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12 results for “YouTube comments”
Disinformation on YouTube: A dataset of YouTube comments on videos related to claims made by Trump and Vance on Haitian immigrants
<div> <div> <div> <div> <div> <p>The corpus contains three files. First, the youtube_haitian_disinformation_videos_meta.csv file includes comments and YouTube video metadata. Data is organized around per video information. The columnar values are: </p> </div> </div> </div> <div> <ul> <li> <p>video_id </p> <p> </p> </li> <li> <p>date (video publication date) </p> <p> </p> </li> <li> <p>title </p> <p> </p> </li> <li> <p>description </p> <p> </p> </li> <li> <p>channel_title </p> <p> </p> </li> <li> <p>transcript </p> <p> </p> </li> <li> <p>transcript_str (Video transcript without timestamps) </p> <p> </p> </li> <li> <p>views </p> <p> </p> </li> <li> <p>likes </p> <p> </p> </li> <li> <p>comments (all comments per video) </p> </li> </ul> </div> <div> <div> <div> <p>Second, the youtube_haitian_disinformation_comment_reply_metadata.csv file includes comments, replies, and comment metadata. Each comment occupies its own row in the spreadsheet. The columnar field are: </p> </div> </div> </div> <div> <ul> <li> <p>video_id </p> <p> </p> </li> <li> <p>comment </p> <p> </p> </li> <li> <p>comment_date </p> <p> </p> </li> <li> <p>comment_like_count </p> <p> </p> </li> <li> <p>author </p> <p> </p> </li> <li> <p>comment_id </p> <p> </p> </li> <li> <p>in_reply_to </p> <p> </p> </li> <li> <p>neg, neu, pos, compound (VADER polarity scores) </p> <p> </p> </li> <li> <p>named_entities (spaCy named entity tags with tokens) </p> <p> </p> </li> <li> <p>emoji (spaCy Emojis and token spans) </p> </li> </ul> </div> </div> <div> <div> <div> <div> <p>Comments and associated metadata are represented in individual rows. </p> </div> <div> <p>The third file contains the results of the TFIDF analysis described herein. The TFIDF analysis features the top 5000 terms weights for the comments to each video in a .csv file. </p> </div> </div> </div> <div> <ul> <li> <p>youtube_disinfo_comments_tfidf_results_per_video.csv </p> </li> </ul> </div> </div> </div>
Youtube comments on Smart Farming
<p>Please cite the following paper, if you are working on or using the above dataset.</p> <p>https://www.mdpi.com/2624-7402/4/2/29</p> <p>Farming Dataset</p> <p>The datasets are taken from the following 16 YouTube videos related to farming:<br> 1. How Japan Is Reshaping Its Agriculture By Harnessing Smart Farming Technology-<br> Science Insider<br> 2. Europe has the best regenerative farmers in the world - Richard Perkins<br> 3. The Futuristic Farms That Will Feed the World - Freethink<br> 4. Vertical farms could take over the world - Freethink<br> 5. India’s largest Precision Farm - Simply Fresh Discover Agriculture<br> 6. Solar Panels Plus Farming? Agrivoltaics Explained Undecided with Matt Ferell<br> 7. 7 Israeli Agriculture Technologies Israel<br> 8. The CNH Industrial Autonomous Tractor Concept (Full Version CNH Industrial<br> 9. IoT Smart Plant Monitoring System Viral Science -the home of creativity<br> 10. Singapore’s Bold Plan to Build the Farms of the Future Tomorrow’s Build<br> 11. Smart Vertical Farms in Sharjah Episode Up<br> 12. Drones, robots, and super sperm – the future of farming DW Documentary<br> 13. Simply Fresh - India’s Largest State Of The Art Precision Farm<br> 14. RIPPA The Farm Robot Exterminates Pests And Weeds- ABC Science<br> 15. Top 10 Agritech Startups Empowering Indian Farmers Backstage With Millionaires<br> 16. This Farm of the Future Uses No Soil and 95% Less Water Stories<br> Total counts: 7334<br> The comments have been divided into the following labels:<br> 1. Praising (970 comments)<br> 2. Opinion (3224 comments),<br> 3. Suggestion (231 comments),<br> 4. Undefined (1386 comments),<br> 5. Queries (1007 comments)<br> 6. Hybrid (516 comments).</p> <p>The file includes:<br> 1. Comment<br> 2. Label</p>
MALAYALAM LANGUAGE (MIX CODE) Recipe channels Youtube Comments
<p>The dataset used for performing Text Classification is a combination of two different datasets scrapped from the comment section of two Youtube channels namely "Veen's Curryworld" and "Lekshmi Nair". The data is extracted using the YouTube API and contains two atrributes namely “text” and “label” where the former contains the comments and later contains the corresponding label. The comments are classified into 7 labels:</p> <p> </p> <p>- Label 1: Gratitude</p> <p>- Label 2: About the recipe</p> <p>- Label 3: About the video</p> <p>- Label 4: Praising</p> <p>- Label 5: Hybrid</p> <p>- Label 6: Undefined</p> <p>- Label 7: Suggestions and Queries</p> <p> </p> <p>The number of instances in each category is listed below:</p> <p> </p> <p>- Label 1: 484</p> <p>- Label 2: 396</p> <p>- Label 3: 300</p> <p>- Label 4: 362</p> <p>- Label 5: 249</p> <p>- Label 6: 2062</p> <p>- Label 7: 438</p>
Youtube cookery channels viewers comments in Hinglish
<p>The data was collected from the famous cookery Youtube channels in India. The major focus was to collect the viewers' comments in Hinglish languages. The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel.</p> <p>Both the datasets comments are divided into seven categories:-</p> <p>Label 1- Gratitude</p> <p>Label 2- About the recipe</p> <p>Label 3- About the video</p> <p>Label 4- Praising</p> <p>Label 5- Hybrid</p> <p>Label 6- Undefined</p> <p>Label 7- Suggestions and queries</p> <p>All the labelling has been done manually.</p> <p> </p> <p><strong>Nisha Madhulika dataset:</strong></p> <p><strong>Dataset characteristics: Multivariate</strong></p> <p><strong>Number of instances: 4900</strong></p> <p><strong>Area: Cooking </strong></p> <p><strong>Attribute characteristics: Real</strong></p> <p><strong>Number of attributes: 3</strong></p> <p><strong>Date donated: March, 2019</strong></p> <p><strong>Associate tasks: Classification</strong></p> <p><strong>Missing values: Null</strong></p> <p> </p> <p><strong>Kabita Kitchen dataset:</strong></p> <p><strong>Dataset characteristics: Multivariate</strong></p> <p><strong>Number of instances: 4900</strong></p> <p><strong>Area: Cooking </strong></p> <p><strong>Attribute characteristics: Real</strong></p> <p><strong>Number of attributes: 3</strong></p> <p><strong>Date donated: March, 2019</strong></p> <p><strong>Associate tasks: Classification</strong></p> <p><strong>Missing values: Null</strong></p> <p> </p> <p>There are two separate datasets file of each channel named as preprocessing and main file .</p> <p>The files with preprocessing names are generated after doing the preprocessing and exploratory data analysis on both the datasets. This file includes:</p> <ul> <li> Id</li> <li>Comment text</li> <li>Labels</li> </ul> <ul> <li>Count of stop-words</li> <li>Uppercase words</li> <li>Hashtags</li> <li>Word count</li> <li>Char count</li> <li>Average words</li> <li>Numeric</li> </ul> <p> </p> <p>The main file includes:</p> <ul> <li>Id</li> <li>comment text</li> <li>Labels</li> </ul> <p>Please cite the paper</p> <p>https://www.mdpi.com/2504-2289/3/3/37</p> <p> </p> <p><strong>MDPI and ACS Style</strong></p> <p>Kaur, G.; Kaushik, A.; Sharma, S. Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach. <em>Big Data Cogn. Comput.</em> <strong>2019</strong>, <em>3</em>, 37.</p>
Sexual Abusive Comments from YouTube by Roma Tre University
<p>1000 sexually abusive comments collected from popular YouTube Videos including cartoons like Peppa Pig</p>
Comments on YouTube videos of top two Indian political parties named Indian National Congress and Bhartiya Janata Party
<p>The datasets are taken from Various YouTube videos of top two Indian political parties named Indian National Congress and Bhartiya Janata Party.</p> <p>Both the datasets are divided into two categories: -</p> <p>Label 1- Positive</p> <p>Label 2- Negative</p> <p>All the labelling has been done manually.</p> <p><br> </p> <p><strong>Indian National Congress dataset:</strong></p> <p>Characteristics: Bivariate</p> <p>Number of instances in dataset: 1998</p> <p>Area of subject: Politics</p> <p>Attribute characteristics of dataset: Real</p> <p>Number of attributes: 2</p> <p>Date donated: March, 2019</p> <p>Task associated: Classification(binary)</p> <p>Missing values: Null</p> <p><br> </p> <p><strong>Bhartiya Janata Party dataset:</strong></p> <p>Characteristics: Bivariate</p> <p>Number of instances in dataset: 1952</p> <p>Area of subject: Politics</p> <p>Attribute characteristics of dataset: Real</p> <p>Number of attributes: 2</p> <p>Date donated: March, 2019</p> <p>Task associated: Classification(binary)</p> <p>Missing values: Null</p> <p><br> </p> <p><br> </p> <p><strong>Bothe datasets contains equal number of positive and negative comments:</strong></p> <p><br> </p> <p>Total number of positive comments present in Bhartiya Janata Party dataset =976</p> <p>Total number of negative comments present in Bhartiya Janata Party dataset =976</p> <p>Total number of positive comments present in Indian National Congress dataset=999</p> <p>Total number of negative comments present in Bhartiya Janata Party dataset=999</p> <p><br> </p> <p><strong>Both datasets contain following attributes:</strong></p> <ul> <li> <p><strong>comment text </strong></p> </li> <li> <p><strong>Labels </strong></p> </li> </ul>
A dataset of media releases (Twitter, News and Comments, Youtube, Facebook) form Poland related to COVID-19 for open research
<p>Social behavior has a fundamental impact on the dynamics of infectious diseases (such as COVID-19), challenging public health mitigation strategies and possibly the political consensus. The widespread use of the traditional and social media on the Internet provides us with an invaluable source of information on societal dynamics during pandemics. With this dataset, we aim to understand mechanisms of COVID-19 epidemic-related social behavior in Poland deploying methods of computational social science and digital epidemiology. We have collected and analyzed COVID-19 perception on the Polish language Internet during 15.01-31.07(06.08) and labeled data quantitatively (Twitter, Youtube, Articles) and qualitatively (Facebook, Articles and Comments of Article) in the Internet by infomediological approach.</p> <p>- manually labelled1,449 articles / Facebook posts from Lower Silesia (facebook_articles_lower_silesia.zip) and 111 texts from outside this region;</p> <p>-manually labelled 1000 most popular tweets (twits_annotated.xlsx) with cathegories is_fake (categorical and numeric) topic and sentiment; </p> <p>-extracted 57,306 representative articles (articles_till_06_08.zip) in Polish using Eventregitry.org tool in language Polish and topic "Coronavirus" in article body;</p> <p>- extracted 1,015,199 (tweets_till_31_07_users.zip and tweets_till_31_07_text.zip) and Tweets from #Koronawirus in language Polish using Twitter API.</p> <p>- collected 1,574 videos (youtube_comments_till_31_07.zip and youtube_movie.csv) with keyword: Koronawirus on YouTube and 247,575 comments on them using Google API;</p> <p>- We supplemented the media observations with an analysis of 244 social empirical studies till 25.05 on COVID-19 in Poland (empirical_social_studies.csv).</p> <p>Reports and analyzes and coding books can be found in Polish at: <a href="http://www.infodemia-koronawirusa.pl/percepcja-koronowirusa-na-dolnym-slasku/">http://www.infodemia-koronawirusa.pl</a></p> <p>Main report (in Polish) https://depot.ceon.pl/handle/123456789/19215 </p>
Brazilian Elections YouTube Comments Dataset (2018-2022)
<p>This dataset provides a comprehensive analysis of YouTube comments related to Brazilian election candidates for the years 2018 and 2022. The dataset is organized into two folders:</p><p><strong>video_gender_mapping_BR_elections</strong>: This folder contains individual files, each corresponding to a YouTube channel analyzed. Each file includes the following fields:</p><ul><li><strong>channelId</strong>: YouTube channel identifier</li><li><strong>channelTitle</strong>: YouTube channel title</li><li><strong>videoId</strong>: YouTube video identifier</li><li><strong>publishedAt</strong>: Datetime of when the video was published</li><li><strong>year_month</strong>: Year and month of when the video was published</li><li><strong>views</strong>: Number of views of the video</li><li><strong>comments</strong>: Number of total comments on the video</li><li><strong>likes</strong>: Number of total likes on the video</li><li><strong>match_type</strong>: Type of match considering candidate name and video title/description (T for Title, D for Description, TD for both)</li><li><strong>match_result</strong>: Array of JSON containing positive matches based on race, elective office, title/description match, and gender</li><li><strong>match_cargos</strong>: List of elective offices that appear in the match</li><li><strong>match_genero</strong>: Overview of gender types (M for male, F for female, MF for both)</li><li><strong>match_raca</strong>: Overview of races for all matched elective offices (BR for white, AM for yellow, PA for brown, PR for black, IN for Indian, NA for None of the above)</li><li><strong>match_genero_raca</strong>: Array of gender and race concatenated for each matched candidate</li></ul><p><strong>comments_folders</strong>: This file provides a sample of comments collected from the videos analyzed in the first file. It includes the following fields:</p><ul><li><strong>id</strong>: Comment identifier</li><li><strong>videoId</strong>: Video identifier where the comment is published</li><li><strong>textDisplay</strong>: Comment as displayed on the YouTube platform</li><li><strong>textDisplay_len</strong>: Number of characters in the displayed comment</li><li><strong>textOriginal</strong>: Encoded comment text</li><li><strong>textOriginal_len</strong>: Number of characters in the original comment text</li><li><strong>authorDisplayName</strong>: Name of the comment author</li><li><strong>authorChannelId</strong>: Comment author channel identifier</li><li><strong>publishedAt</strong>: Timestamp of when the comment was published</li><li><strong>likes</strong>: Number of likes on the comment</li></ul><p>This dataset offers valuable insights into YouTube interactions surrounding Brazilian elections, including demographic and sentiment analysis based on candidate information, gender, and race. Researchers can utilize this dataset for a detailed examination of public engagement and sentiment on political content during the specified election periods.</p>
Motivations for citing research in comments to YouTube videos
<p><strong>This dataset includes 300 YouTube comments that link to research publications or preprints. Each comment has been assigned into one category that define why individuals mention scholarly publications in comments to YouTube videos. </strong></p> <p>Each row of the file "Categories_300random.xlsx" represents one distinct comment to YouTube video.<br> The file includes the following columns:</p> <ul> <li><strong>Comment_text </strong>- the full text of a comment,</li> <li><strong>YouTube_link </strong>- URL to the video where the comment was left,</li> <li><strong>Comment_link </strong>- URL to the thread with the comment,</li> <li><strong>Category </strong>- the final category that was assigned to the comment,</li> <li>Columns <strong>Category_old_schema_SS</strong>, <strong>Category_old_schema_IP</strong>, <strong>Category_old_schema_OZ </strong>- categories assigned by different researchers according to old categorization schema and were used for validation reasons,</li> <li>Columns <strong>Category_OZ</strong>, and <strong>Caregory_LB </strong>- categories assigned by different researchers according to the final schema and were used for validation.<br> <br> </li> </ul>
Dataset Abusive YouTube Comments
<p>Sexually Abusive Comments and specific words collection from popular youtube videos such as music videos and cartoons (Peppa Pig)</p>
Dataset of YouTube Comments on Prophet Muhammad's Lineage and the Ba Alawi Debate
<p>This dataset contains 4,000 YouTube comments collected from various videos discussing the theme of Prophet Muhammad's lineage and claims of descent related to the Ba Alawi debate. The data were gathered using the Apify YouTube Comments Scraper (<a href="https://apify.com/streamers/youtube-comments-scraper" target="_new" rel="noopener">https://apify.com/streamers/youtube-comments-scraper</a>). The comments, written in <strong>Indonesian</strong>, were extracted from videos uploaded by channels such as BILAL Channel, Panji Islam, KAJIAN SURGA OFFICIAL, and others. Metadata includes the comment text, anonymized username, publication date, and number of likes. The dataset underwent a cleaning process to remove duplicates, correct errors, and ensure data consistency. This dataset is suitable for critical discourse analysis or studies focusing on digital interactions in religious debates within the Indonesian context.</p>
Ben Shapiro YouTube Comments
<div> <div> <div> </div> </div> </div> <div> <p>This dataset was complied as a <span>resource for analyzing viewer engagement, sentiment, and discussion trends on the Ben Shapiro YouTube channel over the specified period. It </span>comprises user-generated comments extracted from the Ben Shapiro YouTube channel. The collection process involved first cataloging a comprehensive list of all videos published on the channel. Subsequently, these videos were categorized into three distinct time frames. From each time frame, the ten videos that garnered the highest number of comments were identified for detailed comment extraction. The extraction of videos and their associated comments was conducted utilizing YouTube Data Tools (Rieder, 2015). The dataset was finalized on September 12, 2022, and encompasses 711,909 comments ranging from September 1, 2020, to September 12, 2022. This dataset was uploaded and analyzed in the 4CAT: Capture & Anlysis Toolkit (Peeters & Hagen, 2022). </p> <p>References:</p> <div> <div>Peeters, S., & Hagen, S. (2022). The 4CAT Capture and Analysis Toolkit: A Modular Tool for Transparent and Traceable Social Media Research. <em>Computational Communication Research</em>, <em>4</em>(2), 571–589. <a href="https://doi.org/10.5117/CCR2022.2.007.HAGE">https://doi.org/10.5117/CCR2022.2.007.HAGE</a></div> <div> </div> <div> <div> <div>Rieder, B. (2015). <em>YouTube Data Tools</em> (1.11) [Computer software].</div> </div> </div> </div> </div>
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