Youtube Videos – Analysis of Uploads

Task 1: Background Information

The ABC Online multimedia company came into form in 2015 and has been providing multimedia insights for its clients. In order to provide detailed insights and details about different videos, the total views, comments, likes and dislikes and time-frame of the uploaded videos during the period from 2006 to 2018.

The dataset is provided to the content analyst by Kaggle and is gathered in the excel form. The dataset includes the information regarding video id, number of likes, number of dislikes, comments, publishing date, publishing day, video title, tags, channel titles, video errors and ratings. The dataset contains 161470 data points over 17 different columns.

By providing access to such data, the mission of Content analyst at ABC Online Multimedia Company is to provide different analysis on the peak hours, peak days, most liked videos, most disliked videos and peak hours at which videos are uploaded. The aim of this report is to use analyze the given dataset by using analytical tool i.e. IBM Watson Analytics for exploring, analyzing and visualizing the given dataset. There are different questions that are explored along with advanced insights. The data is downloaded from data world and Kaggle.com. The major aim of this report are:

• To provide logical recommendations to the company for enhancing the multimedia operations
• Assisting the company in achieving its operational/strategic objectives
• Providing the company with advanced and basic insights into the data of uploaded videos

Task 2: Reporting/ Screen Shots of the Dashboard and Chats

There are total 55885 number of videos in the given data set with 18 different types of categories for 4 countries namely Canada, France, USA and Great Britain (GB). In the given data set, there are around 12360 number of unique channels.

According to the given dataset, the Watson analytics showed that France is the top most country with 6678 numbers of channels followed by Canada (5076) and USA (2207) see figure 1 below.

Figure 1: Top 3 Countries in Terms of Channels

While the lowest number of channels are of Great Britain i.e. 1624 (see figure 2 below). In the given data set, USA has only 2207 number of channels.

Figure 2: Lowest Number of Channels of Countries

The detailed observation showed the different titles of different countries. In the figures and tables below, top 10 most viewed video titles can be seen.  

Task 3: Advanced Insights

Insight 1: What Drives Likes?

Figure 13: Drivers of Likes

The advanced insight showed that the likes are derived 31% by comment count and category id, 28% by comment count and views and 28% by dislikes and category id. Comment Count, views and dislikes are the top three single drivers of likes of the videos with 21%, 21% and 19% of predicting power.

Insight 2: What is Predictive Model for Comments by Dislikes

The decision tree showed that likes and 6 other inputs (dislikes, time frame, category id and published day) decides the comment count for each video (Wirga, 2015).

Figure 14: Predictive Model for Comments by Dislikes

Insight 3: What is the Contribution of Likes Over Year by Category Id?

Figure 15: Likes by Category Id

The graph above shows that during previous three years, most of the liked videos belongs to category Music with id of 10 followed by Entertainment with id of 24 and Comedy with id of 23. Music has remained to be the top most viewed content on Youtube recently (Cayari, 2011).

 

 

Insight 4: How Do the Dislikes Values are Compared with Disabled Comments?

Figure 16: Comments Disabled and Dislikes

The insight shows that when the comments are disabled, lesser videos are disliked while with the open comments, more videos are disliked by the viewers.

Insight 5: What Drives Comment Count?

The insight shows that the driver of comment count includes dislikes & category id (14%), likes and category id (13%), views and category id (12%), dislikes and time frame (12%) and likes (10%). These drivers are also predicted by other research conducted by Bessi in 2016.

Figure 17: Predictive Model for Comment Counts

Task 4: Research

Assumptions:

Dataset was processed under these assumptions:

• There are four countries given for the uploaded videos
• All seven days of the week are chosen for uploaded videos

Dashboard 1

This dashboard shows the values of views by each country and its category ID. This dashboard will help the content analyst to see which country has the most views in terms of the categories and the publishing months as well (Hoyt, 2016). It is revealed that the mostly viewed category is music for every country with April being the mostly viewed month and 990% of views made in 2018. The same results were found by the research conducted for deeply analyzing the driver of comments (Schultes, Dorner, & Lehner, 2013).

Dashboard 2

The purpose of this dashboard is to allow the content analyst to see how the comment counts are predicted. A mix of bar charts, predictive decision tree and spirals are used for predicting the model of comments (Lange, 2007). It is observed that likes, opened comments and category ids drive the number of comments for each video (Wen, 2016). It also means that when viewers can see the comments of other viewers, they find it more lucrative to dislike the videos based on others’ comments instead of basing their likes and dislikes on their watching or viewing experience (Cheng, Dale, & Liu, 2012). Moreover, the comments are more made by the viewers who like the content in the video as shown in the above dashboard (Walther, DeAndrea, & Kim, 2015).

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Task 5: Recommendations

Based on the above analysis, following recommendations are made to ABC Online Multimedia Company.

1. It is recommended to disable the comments section for each uploaded video as by disabling the comments section, lesser number of viewers click on dislike option of the video(Madden, Ruthven, & McMenemy, 2013).
2. It is observed that people like music videos more since highest number of uploaded videos over past 3 years have remained to be related to music(Edmond, 2014). Entertainment and politics remain to be the other two most liked categories. Hence, for driving more likes and views, the company is recommended to upload music related videos.
3. It is also seen that movies are uploaded very rarely and are lesser viewed by the viewers. It is because of the piracy issues related to movies industry(Choi, 2007). Hence, it is recommended that for avoiding the litigation by the movie houses and publishers, the company should restrain from uploading movies.
4. It is also seen that most of the videos are uploaded at 17:00-17:59 time frame and are mostly viewed at 4:00-4:59. Hence, it is recommended to upload videos at 17:00 so that more viewers can view the videos at 4:00.
5. It is also predicted that dislikes of the videos are derived by time-frame and category id(Duncum, 2014). Most of the dislikes of the videos are made during the time-frame of 4:00-4:59. Hence, it is recommended that during this time-frame more music videos are uploaded as it is the most liked category of the viewers.

The given analysis will help the company in analyzing the number of likes, comments, views and uploaded content under each year and publishing countries. Uptil now, France had the most of the channels while GB with lowest number of channels (Cranwell, Opazo-Breton, & Britton, 2016). Most of the viewers view Music in France, GB, US and Canada. While, Trailers have been the least viewed video in France only. Most of the views are from Great Britain in every category while France views lesser videos of each category. This also showed that GB has more viewers either due to more population, more internet access and more inclination towards watching youtube (Mitchell & LR Schuster, 2017).

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Task 6: Cover Letter

Andrian White

CEO
ABC Online Multimedia Company

687- San Francisco, CA 94107

Dear Sir:

Improving the Operational Excellence and Achieving Strategic Objectives

This report is prepared by Content Analyst and it is part of the research conducted for analyzing the data sets and discover the patterns in dashboards that can help the management of ABC Online Multimedia Company for achieving its operational excellence and achieving its strategic objectives. The data analysis was performed using the IBM Watson Analytics that revealed the following insights.

Key Findings:

• Highest number of channels are of France (2207) while lowest number of viewers view videos in France.
• Lowest number of channels are of Great Britain (1624) while highest number of viewers view videos in Great Britain.
• Top three most liked, uploaded and viewed category in all four countries have remained to be Music, Entertainment and Politics.
• Most of the videos are uploaded in 2018
• Most of the uploaded videos are uploaded at 16:00-16:59.
• Least of the uploaded videos are uploaded at 6:00 to 6:59.
• Most of the views view videos at 4:00-4:59 and hit likes and dislikes at this time frame
• Trailers and movies videos remained to be the least liked category for all of the countries to be uploaded and viewed.
• Friday remained to be the most like day for uploading videos with 9082 uploads and Saturday remained to be least liked day with 6859 uploads.
• Most of the videos are uploaded during December for all the countries
• likes are derived 31% by comment count and category id, 28% by comment count and views and 28% by dislikes and category id
• Comment Count, views and dislikes are the top three single drivers of likes of the videos with 21%, 21% and 19% of predicting power.
• The insight shows that when the comments are disabled, lesser videos are disliked while with the open comments, more videos are disliked by the viewers.
• The insight shows that the driver of comment count includes dislikes & category id (14%), likes and category id (13%), views and category id (12%), dislikes and time frame (12%) and likes (10%).

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Recommendations:

1. It is recommended to disable the comments section for each uploaded video as by disabling the comments section, lesser number of viewers click on dislike option of the video.
2. It is observed that people like music videos more since highest number of uploaded videos over past 3 years have remained to be related to music. Entertainment and politics remain to be the other two most liked categories. Hence, for driving more likes and views, the company is recommended to upload music related videos.
3. It is also seen that movies are uploaded very rarely and are lesser viewed by the viewers. It is because of the piracy issues related to movies industry. Hence, it is recommended that for avoiding the litigation by the movie houses and publishers, the company should restrain from uploading movies.
4. It is also seen that most of the videos are uploaded at 17:00-17:59 time frame and are mostly viewed at 4:00-4:59. Hence, it is recommended to upload videos at 17:00 so that more viewers can view the videos at 4:00.
5. It is also predicted that dislikes of the videos are derived by time-frame and category id. Most of the dislikes of the videos are made during the time-frame of 4:00-4:59. Hence, it is recommended that during this time-frame more music videos are uploaded as it is the most liked category of the viewers.

Sincerely,

Content Analysts

References

Bessi, A. (2016). Users polarization on Facebook and Youtube. PloS one, 11(8).

Cayari, C. (2011). The YouTube Effect: How YouTube Has Provided New Ways to Consume, Create, and Share Music. International Journal of Education & the Arts, 12(6), 6.

Cheng, X., Dale, C., & Liu, J. (2012). Statistics and social network of youtube videos. Journal of the American Society for Information Science and Technology, 63(3), 616-629.

Choi, D. Y. (2007). Online piracy, innovation, and legitimate business models. Technovation, 27(4), 168-178.

Cranwell, J., Opazo-Breton, M., & Britton, J. (2016). Adult and adolescent exposure to tobacco and alcohol content in contemporary YouTube music videos in Great Britain: a population estimate. J Epidemiol Community Health, 70(5), 488-492.

Duncum, P. (2014). Youth on YouTube as smart swarms. Art education, 67(2), 32-36.

Edmond, M. (2014). Here we go again: Music videos after YouTube. Television & New Media, 15(4), 305-320.

Hoyt, R. E. (2016). IBM Watson analytics: automating visualization, descriptive, and predictive statistics. JMIR public health and surveillance, 2(2).

Lange, P. G. (2007). Commenting on comments: Investigating responses to antagonism on YouTube. Society for Applied anthropology conference, 31.

Madden, A., Ruthven, I., & McMenemy, D. (2013). A classification scheme for content analyses of YouTube video comments. Journal of documentation, 69(5), 693-714.

Mitchell, I. A., & LR Schuster, A. (2017). Why don’t end-of-life conversations go viral? A review of videos on YouTube. BMJ supportive & palliative care, 7(2), 197-204.

Schultes, P., Dorner, V., & Lehner, F. (2013). Leave a Comment! An In-Depth Analysis of User Comments on YouTube. Wirtschaftsinformatik, 42, 659-673.

Walther, J. B., DeAndrea, D., & Kim, J. (2015). The influence of online comments on perceptions of viewers on YouTube. Human Communication Research, 36(4), 469-492.

Wen, X. (2016). Comments on YouTube Product Review Videos. Howard Journal of Communications, 25(3), 281-302.

Wirga, E. W. (2015). evelopment a Popularity Model and Sentiment Analysis as an Analysis Method for Social Media Video YouTube. International Communication Gazette, 21(3), 52-81.