Wormbait

Wormbait is a simple knn-like algo to identify clickbait titles.

What's so special about it ? Wormbait is trained with 1040 Inc.com titles, because besides having great articles, they have some of the most clickbaity titles out there.

Why is it called wormbait ?

• 1. Click ==> worms
• 1. I built [fishingrod])(http://npmjs.org/package/fishingrod) so obviously you need bait and worms

CLI

Wormbait is bunbled with a very small CLI tool

Installation

npm install -g wormbait

Usage

$ wormbait "this is not clickbait" "deep fried chicken" Learning from 1040 sentences Finished Learning! YES: "this is not clickbait" is PROBABLY clickbait with a score of 0.2942307692307693 NO: "deep fried chicken" is PROBABLY NOT clickbait with a score of 0.013565

API

Wormbait is a class you can instanciate and provides some utility methods

Installation

npm install wormbait

tl;dr

const Wormbait = require('wormbait');
let inc = new Wormbait();
 
inc.learn();
let is_bait = inc.predict('This is some title of some article');
 
if(is_bait){
    console.log('CLICKBAIT!!!')
}

Methods

getDataSet()

Returns the data set bundled with wormbait, with over 1000 Inc.com article titles.

DataSet is simply an array filled with strings.

learn(data)

data: Array with strings

Learns from the provided data. Simple example:

let data = w.getDataSet().splice(0, 800);
w.learn(data);

predict(title)

title: String

Returns boolean predicting if clickbait or not depending on data learned from.

test(data)

data: Array with LearnObject

LearnObject:

{
    title: 'This is some title to test against',
    clickbait: true
}

Tests the learning algorithm, and returns predictions and statistics

{
    predictions: [{
        score: 0.005,
        val: true,
        correct: false
    }],
    ratio: 0.8,
    correct: 80,
    wrong: 20
}

__predict(title)

Returns the associated score (0 to 1)

setThreshold(t)

Sets the minimum threshold to identify title as clickbait. Default is 0.2