{"title":"A method for time-varying analysis of YouTube search results and related videos: The case of the war in Ukraine","authors":"João Guilherme Bastos dos Santos","doi":"10.1177/02673231231190762","DOIUrl":null,"url":null,"abstract":"The increasing relevance of platformization sheds light on the role of algorithms in filtering political content, profiling audiences and defining the rules for the competition between traditional outlets and new content creators online. More importantly, algorithms learn and adapt results based on users’ activities online. But, if algorithms learn over time, how to deal with this time-varying dynamic when analysing them? The present paper brings a method for analysing YouTube search ranking and related video algorithm results over time, applied to a corpus of 1346 videos related to the war in Ukraine connected through 7934 related video links, starting on 21st November and stopping on 5th December. Results show that YouTube search and related video algorithms differ considerably in their behaviours, considering the channels and video clusters they benefited over time. It could be a dangerous bias to focus solely on one of the algorithms or presume its functioning based on collections made after – and not during – the political events they influenced. More than a matter of choosing methods, to understand how algorithms are changing the network structure of the current public sphere, it is important to develop new ones.","PeriodicalId":47765,"journal":{"name":"European Journal of Communication","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Communication","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/02673231231190762","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
The increasing relevance of platformization sheds light on the role of algorithms in filtering political content, profiling audiences and defining the rules for the competition between traditional outlets and new content creators online. More importantly, algorithms learn and adapt results based on users’ activities online. But, if algorithms learn over time, how to deal with this time-varying dynamic when analysing them? The present paper brings a method for analysing YouTube search ranking and related video algorithm results over time, applied to a corpus of 1346 videos related to the war in Ukraine connected through 7934 related video links, starting on 21st November and stopping on 5th December. Results show that YouTube search and related video algorithms differ considerably in their behaviours, considering the channels and video clusters they benefited over time. It could be a dangerous bias to focus solely on one of the algorithms or presume its functioning based on collections made after – and not during – the political events they influenced. More than a matter of choosing methods, to understand how algorithms are changing the network structure of the current public sphere, it is important to develop new ones.
期刊介绍：
The European Journal of Communication is interested in communication research and theory in all its diversity, and seeks to reflect and encourage the variety of intellectual traditions in the field and to promote dialogue between them. The Journal reflects the international character of communication scholarship and is addressed to a global scholarly community. Rigorously peer-reviewed, it publishes the best of research on communications and media, either by European scholars or of particular interest to them.
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