This ARTIFICIALAPPROPRIATIONreadme.txt file was generated on [2026/05/24] by [Rianna Walcott] ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset: Artificial Appropriation 2. Author Information: Principal Investigator Contact Information: Name: Rianna Walcott Institution: University of Maryland Address: Department of Communication, 2130 Skinner Building, University of Maryland, College Park, MD 20742 Email: rwalcott@umd.edu Associate or Co-investigator Contact Information: Name: Nessa Keddo Institution: King's College London Address: King's College London, Strand, London, WC2R 2LS, United Kingdom Email: nessa.keddo@kcl.ac.uk 3. Dates of data collection: 2025/07/04 - 2025/07/15 4. Geographic location of data collection: Researchers based in USA and UK, data collected online while in USA. 5. Information about funding sources that supported the collection of the data: N/A -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: N/A 2. Links to publications that cite or use the data: tbd after submission 3. Links to other publicly accessible locations of the data: N/A 4. Links/relationships to ancillary data sets: N/A 5. Was data derived from another source? If yes, list source(s): N/A 6. Recommended citation for the data: Walcott, R., & Keddo, N. (2026). Artificial Appropriation dataset (Version V1) [Data set]. Digital Repository at the University of Maryland (DRUM). --------------------- DATA & FILE OVERVIEW --------------------- 1. File List: A. Anonymised first round data collection Short description: Spreadsheet (CSV file) detailing 184 collected TikToks including associated metadata (anonymised post ID and thread ID, video caption, stickers used (i.e. text overlaid on video), time posted, URL, # of likes, comments, shares, plays, hashtags used). B. Anonymised shortlisted data Short description: Spreadsheet (CSV file) of 17 posts shortlisted from the total collected TikToks. Metadata includes: anonymised post ID and thread ID, author follower count, # of author videos, video caption, stickers used (i.e. text overlaid on video), time posted, URL, # of likes, comments, shares, plays, hashtags used. Also included are the researchers' thematic groupings of videos into three categories: 'Plays/identifies with', 'Skeptical/challenges', and 'refusal'. Video transcripts are also included. C. Anonymised shortlisted TikTok comments Spreadsheet (CSV file) of 684 user comments collected from the 17 shortlisted posts. Metadata includes: anonymised post ID, thread ID, comment ID (hierarchical relationship in unique IDs from post, to threads relating to that post, to individual comments within threads), video caption text, comment text, # of likes and replies, comment timestamp. D. Shortlisted TikTok thumbnails Folder containing 17 image thumbnail files (.jpeg files) from shortlisted TikToks, filenames refer to associated post IDs. E. Shortlisted data thumbnail image wall Image wall (.png file) of thumbnails from 17 shortlisted TikToks. 2. Relationship between files: Post and Thread IDs are unique identifiers; matching data points that are consistent across files to enable cross-referencing. 3. Additional related data collected that was not included in the current data package: All 184 TikTok video files were downloaded at time of data collection. Videos have not been included in this data package to protect the creators' rights to erase their personal data. Instead, TikTok URLs are included in metadata as this protects creators' rights to erase their data by removing their content at source. -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: Researchers collected a purposive sample of high-engagement videos and users within the field site, and overarching discourse on the topic. Identified commonly used terms and hashtags from this sample for systematic data collection. Researchers then created 'clean' research accounts and used TikTok's search function and filters to identify an initial sample of 236 videos (106 and 139 videos respectively) that were saved into two public 'collections' using TikTok's native organising function. Popular terms derived from initial exploration were supplemented by TikTok's algorithmic recommendations of related hashtags/search terms. Terms used were: ‘ChatGPT’, ‘blackgpt’, ‘Black AI’, ‘Jamaica chatgpt’, ‘Jamaicangpt’, ‘ChatGPT patois’, ‘ChatGPT pidgin’, ‘ChatGPT creole’, ‘ChatGPT accent’ and ‘ChatGPT Ebonics’. Results were sorted by relevance and date posted, and researchers collated results as well as germane recommended videos suggested by TikTok's algorithm that did not explicitly contain predetermined search terms within captions or hashtags, but were thematically related. Researchers assessed all content for relevancy, only including content in collection folders that met inclusion criteria as follows: 1. Content generated by Black users (based on researcher's assessment) 2. Content is predominantly in English (inclusive of non-standard/vernacular English) 3. Content concerns GenAI AND Black identity, concerns meta-ontological discourse about (post-)human identity rather than simply prescriptive advice on using GenAI 4. Content has significant engagement, >10,000 'likes' and/or >100 comments 5. Content was posted between 25th September 2024 and 7th July 2025 (starting date of data collection process). After removing content that did not meet inclusion criteria from the initial 236 videos, Zeeschuimer browser extension was used for data capture of the respective public TikTok collections. Duplicate entries were subsequently removed by 'filtering for unique items' using the 4CAT (Capture and Analysis Toolkit) interface, leaving 184 unique TikTok videos. Stijn Peeters. (2026). Zeeschuimer (v1.13.6). Zenodo. https://doi.org/10.5281/zenodo.18670373 Dataset was further refined from 184 videos to a sample of 17 by selecting the videos with highest engagement. Zeeschuimer browser extension was then used for data capture of 683 comments with significant engagement (>10 likes) from remaining sample. 2. Methods for processing the data: 4CAT (Capture and Analysis Toolkit) interface was used to analyse and process collected data. Processes included generation of .CSV files that included metadata as indicated in above file descriptions; anonymisation of datasets; download of TikTok videos and extracted audio files; transcription of video audio; download of thumbnails of selected videos; and creation of a image wall from extracted thumbnails. Peeters, S., & Hagen, S. (2022). The 4CAT Capture and Analysis Toolkit: A Modular Tool for Transparent and Traceable Social Media Research. Computational Communication Research, 4(2), 571–589. https://doi.org/10.5117/CCR2022.2.007.HAGE 3. Environmental/experimental conditions: Data was collected by researchers located in USA/UK - noted for potential impact on location-based data gathered. Researchers both created 'clean' research accounts to reduce the influence of personal account preferences and search histories on algorithmic content-ordering processes that determine the content returned by search queries. 4. People involved with sample collection, processing, analysis and/or submission: Rianna Walcott and Nessa Keddo