Core Viewpoint - The article discusses the inherent contradictions faced by content platforms like Douyin regarding the transparency of recommendation algorithms and their impact on user behavior and content quality [2][3]. Group 1: Algorithm Transparency and User Interaction - Douyin has established an algorithm transparency project team to explain the principles of its recommendation algorithms and its approach to content governance [4]. - The recommendation algorithm learns user behavior patterns by estimating the probability of user interactions with videos, such as likes and comments, based on historical data [5][9]. - The algorithm's effectiveness is enhanced by continuously feeding historical data into machine learning models, allowing for more accurate predictions of user behavior [9][10]. Group 2: Value Scoring and A/B Testing - Douyin determines the value score of user behaviors through A/B testing, adjusting the recommendation algorithms for different user groups to observe changes in key performance indicators [14]. - Multiple performance indicators may change in different directions, and Douyin's data analysis team creates relationships between these indicators to ensure they align with the primary goal of long-term user retention [16]. Group 3: Content Quality and Governance - Douyin's content operations team focuses on increasing the proportion of high-quality content by defining what constitutes "high-quality supply" and regularly reviewing and refining these standards [17][18]. - The platform employs a multi-layered review process for content, combining machine and human evaluations to filter out harmful or low-quality videos [19][24]. Group 4: User Behavior and Platform Adaptation - Douyin's governance measures are influenced by user behavior, with the platform adapting to user habits and preferences, such as the use of alternative terms to avoid perceived censorship [24][25]. - The platform has established a dedicated team to identify and counteract rumors, utilizing a "rumor database" to train machine learning models for better detection of false information [25][26]. Group 5: Evolving Standards and Regulatory Response - The article highlights the ongoing evolution of content platforms in response to public scrutiny and regulatory frameworks, emphasizing the need for continuous adaptation to societal expectations [28][29][30].

责编 ： 罗晓霞 校对：杨舒欣 网信部门依法查处一批低俗炒作娱乐明星信息账号 网信部门持续打击文娱领域"饭圈"乱象，近期，督促网站平台依法依约关闭和长期禁言处置"超能摄影阳 阳""摄影刘大锤""老板王大发""娱乐冇饭""追星少女卓小宴""贴小君""娱三少""ba哥专用""Real皮皮 王""八酱日常""明星娱乐一线""明星娱乐最前线""娱蜀黍"等一批低俗炒作绯闻丑闻八卦的违法违规账号。 一段时间以来，相关账号通过偷拍、跟拍明星非公开行程，发布未经核实的"爆料"信息；或以"知情人"名 义编造、转发不实信息，或借"标题党"和虚假预告等形式，制造噱头，恶意博取流量；或使用暗语、隐喻 等手段，无底线炒作明星八卦信息，严重破坏网络生态。 下一步，网信部门将继续压实网站平台主体责任，督促严格落实《网络信息内容生态治理规定》《关于进 一步加强娱乐明星网上信息规范相关工作的通知》等要求，聚焦泛娱乐化倾向和低俗炒作现象，坚决整治 流量至上和"饭圈"乱象，加大网络执法力度，努力营造积极向上的网络环境。同时，欢迎广大网民积极参 与监督举报，共同营造清朗网络空间。 来源：网信中国微信公众号 百万用户都在看 事关降息！重磅来袭！ 慌了 ...