70 lines
3.2 KiB
Markdown
70 lines
3.2 KiB
Markdown
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What Goes Into a Successful Reddit Post?
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======================================
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Here I'll pull and explore some data from Reddit
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I'll attempt to find out what about a Reddit post makes it successful using classification. In particular a Random Forest model and KNNeighbors model.
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Then I'll score the results and see what the highest predictors are
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To find what goes into making a successful Reddit post we'll have to do a few things, first of which is collecting data:
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## Introducing Scrapey!
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[Scrapey](scrapey.ipynb) takes a snapshot of [Reddit/r/all hot](https://www.reddit.com/r/all), and saves the data to a .csv file including a calculated age for each post about every 12 minutes. Run time is about 2 minutes per iteration and each time adds about 100 unique posts to the list while updating any post it's already seen.
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I run this in the background in a terminal and it updates my data set every ~12 minutes. I have records of all posts within about 12 minutes of them disappearing from /r/all.
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## EDA
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Chosen Features:
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* Title
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* Subreddit
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* Over_18
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* Is_Original_Content
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* Is_Self
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* Spoiler
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* Locked
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* Stickied
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* Num_Comments (Target)
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[EDA](EDA.ipynb)
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## Clean
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* Scale numeric features between 0-1
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* Convert '_' and '-' to whitespace
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* Remove any not a-z or A-Z or whitespace
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* Strip any leftover whitespace
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* Delete any titles that were reduced to empty strings
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[Clean](clean.ipynb)
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## Model
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* Split data to train/test samples
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* Test RandomForestClassifier, KNeighborsClassifier
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* My preferred is RandomForest
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[Model](model.ipynb)
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### Conclusion
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Long story short, if you want to karma farm then post OC memes and porn.
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Some Predictors from Top 25:
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* Is_Self
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* Subreddit_Memes
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* OC
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* Over_18
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* Subreddit_Shitposting
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* Is_Original_Content
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* Subreddit_Superstonk
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Popular words:
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'like', 'just', 'time', 'new', 'oc', 'good', 'got', 'day', 'today', 'im', 'dont', and 'love'.
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People on Reddit (at least in the past few days) like their memes, porn, and talking about their day. And it's preferred if the content is original and self posted.
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So yes, post your memes to memes and shitposting, tag them NSFW, use some words from the list, and rake in all that sweet karma!
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But it's not that simple, this is a fairly simple model, with simple data. To go beyond this I think the comments would have to be analyzed. Tokenization I thought would be the most influential piece, and I still think that thinking is correct. But in this case it doesn't apply because there is no real meaning to be had from reddit post titles, at least to a computer. There's a lot more seen by a human than just the text in the title, there's often an image attached, most posts reference a recent/current event, they could be an inside joke of sorts. For some posts there could be emojis in the title, and depending on their combination they can take on a meaning completely different from their individual meanings.
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The next step from here I believe is to analyze the comments section of these posts because in this moment I think that's the easiest way to truly describe the meaning of a post to a computer. With what was gathered here I'm only to get 10% above baseline and I think that's all there is to be had here, I mean we can tweak for a few percent probably but I don't think there's much left on the table.
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