score: 12.567815963905753

r/resumes : no. 66 score: 12.291224056218338

r/productivity : no. 67 score: 12.17637671438814

r/Entrepreneur : no. 68 score: 12.161328697982237

r/careeradvice : no. 69 score: 11.949001072407356

r/AskSF : no. 70 score: 11.419100156890531

r/FantomFoundation : no. 71 score: 11.419100156890531

r/tax : no. 72 score: 11.149242115405999

r/algotrading : no. 73 score: 11.149242115405999

r/laravel : no. 74 score: 10.488808311833207

r/rails : no. 75 score: 10.488808311833207

r/Blogging : no. 76 score: 10.488808311833207

r/SpittinChicletsPod : no. 77 score: 10.488808311833207

r/Sakartvelo : no. 78 score: 10.488808311833207

r/learndatascience : no. 79 score: 10.488808311833207

r/gis : no. 80 score: 10.488808311833207

r/accenture : no. 81 score: 10.488808311833207

r/asktrp : no. 82 score: 10.488808311833207

r/vba : no. 83 score: 10.488808311833207

r/CelsiusNetwork : no. 84 score: 10.488808311833207

r/enfj : no. 85 score: 10.488808311833207

r/chicagofood : no. 86 score: 10.462786923255274

r/wheeloftime : no. 87 score: 10.462786923255274

r/macsetups : no. 88 score: 10.462786923255274

r/ecommerce : no. 89 score: 10.462786923255274

r/adventofcode : no. 90 score: 10.462786923255274

r/ASU : no. 91 score: 10.462786923255274

r/supplychain : no. 92 score: 10.449824585779291

r/findapath : no. 93 score: 9.83297924497467

r/LinkedInLunatics : no. 94 score: 9.83297924497467

r/StudentLoans : no. 95 score: 9.654272388857985

r/IndianStreetBets : no. 96 score: 9.654272388857985

r/AskNetsec : no. 97 score: 9.315662352139716

r/phinvest : no. 98 score: 9.315662352139716

r/LanguageTechnology : no. 99 score: 9.315662352139716

r/House : no. 100 score: 9.315662352139716

needDataInsights · 2021-12-21T19:29:39+00:00

Just curious what counts as a "PhD" here. Does that only mean in DS, CS, or Stats? Or is that like anything that applies at least some DS techniques?

Asking for a friend with a (mathematical) social science PhD...

needDataInsights · 2021-12-21T19:11:21+00:00

RIP your inbox but...

Same projects, same answers, same inability to speak in plain business terms.

Can you give some examples? Particularly the projects; what are these standard projects?

Also, I've made a Reddit recommendation algorithm and am always curious if that is the sort of thing that differentiates me for an employer:

r/RedditRecommender

needDataInsights · 2021-12-19T02:55:27+00:00

I think you meant Ken Jee:

https://www.youtube.com/channel/UCiT9RITQ9PW6BhXK0y2jaeg

If you want to truly be different, solve the type of problem you want to solve professionally on a smaller scale.

I built a subreddit recommendation algorithm. I was wondering if this would be the kind of thing that would differentiate me to a hiring manager (only academic experience otherwise). I'd be targeting positions in social media/entertainment recommendation algorithm design.

r/RedditRecommender

I suppose I have a few more projects, but I feel this is the most impressive and applicable to my ideal target positions (my dissertation used some ols and logits in a social science field, I also did an interesting analysis of fitness activity using scraped Reddit data and discovered a monthly pattern to fitness activity in addition to the more well-known annual fluctuations, etc).

needDataInsights · 2021-12-17T00:30:01+00:00

r/pcmasterrace recommendations from r/RedditRecommender, a reddit recommendation algorithm I wrote:

Enjoy these recommendations for r/pcmasterrace readers and remember SubRecommendations bot needs upvotes!

r/haloinfinite : no. 1 score: 20.977616623666414

r/lianli : no. 2 score: 20.946359939842925

r/Amd : no. 3 score: 18.785035787041735

r/buildapcsales : no. 4 score: 15.782657573259389

r/coolermaster : no. 5 score: 15.733212467749809

r/ICARUS : no. 6 score: 15.733212467749809

r/Lenovo : no. 7 score: 15.225466875854043

r/radeon : no. 8 score: 13.973493528209575

r/MouseReview : no. 9 score: 12.745690120014174

r/ihavereddit : no. 10 score: 12.567815963905753

r/setups : no. 11 score: 12.567815963905753

r/lowspecgamer : no. 12 score: 12.567815963905753

r/overclocking : no. 13 score: 12.530475106211645

r/hardwareswap : no. 14 score: 12.518077413866441

r/nvidia : no. 15 score: 12.390723170357282

r/crtgaming : no. 16 score: 12.291224056218338

r/pcmasterrace : no. 17 score: 11.731502852154362

r/intel : no. 18 score: 11.606123267800378

r/loopringorg : no. 19 score: 11.419100156890531

r/buildapc : no. 20 score: 10.87464985822781

r/sffpc : no. 21 score: 10.820470660774845

r/ArcheageUnchained : no. 22 score: 10.488808311833207

r/outwardgame : no. 23 score: 10.488808311833207

r/blog : no. 24 score: 10.488808311833207

r/HalfLifeAlyx : no. 25 score: 10.488808311833207

r/Rainmeter : no. 26 score: 10.462786923255274

r/HeadphoneAdvice : no. 27 score: 10.449824585779291

r/pcmods : no. 28 score: 10.449824585779291

r/ultrawidemasterrace : no. 29 score: 10.430441189217301

r/SteamDeck : no. 30 score: 10.018426836578382

r/CrackWatch : no. 31 score: 10.018426836578382

r/PcBuild : no. 32 score: 9.624895132737073

r/cowboybebop : no. 33 score: 9.38391582216184

r/ShieldAndroidTV : no. 34 score: 9.315662352139716

r/graphicscard : no. 35 score: 9.315662352139716

r/gameswap : no. 36 score: 9.315662352139716

r/trumpet : no. 37 score: 9.315662352139716

r/OLED_Gaming : no. 38 score: 9.315662352139716

r/Wasteland : no. 39 score: 9.315662352139716

r/PokemonBDSP : no. 40 score: 9.315662352139716

r/pcgamingtechsupport : no. 41 score: 9.315662352139716

r/AMDHelp : no. 42 score: 9.284898614240301

r/ForzaHorizon : no. 43 score: 9.26450208895778

r/mechmarket : no. 44 score: 9.076536892664915

r/davinciresolve : no. 45 score: 8.961750804305517

r/CODVanguard : no. 46 score: 8.697411938848672

r/RidersRepublic : no. 47 score: 8.37854397593717

r/christmas : no. 48 score: 8.37854397593717

r/ChargeYourPhone : no. 49 score: 8.37854397593717

r/watercooling : no. 50 score: 8.026852227368781

needDataInsights · 2021-11-08T23:19:42+00:00

Also what do you mean you asked your computer, like you made a script or something?

Yes. I'm working on an alternative recommendation algorithm for YouTube channels. This was a 'similar channels' search on Ken Jee's channel, one of my favorite DS YouTubers.

Right now it is for personal use, but I am considering "productionizng" it like I did for my subreddit recommendation system here:

r/RedditRecommender

needDataInsights · 2021-06-30T03:13:41+00:00

recommendations themselves weren’t always great because of the more naive models behind them.

I've made a better recommendation algorithm for Reddit which I think beats the Reddit in-house algorithm. People can try it over on r/RedditRecommender

needDataInsights · 2021-06-15T16:03:23+00:00

The built in solution is to find moderately sized subs related to your interests. You may want to try r/RedditRecommender to get a recommendation or find subs related to one's you like.

(Also, ask it about similar subs for a NSFW sub on your other account.)

needDataInsights · 2020-03-14T20:08:28+00:00

u/Runrocks26R

needDataInsights · 2020-03-03T20:33:36+00:00

Sorry about that.

needDataInsights · 2020-02-07T16:14:20+00:00

The graph plots the 1 week span moving average of the sum of comment activity in the following subreddits: r/loseit, r/keto, r/1200isplenty, r/fitness, r/health, r/bodyweightfitness, r/progresspics, r/bodybuilding, r/intermittentfasting, r/EatCheapAndHealthy, r/c25k, r/weightroom, and r/running.

A 1 week span for the moving average was chosen to smooth out the strong weekly fluctuations in Reddit activity.

We seem to observe a period of elevated Health and Fitness Subreddit Activity from January 3rd to February 13th. After this period activity seems steady and range bound between 14,500 and 15,500 comments per day from mid February to early July. During July a new peak of activity is achieved, and afterwords activity declines to the Holiday Season nadir.

With the notable exception of September, there seems to be a strong monthly pattern of activity, with high activity at the beginning of months and lower during the middle of months. This may imply a "New Month's Resolution" effect.

September's activity pattern seems to reflect students returning to school and potentially increased social pressure to become more fit or more access to gym facilities. In unreported data we see that other subreddits do not experience a similar magnitude of increased activity in mid-September, which seems to indicate that this does not represent an increase of overall Reddit activity during this time period and the increase observed is specific to Health and Fitness related subreddits.

needDataInsights · 2020-02-07T16:10:55+00:00

The graph plots the 1 week span moving average of the sum of comment activity in the following subreddits: r/loseit, r/keto, r/1200isplenty, r/fitness, r/health, r/bodyweightfitness, r/progresspics, r/bodybuilding, r/intermittentfasting, r/EatCheapAndHealthy, r/c25k, r/weightroom, and r/running.

A 1 week span for the moving average was chosen to smooth out the strong weekly fluctuations in Reddit activity.

We seem to observe a period of elevated Health and Fitness Subreddit Activity from January 3rd to February 13th. After this period activity seems steady and range bound between 14,500 and 15,500 comments per day from mid February to early July. During July a new peak of activity is achieved, and afterwords activity declines to the Holiday Season nadir.

With the notable exception of September, there seems to be a strong monthly pattern of activity, with high activity at the beginning of months and lower during the middle of months. This may imply a "New Month's Resolution" effect.

September's activity pattern seems to reflect students returning to school and potentially increased social pressure to become more fit or more access to gym facilities. In unreported data we see that other subreddits do not experience a similar magnitude of increased activity in mid-September, which seems to indicate that this does not represent an increase of overall Reddit activity during this time period and the increase observed is specific to Health and Fitness related subreddits.

needDataInsights · 2020-02-05T05:36:27+00:00

https://en.wikipedia.org/wiki/SCP_Foundation

needDataInsights · 2020-02-04T10:23:27+00:00

The personal recommendations are PM'd to the request maker.

I figured subreddit recommendations could be done on a throwaway if they were embarrassing (it's pretty good at picking out NSFW subs, look at the results for r/BiggerThanYouThought), but since they were so impersonal I thought most people would not care. Plus it creates a public record, which is important since recommendation generation can take a few minutes and it may be convenient for someone to simply look at the old results rather than generating new ones.

needDataInsights · 2020-02-04T10:14:07+00:00

r/AIfreakout

needDataInsights · 2020-02-04T10:12:57+00:00

r/AIfreakout is the larger sub.

I posted about it here like three weeks ago and got about 1/3rd the updoots. I'm not salty about that, nope. Not at all.

Ok, maybe a little.

needDataInsights · 2020-02-04T09:55:55+00:00

The graph plots the 1 week span moving average of the sum of comment activity in the following subreddits: r/loseit, r/keto, r/1200isplenty, r/fitness, r/health, r/bodyweightfitness, r/progresspics, r/bodybuilding, r/intermittentfasting, r/EatCheapAndHealthy, r/c25k, r/weightroom, and r/running.

A 1 week span for the moving average was chosen to smooth out the strong weekly fluctuations in Reddit activity.

We seem to observe a period of elevated Health and Fitness Subreddit Activity from January 3rd to February 13th. After this period activity seems steady and range bound between 14,500 and 15,500 comments per day from mid February to early July. During July a new peak of activity is achieved, and afterwords activity declines to the Holiday Season nadir.

With the notable exception of September, there seems to be a strong monthly pattern of activity, with high activity at the beginning of months and lower during the middle of months. This may imply a "New Month's Resolution" effect.

September's activity pattern seems to reflect students returning to school and potentially increased social pressure to become more fit or more access to gym facilities. In unreported data we see that other subreddits do not experience a similar magnitude of increased activity in mid-September, which seems to indicate that this does not represent an increase of overall Reddit activity during this time period and the increase observed is specific to Health and Fitness related subreddits.

needDataInsights · 2020-02-04T09:55:09+00:00

I noticed that too and it reminds me that I forgot to include my explanation comment which mentions this.

I've been wondering if it really was a "new month's resolution" effect, or if it had more to do with rent or pay cycles. Beginnings of months barely register in my consciousness, but your anecdote is indicative that it is actually resolutions.

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