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29 results for “TikTok”
The Invasion of Ukraine Viewed through TikTok: A Dataset
<p>This is a dataset of videos and comments related to the invasion of Ukraine, published on TikTok by a number of users over the year of 2022. It was compiled by Benjamin Steel, Sara Parker and Derek Ruths at the Network Dynamics Lab, McGill University. We created this dataset to facilitate the study of TikTok, and the nature of social interaction on the platform relevant to a major political event.</p> <p>The dataset has been released here on Zenodo: <a href="https://doi.org/10.5281/zenodo.7534952">https://doi.org/10.5281/zenodo.7926959</a> as well as on Github: <a href="https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok">https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok</a></p> <p>To create the dataset, we identified hashtags and keywords explicitly related to the conflict to collect a core set of videos (or ”TikToks”). We then compiled comments associated with these videos. All of the data captured is publically available information, and contains personally identifiable information. In total we collected approximately 16 thousand videos and 12 million comments, from approximately 6 million users. There are approximately 1.9 comments on average per user captured, and 1.5 videos per user who posted a video. The author personally collected this data using the web scraping PyTok library, developed by the author: <a href="https://github.com/networkdynamics/pytok">https://github.com/networkdynamics/pytok</a>.</p> <p>Due to scraping duration, this is just a sample of the publically available discourse concerning the invasion of Ukraine on TikTok. Due to the fuzzy search functionality of the TikTok, the dataset contains videos with a range of relatedness to the invasion.</p> <p>We release here the unique video IDs of the dataset in a CSV format. The data was collected without the specific consent of the content creators, so we have released only the data required to re-create it, to allow users to delete content from TikTok and be removed from the dataset if they wish. Contained in this repository are scripts that will automatically pull the full dataset, which will take the form of JSON files organised into a folder for each video. The JSON files are the entirety of the data returned by the TikTok API. We include a script to parse the JSON files into CSV files with the most commonly used data. We plan to further expand this dataset as collection processes progress and the war continues. We will version the dataset to ensure reproducibility.</p> <p>To build this dataset from the IDs here:</p> <ol> <li>Go to <a href="https://github.com/networkdynamics/pytok">https://github.com/networkdynamics/pytok</a> and clone the repo locally</li> <li>Run <code>pip install -e .</code> in the pytok directory</li> <li>Run <code>pip install pandas tqdm</code> to install these libraries if not already installed</li> <li>Run <code>get_videos.py</code> to get the video data</li> <li>Run <code>video_comments.py</code> to get the comment data</li> <li>Run <code>user_tiktoks.py</code> to get the video history of the users</li> <li>Run <code>hashtag_tiktoks.py</code> or <code>search_tiktoks.py</code> to get more videos from other hashtags and search terms</li> <li>Run <code>load_json_to_csv.py</code> to compile the JSON files into two CSV files, <code>comments.csv</code> and <code>videos.csv</code></li> </ol> <p>If you get an error about the wrong chrome version, use the command line argument <code>get_videos.py --chrome-version YOUR_CHROME_VERSION</code> Please note pulling data from TikTok takes a while! We recommend leaving the scripts running on a server for a while for them to finish downloading everything. Feel free to play around with the delay constants to either speed up the process or avoid TikTok rate limiting.</p> <p>Please do not hesitate to make an issue in this repo to get our help with this!</p> <p> </p> <p>The <code>videos.csv</code> will contain the following columns:</p> <p><code>video_id</code>: Unique video ID</p> <p><code>createtime</code>: UTC datetime of video creation time in YYYY-MM-DD HH:MM:SS format</p> <p><code>author_name</code>: Unique author name</p> <p><code>author_id</code>: Unique author ID</p> <p><code>desc</code>: The full video description from the author</p> <p><code>hashtags</code>: A list of hashtags used in the video description</p> <p><code>share_video_id</code>: If the video is sharing another video, this is the video ID of that original video, else empty</p> <p><code>share_video_user_id</code>: If the video is sharing another video, this the user ID of the author of that video, else empty</p> <p><code>share_video_user_name</code>: If the video is sharing another video, this is the user name of the author of that video, else empty</p> <p><code>share_type</code>: If the video is sharing another video, this is the type of the share, stitch, duet etc.</p> <p><code>mentions</code>: A list of users mentioned in the video description, if any</p> <p> </p> <p>The <code>comments.csv</code> will contain the following columns:</p> <p><code>comment_id</code>: Unique comment ID</p> <p><code>createtime</code>: UTC datetime of comment creation time in YYYY-MM-DD HH:MM:SS format</p> <p><code>author_name</code>: Unique author name</p> <p><code>author_id</code>: Unique author ID</p> <p><code>text</code>: Text of the comment</p> <p><code>mentions</code>: A list of users that are tagged in the comment</p> <p><code>video_id</code>: The ID of the video the comment is on</p> <p><code>comment_language</code>: The language of the comment, as predicted by the TikTok API</p> <p><code>reply_comment_id</code>: If the comment is replying to another comment, this is the ID of that comment</p> <p>The date can be compiled into a user interaction network to facilitate study of interaction dynamics. There is code to help with that here: <a href="https://github.com/networkdynamics/polar-seeds">https://github.com/networkdynamics/polar-seeds</a>. Additional scripts for further preprocessing of this data can be found there too.</p>
Italian TikTok users online behaviour patterns and social attitudes (survey)
<p>Survey of 500 young TikTok users (18-35) in Italy covering online behaviour patterns and social attitudes</p>
Disclosure Privacy Information Social Media on TikTok
<p><em><span>With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy</span></em></p>
Dissecting Tiktok and social media for children and young adults
<p>This data is related to protocol for systematic literature review and the details of literatures selected for systematic review.</p>
Technoableism & Social Media: TikTok Python Scrape
<p>This Notebook uses Deen Freelon's module "Pyktok" to scrape metadata from videos that mention terms related to disability and technology. Search terms include "wearable tech," "disability," "technology," and "biohack." </p>
Technoableism & Social Media: TikTok Dataset (v1)
<p>This dataset draws upon metadata scraped from TikTok using Deen Freelon's python module, "Pyktok." It contains metadata from TikTok videos obtained with Pyktok, using keywords and video IDs; notes relating to video content; brief summary statistics; and word frequency data generated in Voyant. </p>
Dataset for the Instagram and TikTok problematic use
<p>This dataset supports research on how engagement with social media (Instagram and TikTok) was related to problematic social media use (PSMU) and mental well-being. There are three different files. The SPSS and Excel spreadsheet files include the same dataset but in a different format. The SPSS output presents the data analysis in regard to the difference between Instagram and TikTok users.</p>
Data Responden Keputusan Pembelian Pelanggan E-commerce berdasarkan Konten TikTok
<p>The data of respondents' answers for research of Customer's Purchase Decisions on e-commerce Influenced by TikTok Contents. We remove the private information such as name, age, universities, and gender. We only store the questions and the answers. The data is in the form of a CSV file. </p>
TikTok dataset - Current affairs on TikTok. Virality and entertainment for digital natives
<p>Tiktok network graph with 5,638 nodes and 318,986 unique links, representing up to 790,599 weighted links between labels, using Gephi network analysis software.</p> <p>Source of:</p> <p>Peña-Fernández, Simón, Larrondo-Ureta, Ainara, & Morales-i-Gras, Jordi. (2022). Current affairs on TikTok. Virality and entertainment for digital natives. Profesional De La Información, 31(1), 1–12. <a href="https://doi.org/10.5281/zenodo.5962655">https://doi.org/10.5281/zenodo.5962655</a></p> <p>Abstract:</p> <p>Since its appearance in 2018, TikTok has become one of the most popular social media platforms among digital natives because of its algorithm-based engagement strategies, a policy of public accounts, and a simple, colorful, and intuitive content interface. As happened in the past with other platforms such as Facebook, Twitter, and Instagram, various media are currently seeking ways to adapt to TikTok and its particular characteristics to attract a younger audience less accustomed to the consumption of journalistic material. Against this background, the aim of this study is to identify the presence of the media and journalists on TikTok, measure the virality and engagement of the content they generate, describe the communities created around them, and identify the presence of journalistic use of these accounts. For this, 23,174 videos from 143 accounts belonging to media from 25 countries were analyzed. The results indicate that, in general, the presence and impact of the media in this social network are low and that most of their content is oriented towards the creation of user communities based on viral content and entertainment. However, albeit with a lesser presence, one can also identify accounts and messages that adapt their content to the specific characteristics of TikTok. Their virality and engagement figures illustrate that there is indeed a niche for current affairs on this social network.</p> <p> </p>
A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and other sources about the 2024 outbreak of Measles
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: <a href="https://doi.org/10.48550/arXiv.2406.07693" rel="nofollow">https://doi.org/10.48550/arXiv.2406.07693</a>)</p> <p><strong>Abstract</strong></p> <p>This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.</p>
#Coronavirus on TikTok: User engagement with misinformation as a potential threat to public health behavior
<p><strong>Background:</strong> COVID-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok.</p> <p><strong>Methods:</strong> A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior.</p> <p><strong>Results:</strong> 166 TikTok videos were identified. Moderate misinformation was present in 36 (22%) videos, and high-level misinformation was present in 11 (7%). After controlling for characteristics and content, videos containing moderate misinformation were less likely to generate a user response indicating intended behavior change. By contrast, videos containing high-level misinformation were less likely to be viewed but demonstrated a non-significant trend towards higher engagement among viewers.</p> <p><strong>Conclusions:</strong> COVID-related misinformation is less frequently viewed on TikTok but more likely to engage viewers. Public health authorities can combat misinformation on social media by posting content of their own. </p>
data and code for "Depression and social anxiety in relation to problematic TikTok use severity: The mediating role of boredom proneness and distress intolerance "
<p>data and code for the paper titled "Depression and social anxiety in relation to problematic TikTok use severity: The mediating role of boredom proneness and distress intolerance", to be published on Computers in Human Behavior.</p> <p> </p> <p> </p>
gipsidipsi/etikaTI: Pertanyaan Survey Pengaruh Pengaruh Tiktok Terhadap Spiritual
<p><a href="https://github.com/gipsidipsi/etikaTI/files/12781928/ANALISIS.PENGARUH.MEDIA.SOSIAL.TIKTOK.TERHADAP.SPIRITUAL.PADA.MAHASISWA.SISTEM.INFORMASI.ITS.Responses.-.Form.responses.1.csv">ANALISIS PENGARUH MEDIA SOSIAL TIKTOK TERHADAP SPIRITUAL PADA MAHASISWA SISTEM INFORMASI ITS (Responses) - Form responses 1.csv</a></p>
#Coronavirus on TikTok: User engagement with misinformation as a potential threat to public health behavior
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shelziagrayxena/Jurnal-EP: Dataset Aplikasi TikTok
<p>Dataset Jurnal EP-5 kelompok 5</p>
Registro de datos TFM "Estrategias de contenido en TikTok: Un análisis de las 10 marcas de moda rápida mejor posicionadas en España"
<p>Registros de datos: Interacciones, frecuancia de publicación, duración, formato y tipo de contenido de las publicaciones en TikTok.<br>Para la realización del TFM "Estrategias de contenido en TikTok: Un análisis de las 10 marcas de moda rápida mejor posicionadas en España".</p>
Explorando la iniciativa de bienestar mental en tiktok
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
Participación, consumo y cultura de la remezcla en TikTok: prácticas creativas por adolescentes españoles más allá de los contextos educativos formales
<p>Base de datos de la investigación cuantitativa y cualitativa realizada a la muestra indicada. </p>
A Data-Driven Approach for Finding Requirements Relevant Feedback from TikTok and YouTube
<p>This dataset includes the list of videos from TikTok and YouTube, regarding 20 different products, used in our study on utilizing videos to identify requirements relevant user feedback. We also provide the content and labeling for each video. In addition, we provide the search terms for each of the products that helped us find the videos. </p>
Usefulness and Ease of Use of TikTok as an Academic Support Tool: An Empirical Study of University Students
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