Clickbait Detection using Deep Learning with Tensorflow

Amol Agrawal (~amol2)


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Description:

Clickbait is content, especially that of a sensational or provocative nature, whose main purpose is to attract attention and draw visitors to a particular web page. Due to its nature, the presence of clickbait is increasing alarmingly in social media.

We gather data from a number of social media sites and annotate using three annotators. Then this data is fed into the deep learning model which is made by convolutional neural network and takes word2vec word embeddings as input. The model is able to classify the data into two categories with very good metrics.

This presentation is based on the research conducted by me which is soon to be published.

Prerequisites:

Machine Learning basics. A little knowledge about Convolutional Neural Networks will help.

Content URLs:

~ Will be added soon ~

Speaker Info:

Amol Agrawal currently works at Citrix R&D India. He is a NIT-A graduate with interests in Machine Learning and AI. He has also conducted workshops about Machine Learning as part of PythonExpress and BangPypers.

Speaker Links:

www.github.com/pfrcks

Section: Others
Type: Open space
Target Audience: Intermediate
Last Updated:

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