{"@context":{"@language":"en","@vocab":"https://schema.org/","citeAs":"cr:citeAs","column":"cr:column","conformsTo":"dct:conformsTo","cr":"http://mlcommons.org/croissant/","rai":"http://mlcommons.org/croissant/RAI/","data":{"@id":"cr:data","@type":"@json"},"dataType":{"@id":"cr:dataType","@type":"@vocab"},"dct":"http://purl.org/dc/terms/","examples":{"@id":"cr:examples","@type":"@json"},"extract":"cr:extract","field":"cr:field","fileProperty":"cr:fileProperty","fileObject":"cr:fileObject","fileSet":"cr:fileSet","format":"cr:format","includes":"cr:includes","isLiveDataset":"cr:isLiveDataset","jsonPath":"cr:jsonPath","key":"cr:key","md5":"cr:md5","parentField":"cr:parentField","path":"cr:path","recordSet":"cr:recordSet","references":"cr:references","regex":"cr:regex","repeated":"cr:repeated","replace":"cr:replace","sc":"https://schema.org/","separator":"cr:separator","source":"cr:source","subField":"cr:subField","transform":"cr:transform","wd":"https://www.wikidata.org/wiki/"},"@type":"sc:Dataset","conformsTo":"http://mlcommons.org/croissant/1.0","name":"Professional Live Streaming Strategies and Algorithmic Operations Dataset (2026)","url":"https://doi.org/10.7910/DVN/HYNUQT","creator":[{"@type":"Person","givenName":"Umut","familyName":"Çıtak","affiliation":{"@type":"Organization","name":"bq5m.com"},"name":"Çıtak, Umut"}],"description":"This dataset contains strategic, psychological, and technical operational data extracted from the \"Professional Live Streaming Guide: Algorithmic Strategies and Audience Psychology\" research. It is structured to analyze viewer retention patterns, algorithmic discoverability, and content monetization. The dataset is divided into two main parts: Part 1 (Sections 1-15) covers platform algorithms, neuro-marketing, and technical setups (SRT, Bitrate). Part 2 (Sections 16-31) focuses on crisis management, AI tool integration, SEO strategies, and career sustainability. It is designed for content creators, behavioral researchers, and digital marketing analysts.","keywords":["Business and Management","Computer and Information Science","Social Sciences","Live Streaming Strategies","Audience Psychology","Neuro-Marketing","Content Engineering","Twitch and YouTube Algorithms"],"license":"https://dataverse.harvard.edu/api/datasets/:persistentId/versions/1.0/customlicense?persistentId=doi:10.7910/DVN/HYNUQT","datePublished":"2026-06-04","dateModified":"2026-06-04","includedInDataCatalog":{"@type":"DataCatalog","name":"Harvard Dataverse","url":"https://dataverse.harvard.edu"},"publisher":{"@type":"Organization","name":"Harvard Dataverse"},"version":"1.0","citeAs":"@data{DVN/HYNUQT_2026,author = {Çıtak, Umut},publisher = {Harvard Dataverse},title = {Professional Live Streaming Strategies and Algorithmic Operations Dataset (2026)},year = {2026},url = {https://doi.org/10.7910/DVN/HYNUQT}}","distribution":[{"@type":"cr:FileObject","@id":"part1_streaming_strategy.csv","name":"part1_streaming_strategy.csv","encodingFormat":"text/comma-separated-values","md5":"4c0f723429c3ad141de948a155a731ed","contentSize":"2557","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/13986569"},{"@type":"cr:FileObject","@id":"part2_streaming_operations.csv","name":"part2_streaming_operations.csv","encodingFormat":"text/comma-separated-values","md5":"2a6a8d777c423dcf1fd13726d19d8208","contentSize":"2439","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/13986568"}]}