This repo represent an AI method to classify an article as clickbait or non-clickbait. To classify article we extract some features from both title and body paragraphs. We applied gradient boosting decision tree into these features and we measured accuracy of the model. Lastly, we create a web service that provide clickbait score of provided article by user. Using this web service, news application or web sites such as Bundle FlipBoard can provide better content to their users

https://clickbaitdetection.herokuapp.com/

/Training-> Extracting features from dataset and train them for machine learning models /performance_test-> Training extracted features with different machine learning methods to compare their performances /server-> codes for webservice. demo: https://clickbaitdetection.herokuapp.com/

With Curl curl -X POST -H 'Content-Type: application/x-www-form-urlencoded' -H 'charset: UTF-8' -d 'title=[title]&body=[body]' -v -i 'https://clickbaitdetection.herokuapp.com/classify.ai'

With Javascript:

    var title = document.getElementById("fname").value
    var body = document.getElementById("subject").value

    var xhr = new XMLHttpRequest();
    xhr.open("POST", "https://clickbaitdetection.herokuapp.com/classify.ai", true);
    xhr.setRequestHeader("Content-Type", "application/x-www-form-urlencoded; charset=UTF-8");
    xhr.send("title="+title+"&body="+body);

    xhr.onreadystatechange = function() { 
        if (xhr.readyState == 4 && xhr.status == 200) {
            console.log(xhr.responseText);
            result = document.getElementById("result")
            result.style.visibility= 'visible'
            if (xhr.responseText == "1") {
                //clicbait
            }
            else if (xhr.responseText == "0") {
                //non-clickbait
            }
            else {
                //server error
            }
        }
    }

1-> Clickbait 0->Non-clickbait -1->Server error (check input values)

Body text input should be more than five paragraphs.

0. Dataset: https://clickbait-challenge.org/#data

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