Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Predicting Tomorrow's Headline using Today's Twitter Deliberations
by
Dandapat, Sourav Kumar
, Chandra, Joydeep
, Khatua, Apalak
, Chakraborty, Roshni
, Kharat, Abhijeet
in
Digital media
/ Mathematical models
/ News
/ Polarity
/ Social networks
2019
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Predicting Tomorrow's Headline using Today's Twitter Deliberations
by
Dandapat, Sourav Kumar
, Chandra, Joydeep
, Khatua, Apalak
, Chakraborty, Roshni
, Kharat, Abhijeet
in
Digital media
/ Mathematical models
/ News
/ Polarity
/ Social networks
2019
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Predicting Tomorrow's Headline using Today's Twitter Deliberations
Paper
Predicting Tomorrow's Headline using Today's Twitter Deliberations
2019
Request Book From Autostore
and Choose the Collection Method
Overview
Predicting the popularity of news article is a challenging task. Existing literature mostly focused on article contents and polarity to predict popularity. However, existing research has not considered the users' preference towards a particular article. Understanding users' preference is an important aspect for predicting the popularity of news articles. Hence, we consider the social media data, from the Twitter platform, to address this research gap. In our proposed model, we have considered the users' involvement as well as the users' reaction towards an article to predict the popularity of the article. In short, we are predicting tomorrow's headline by probing today's Twitter discussion. We have considered 300 political news article from the New York Post, and our proposed approach has outperformed other baseline models.
Publisher
Cornell University Library, arXiv.org
Subject
This website uses cookies to ensure you get the best experience on our website.