Chenbo Fu, Qiushun Che, Zhanghao Li, Fengyan Yuan, Yong Min
{"title":"重度用户未能陷入过滤泡沫:来自中国网络视频平台的证据","authors":"Chenbo Fu, Qiushun Che, Zhanghao Li, Fengyan Yuan, Yong Min","doi":"10.3389/fphy.2024.1423851","DOIUrl":null,"url":null,"abstract":"Accelerated by technological advancements, while online platforms equipped with recommendation algorithms offer convenience to obtain information, it also brought algorithm bias, shaping the norms and behaviors of their users. The filter bubble, conceived as a negative consequence of algorithm bias, means the reduction of the diversity of users’ information consumption, garnering extensive attention. Previous research on filter bubbles typically used users’ self-reported or behavioral data independently. However, existing studies have disputed whether filter bubbles exist on the platform, possibly owing to variations in measurement methods. In our study, we took content category diversity to measure the filter bubbles and innovatively used a combination of participants’ self-reported and website behavioral data, examining filter bubbles on a single online video platform (Bilibili). We conducted a questionnaire survey among 337 college students and collected 3,22,324 browsing records with their informed authorization, constituting the dataset for research analysis. The existence of filter bubbles on Bilibli is found, such that diversity will decrease when viewing <jats:italic>Game</jats:italic> videos increases. Furthermore, we considered the factors that influence filter bubbles from the perspective of demographics and user behavior. In demographics, female and non-member users are more likely to be trapped in filter bubbles. In user behavior, results of feature importance analysis indicate that the diversity of information consumption of heavy users is higher than others, and both activity and fragmentation have an impact on the formation of filter bubbles, but in different directions. Finally, we discuss the reasons for these results and a theoretical explanation that the filter bubbles effect may be lower than we thought for both heavy and normal users on online platforms. Our conclusions provide valuable insights for understanding filter bubbles and platform management.","PeriodicalId":12507,"journal":{"name":"Frontiers in Physics","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Heavy users fail to fall into filter bubbles: evidence from a Chinese online video platform\",\"authors\":\"Chenbo Fu, Qiushun Che, Zhanghao Li, Fengyan Yuan, Yong Min\",\"doi\":\"10.3389/fphy.2024.1423851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accelerated by technological advancements, while online platforms equipped with recommendation algorithms offer convenience to obtain information, it also brought algorithm bias, shaping the norms and behaviors of their users. The filter bubble, conceived as a negative consequence of algorithm bias, means the reduction of the diversity of users’ information consumption, garnering extensive attention. Previous research on filter bubbles typically used users’ self-reported or behavioral data independently. However, existing studies have disputed whether filter bubbles exist on the platform, possibly owing to variations in measurement methods. In our study, we took content category diversity to measure the filter bubbles and innovatively used a combination of participants’ self-reported and website behavioral data, examining filter bubbles on a single online video platform (Bilibili). We conducted a questionnaire survey among 337 college students and collected 3,22,324 browsing records with their informed authorization, constituting the dataset for research analysis. The existence of filter bubbles on Bilibli is found, such that diversity will decrease when viewing <jats:italic>Game</jats:italic> videos increases. Furthermore, we considered the factors that influence filter bubbles from the perspective of demographics and user behavior. In demographics, female and non-member users are more likely to be trapped in filter bubbles. In user behavior, results of feature importance analysis indicate that the diversity of information consumption of heavy users is higher than others, and both activity and fragmentation have an impact on the formation of filter bubbles, but in different directions. Finally, we discuss the reasons for these results and a theoretical explanation that the filter bubbles effect may be lower than we thought for both heavy and normal users on online platforms. Our conclusions provide valuable insights for understanding filter bubbles and platform management.\",\"PeriodicalId\":12507,\"journal\":{\"name\":\"Frontiers in Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.3389/fphy.2024.1423851\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.3389/fphy.2024.1423851","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Heavy users fail to fall into filter bubbles: evidence from a Chinese online video platform
Accelerated by technological advancements, while online platforms equipped with recommendation algorithms offer convenience to obtain information, it also brought algorithm bias, shaping the norms and behaviors of their users. The filter bubble, conceived as a negative consequence of algorithm bias, means the reduction of the diversity of users’ information consumption, garnering extensive attention. Previous research on filter bubbles typically used users’ self-reported or behavioral data independently. However, existing studies have disputed whether filter bubbles exist on the platform, possibly owing to variations in measurement methods. In our study, we took content category diversity to measure the filter bubbles and innovatively used a combination of participants’ self-reported and website behavioral data, examining filter bubbles on a single online video platform (Bilibili). We conducted a questionnaire survey among 337 college students and collected 3,22,324 browsing records with their informed authorization, constituting the dataset for research analysis. The existence of filter bubbles on Bilibli is found, such that diversity will decrease when viewing Game videos increases. Furthermore, we considered the factors that influence filter bubbles from the perspective of demographics and user behavior. In demographics, female and non-member users are more likely to be trapped in filter bubbles. In user behavior, results of feature importance analysis indicate that the diversity of information consumption of heavy users is higher than others, and both activity and fragmentation have an impact on the formation of filter bubbles, but in different directions. Finally, we discuss the reasons for these results and a theoretical explanation that the filter bubbles effect may be lower than we thought for both heavy and normal users on online platforms. Our conclusions provide valuable insights for understanding filter bubbles and platform management.
期刊介绍:
Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.