dc.contributor.author |
Agarwal, Aman |
|
dc.contributor.author |
Kumaraguru, Ponnurangam (Advisor) |
|
dc.date.accessioned |
2019-10-07T05:46:35Z |
|
dc.date.available |
2019-10-07T05:46:35Z |
|
dc.date.issued |
2018-11-22 |
|
dc.identifier.uri |
http://repository.iiitd.edu.in/xmlui/handle/123456789/744 |
|
dc.description.abstract |
Virality of online content on social networking websites is an important but esoteric phenomenon often studied in elds like marketing, psychology and data mining. In this
project, I have studied viral content from a computer vision and natural language perspective. I have introduced a new dataset from Twitter for studying virality and de ned an annotation score using Twitter metadata. I have also trained a deep learning Siamese model to predict virality of individual posts using relative virality in pairs of posts. |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
IIITD-Delhi |
en_US |
dc.subject |
Deep learning for the web |
en_US |
dc.subject |
Convolutional neural networks |
en_US |
dc.subject |
Image virality |
en_US |
dc.subject |
Image attributes |
en_US |
dc.subject |
Text virality |
en_US |
dc.title |
Virality of content on social media |
en_US |
dc.type |
Other |
en_US |