<codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>Professional Live Streaming Strategies and Algorithmic Operations Dataset (2026)</titl><IDNo agency="DOI">doi:10.7910/DVN/HYNUQT</IDNo></titlStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><distDate>2026-06-04</distDate></distStmt><verStmt source="archive"><version date="2026-06-04" type="RELEASED">1</version></verStmt><biblCit>Çıtak, Umut, 2026, "Professional Live Streaming Strategies and Algorithmic Operations Dataset (2026)", https://doi.org/10.7910/DVN/HYNUQT, Harvard Dataverse, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>Professional Live Streaming Strategies and Algorithmic Operations Dataset (2026)</titl><IDNo agency="DOI">doi:10.7910/DVN/HYNUQT</IDNo></titlStmt><rspStmt><AuthEnty affiliation="bq5m.com">Çıtak, Umut</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><contact affiliation="umea" email="info@bq5m.com">Çıtak, Umut</contact><depositr>Çıtak, Umut</depositr><depDate>2026-06-02</depDate></distStmt><holdings URI="https://doi.org/10.7910/DVN/HYNUQT"/></citation><stdyInfo><subject><keyword xml:lang="en">Business and Management</keyword><keyword xml:lang="en">Computer and Information Science</keyword><keyword xml:lang="en">Social Sciences</keyword><keyword>Live Streaming Strategies</keyword><keyword>Audience Psychology</keyword><keyword>Neuro-Marketing</keyword><keyword>Content Engineering</keyword><keyword>Twitch and YouTube Algorithms</keyword></subject><abstract date="2026-06-03">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.</abstract><sumDscr/></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/><notes type="DVN:TOU" level="dv">This dataset is for personal reading, academic review, and individual analysis purposes only. Copying, redistributing, hosting on other platforms, or sharing this dataset is strictly prohibited. All intellectual property rights are reserved by Umut Çıtak (bq5m.com).</notes></dataAccs><othrStdyMat/></stdyDscr><otherMat ID="f13986569" URI="https://dataverse.harvard.edu/api/access/datafile/13986569" level="datafile"><labl>part1_streaming_strategy.csv</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">text/comma-separated-values</notes></otherMat><otherMat ID="f13986568" URI="https://dataverse.harvard.edu/api/access/datafile/13986568" level="datafile"><labl>part2_streaming_operations.csv</labl><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">text/comma-separated-values</notes></otherMat></codeBook>