Skip to main content

Youtube Virality Analysis

INTRODUCTION

The aim of our project is to analyze the characteristics of viral videos given any topic.


The motivation behind it comes from two points of view :

Security

If known the characteristics of videos under sensitive topics like the Anti Muslim Videos or the Blue Whale Challenge, the government can strategize its mitigation topics efficiently as they can know where the videos are prone to affect more, which channels are more likely to post negative content, which users are likely to post provoking content etc. 

Youtube User 

Before uploading a video, people can know what characteristics are likely to make their video go viral like what type of words in the title are more likely to entice people to watch your video, should the description of your video be abstract or elaborated, what should be the length of your video etc.


APPLICATION OVERVIEW

We have built an application which when given a topic, analyzes most popular 50 videos under that topic taking into account the following features :
  • Content of the video(Positive Content or Negative Content)
  • Public Reactions to that video(Likes, Dislikes, Views)
  • Title
  • Description
  • Uploader
  • Verified channel or unverified
  • Duration
  • Comments of the video and their sentiment
The 50 most viral videos have been selected based on the following :
  • Likes/Views Ratio
  • Likes/Dislikes Ratio
The application works for any topic but we have done in-depth analysis on the following two topics :
  • Anti Muslim
  • Blue Whale

METHODOLOGY 


RESULTS AND OBSERVATIONS :

 We obtained a lot of graphs in our analysis, some of which are as under :

Polarity of videos(Negative Content or Positive Content)



Comments Sentiment Analysis


The main observations we recorded are as follows :
  • The videos of Blue Whale Challenge were much more popular than the Anti Muslim Videos. This can be attributed to the fact that it was a new topic and the Anti Muslim has been a topic of discussion for a long time now.
  • Around 93% of the comments under the Blue whale challenge were of positive polarity compared to only 56% in the Anti Muslim section. This suggests that the views of the people were united and positive in the case of Blue Whale and differed in the case of Anti Muslim. Analyzing the negative polarity videos, we found lots of negative and hatred borne comments against Islam and Muslims and blaming them for terrorism etc.
  • Out of the 50 videos, 4 were of negative content in the Blue Whale Challenge and 7 were of negative content in the Anti Muslim section.









Comments

Popular posts from this blog

White or Blue, the Whale gets its Vengeance: A Social Media Analysis of the Blue Whale Challenge

The Blue Whale Challenge - a set of tasks that must be completed in a duration of 50 days - is an online social media rage. The tasks of the “game” cause both physical and mental harm to the players; the final task is to take his/her own life. The tasks include waking up at odd hours, listening to psychedelic music, watching scary videos, inflicting cuts and wounds on their bodies and the final task is to commit suicide. The game is supposedly administered by people called “curators” who incite others to take the challenge, brainwash them to cause self harm and ultimately commit suicide. Most conversations between curators and players are suspected to take place via direct message but, in order to find curators, the players need a public platform where they can express their desire to play the game - knowingly or unknowingly. Online social media serves as this platform as people post about not just their desire to be a part of the game but also details and pictures of the various task…

Social Bot Detection on Twitch

Twitch is the leading world live streaming video platform for the Gamer’s community. It is a very famous networking site and has close to 100 million monthly unique users. Bots are very prominent on the network due to various financial favors that the gaming platform provides to a user. The main objective of our Project is Detecting Social Bots on Twitch using various techniques such as Meta-data Analysis, Sentiment analysis from Chats on a Channel, and classification using Machine learning.
We started by collecting usernames of 510 channels for which we compared chatters and viewers on that channels live video. We got 51 channels which had chatters>viewers. On those channels, we did Temporal analysis for over a period of 4 weeks. Alongside, we collected their metadata, such as, Follower, Followings, Status, Partner, and total views. We calculated a Score using these features, from which we could conclude that higher the score, higher the probability of an account being a Bot accoun…

Privacy Concerns on Tinder

Introduction
Mobile dating apps have become a popular means to meet potential partners. Mobile dating application such as Tinder have exploded in popularity in recent years. Most users on Tinder use/have used Facebook as their primary way to sign up. By doing this, Tinder automatically takes user information directly from Facebook, thus saving the need to authenticate the user and user details.  In this project we aim to identify a Tinder profile on Facebook using the information that tinder obtains from Facebook. Below is the information that Tinder takes from a user when they log in for the first time.