YouTube Video Analysis Article Swipe
YouTube (youtube.com) is an online video-sharing platform that allows users to upload, view, rate, share, add to playlists, report, comment on videos, and subscribe to other users. Over 2 billion logged-in users visit YouTube each month, and every day people watch over a billion hours of video and generate billions of views. UGC (User-Generated Content) makes up a good portion of the content available on YouTube, and more and more people post videos on YouTube, many of which become well-known YouTubers. A notable trend to look at for these YouTubers is how their channel grows over time. \n \nWe were tasked with analyzing how certain YouTubers become successful over time, how their early videos differ from later ones in terms of scripts, and how comments change with fame. Such analysis requires us to look into two sets of data. The first set is numerical data of the channels, which consists of view counts of videos, likes and dislikes on videos, published dates of the video, the interactions between the video creator and the audience, etc. The second set is textual data, which consists of the auto-generated scripts from videos as well as comments from the users. With the help of YouTube APIs and other available helper tools, we are able to scrape the metadata from data of videos and output them as CSV files for future studies. \n \nFor the analysis, we generate some scatter graphs where each dot stands for one instance of the video, where the x-axis represents the published date while the y-axis represents the views it gets, and then the color of the dot represents some other metrics for evaluation (for instance, the duration of videos). With the Python NLTK package, we are able to conduct analyses over the transcripts from the videos and comments, to see what words are spoken the most, what words appear frequently in the comments and if they are positive or negative, how many words the creator says in a minute, etc. Combining these data we can generate a more thorough scatter graph for discovering if there is a pattern on how certain YouTubers become more and more successful.
Related Topics
Concepts
Computer science
Presentation (obstetrics)
Word (group theory)
Channel (broadcasting)
Indie film
Function (biology)
Artificial intelligence
Natural language processing
Information retrieval
World Wide Web
Linguistics
Telecommunications
Art
Visual arts
Radiology
Medicine
Biology
Philosophy
Evolutionary biology
Metadata
- Type
- article
- Language
- es
- Landing Page
- http://hdl.handle.net/10919/103255
- http://hdl.handle.net/10919/103255
- OA Status
- green
- Related Works
- 6
- OpenAlex ID
- https://openalex.org/W3160610989
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3160610989Canonical identifier for this work in OpenAlex
- Title
-
YouTube Video AnalysisWork title
- Type
-
articleOpenAlex work type
- Language
-
esPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-07Full publication date if available
- Authors
-
Akhil Bachubhay, Danny Chhour, Heji Deng, Trung TranList of authors in order
- Landing page
-
https://hdl.handle.net/10919/103255Publisher landing page
- PDF URL
-
https://hdl.handle.net/10919/103255Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://hdl.handle.net/10919/103255Direct OA link when available
- Concepts
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Computer science, Presentation (obstetrics), Word (group theory), Channel (broadcasting), Indie film, Function (biology), Artificial intelligence, Natural language processing, Information retrieval, World Wide Web, Linguistics, Telecommunications, Art, Visual arts, Radiology, Medicine, Biology, Philosophy, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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6Other works algorithmically related by OpenAlex
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| abstract_inverted_index.online | 4 |
| abstract_inverted_index.output | 217 |
| abstract_inverted_index.people | 39, 70 |
| abstract_inverted_index.scrape | 209 |
| abstract_inverted_index.second | 174 |
| abstract_inverted_index.share, | 14 |
| abstract_inverted_index.spoken | 300 |
| abstract_inverted_index.stands | 235 |
| abstract_inverted_index.tasked | 98 |
| abstract_inverted_index.tools, | 204 |
| abstract_inverted_index.users. | 26, 193 |
| abstract_inverted_index.video, | 162, 241 |
| abstract_inverted_index.videos | 72, 111, 186, 215, 292 |
| abstract_inverted_index.views. | 51 |
| abstract_inverted_index.x-axis | 244 |
| abstract_inverted_index.y-axis | 251 |
| abstract_inverted_index.YouTube | 0, 33, 198 |
| abstract_inverted_index.between | 165 |
| abstract_inverted_index.billion | 29, 43 |
| abstract_inverted_index.certain | 102, 347 |
| abstract_inverted_index.channel | 93 |
| abstract_inverted_index.comment | 19 |
| abstract_inverted_index.conduct | 285 |
| abstract_inverted_index.content | 62 |
| abstract_inverted_index.creator | 168, 321 |
| abstract_inverted_index.metrics | 267 |
| abstract_inverted_index.minute, | 325 |
| abstract_inverted_index.notable | 82 |
| abstract_inverted_index.pattern | 344 |
| abstract_inverted_index.portion | 59 |
| abstract_inverted_index.report, | 18 |
| abstract_inverted_index.scatter | 230, 336 |
| abstract_inverted_index.scripts | 184 |
| abstract_inverted_index.textual | 177 |
| abstract_inverted_index.upload, | 11 |
| abstract_inverted_index.videos, | 21, 152, 157 |
| abstract_inverted_index.Content) | 54 |
| abstract_inverted_index.YouTube, | 65, 74 |
| abstract_inverted_index.analyses | 286 |
| abstract_inverted_index.analysis | 127 |
| abstract_inverted_index.billions | 49 |
| abstract_inverted_index.comments | 122, 190, 309 |
| abstract_inverted_index.consists | 147, 180 |
| abstract_inverted_index.dislikes | 155 |
| abstract_inverted_index.duration | 273 |
| abstract_inverted_index.generate | 48, 228, 332 |
| abstract_inverted_index.instance | 238 |
| abstract_inverted_index.metadata | 211 |
| abstract_inverted_index.package, | 280 |
| abstract_inverted_index.platform | 6 |
| abstract_inverted_index.positive | 314 |
| abstract_inverted_index.requires | 128 |
| abstract_inverted_index.scripts, | 119 |
| abstract_inverted_index.thorough | 335 |
| abstract_inverted_index.videos). | 275 |
| abstract_inverted_index.Combining | 327 |
| abstract_inverted_index.YouTubers | 89, 103, 348 |
| abstract_inverted_index.analysis, | 226 |
| abstract_inverted_index.analyzing | 100 |
| abstract_inverted_index.audience, | 171 |
| abstract_inverted_index.available | 63, 202 |
| abstract_inverted_index.channels, | 145 |
| abstract_inverted_index.comments, | 294 |
| abstract_inverted_index.instance, | 271 |
| abstract_inverted_index.logged-in | 30 |
| abstract_inverted_index.negative, | 316 |
| abstract_inverted_index.numerical | 141 |
| abstract_inverted_index.published | 158, 247 |
| abstract_inverted_index.subscribe | 23 |
| abstract_inverted_index.YouTubers. | 80 |
| abstract_inverted_index.evaluation | 269 |
| abstract_inverted_index.frequently | 306 |
| abstract_inverted_index.playlists, | 17 |
| abstract_inverted_index.represents | 245, 252, 264 |
| abstract_inverted_index.successful | 105 |
| abstract_inverted_index.well-known | 79 |
| abstract_inverted_index.discovering | 339 |
| abstract_inverted_index.successful. | 353 |
| abstract_inverted_index.transcripts | 289 |
| abstract_inverted_index.interactions | 164 |
| abstract_inverted_index.(youtube.com) | 1 |
| abstract_inverted_index.video-sharing | 5 |
| abstract_inverted_index.auto-generated | 183 |
| abstract_inverted_index.(User-Generated | 53 |
| abstract_inverted_index.time. \n \nWe | 96 |
| abstract_inverted_index.studies. \n \nFor | 224 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.08807497 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |