数字时代，我们需要的不是"流量冠军"，而是"留量大师"。流量是风，留量是根，风再大也有停下来的 一天，只有厚植根基才能守住长远。 从流量触达走向留量沉淀 中国人民大学新闻学院教授唐铮 过去10年，数字技术重塑文化内容的传播逻辑。短视频平台、社交媒体算法、大数据推荐系统让传播内 容实现了指数级触达，展现了技术对传播效率的赋能。 然而，流量触达的"快"也带来了"浅"的困境：用户的注意力被碎片化内容稀释，文化传播缺乏深度共鸣 与情感沉淀。 文化传播的目标不是流量为王，而是留量长青。如何让流量转化为留量，从流量触达走向留量沉淀？ 留量沉淀的关键是从"被看见"到"被记住"。数字技术为文化内容提供了精准触达用户的工具，但只有打 动人心的内容，才能与用户建立深度连接，构建情感共同体，从而被长久记住。留量沉淀的过程不 是"摘果子"，而是"种树"；不是"圈地"，而是"盖楼"。 首先，要提高内容触达的精准性。利用大数据分析用户兴趣，实现千人千面的内容推荐，避免广撒网式 的无效传播。其次，要维护内容运营的持续性。通过定期更新、社群运营、用户反馈的机制，保持与用 户的长期互动。最后，提升传播内容的文化性。坚守文化内核，避免为博取流量 ...

Core Viewpoint - The National Cybersecurity and Information Security Information Notification Center reported that 69 mobile applications were found to illegally collect and use personal information, violating laws such as the Cybersecurity Law and the Personal Information Protection Law [1][2][3]. Group 1: Non-compliance with Privacy Policies - 15 mobile applications failed to clearly inform users about the collection and use of personal information at the first run, including the lack of accessible privacy policies [1]. - 39 mobile applications did not list the purposes, methods, and scope of personal information collection in their privacy policies [2]. - 15 mobile applications did not inform users about the recipients of their personal information when shared with other processors, nor did they obtain separate consent [3]. Group 2: User Consent and Rights - 3 mobile applications began collecting personal information without obtaining user consent [4]. - 8 mobile applications did not provide effective means for users to correct, delete personal information, or cancel their accounts, or set unreasonable conditions for these actions [5]. - 3 mobile applications failed to process complaints and reports within the promised timeframe and did not establish a convenient mechanism for users to exercise their rights [6]. Group 3: Withdrawal of Consent - 35 mobile applications did not provide users with a way to withdraw consent for the collection of personal information [7]. Group 4: Automated Decision-Making and Sensitive Information - 5 mobile applications used automated decision-making for information push and marketing without providing options to refuse or not targeting personal characteristics [8]. - 1 mobile application did not inform users about the necessity and impact of processing sensitive personal information [8]. Group 5: Security Measures - 21 mobile applications did not implement appropriate security measures such as encryption or de-identification [8]. Group 6: Absence of Privacy Policies - 2 mobile applications were found to have no privacy policy at all [8].