Core Viewpoint - The article discusses the evolution and characteristics of social risks associated with network technology in China over the past 30 years, emphasizing the need for proactive measures in data governance, capital operation, and political communication to mitigate these risks and enhance national security [2][3][32]. Group 1: Evolution of Network Technology - The internet era in China began in 1994, leading to a significant increase in internet users, reaching 1.108 billion by December 2024, with an internet penetration rate of 78.6% [3]. - Network technology has evolved through various stages: Web 1.0 (static pages), Web 2.0 (interactive platforms), Web 3.0 (decentralized systems), and Web 4.0 (AI and metaverse), each contributing to a complex interplay of benefits and risks [4]. Group 2: Social Benefits of Network Technology - Network technology enhances individual empowerment by facilitating knowledge flow and reshaping power structures, allowing users to influence public discourse [5]. - It stimulates individual potential by transforming users from passive recipients to active participants in information dissemination and resource allocation [5]. - The technology promotes market competition by increasing transparency and reducing information asymmetry, enabling users to make informed decisions [5]. Group 3: Negative Effects of Technological Evolution - The transition from single products to ecological platforms has led to chaotic competition among major platforms, resulting in resource misallocation [6]. - Users initially attracted by subsidies may later face monopolistic practices, including price discrimination and reduced market competition, which challenge the normal functioning of the market [7]. Group 4: Specific Manifestations of Social Risks - The rapid evolution of network technology has outpaced the ability of social institutions to adapt, leading to new social risks in data governance, capital, and political spheres [8]. Group 5: Data Governance Challenges - Over-collection of data and algorithmic control mechanisms pose significant risks, leading to information silos and echo chambers that distort public perception [10]. - The pervasive collection of personal data raises privacy concerns, resulting in decreased social trust and increased vulnerability to data exploitation [11]. Group 6: Capital-Driven Alienation - The intertwining of network technology and capital has led to monopolistic platforms that stifle competition and innovation, creating a market dominated by a few major players [14][15]. - The exploitation of digital labor through algorithmic control has raised concerns about workers' rights and the ethical implications of such practices [18]. Group 7: Political Intervention Risks - Network technology has been used to manipulate political discourse through social bots, impacting public opinion and political stability [20]. - Social media serves as a platform for mobilizing social movements, which can both empower citizens and pose risks to governance [22]. Group 8: Pathways for Risk Mitigation - Proactive measures are needed in value guidance, institutional regulation, technological empowerment, and multi-stakeholder governance to effectively address the social risks posed by network technology [23]. - Establishing a layered regulatory framework and ensuring data sovereignty are critical for protecting individual privacy and enhancing data security [25][26]. - Encouraging technological innovation while anticipating risks through simulation and predictive measures can help in managing future challenges [29][30].

Core Viewpoint - The article discusses the concept of "information cocoons" and the emergence of "information beehives" as a solution to enhance information diversity and break free from algorithm-driven content filtering [11][56]. Group 1: Information Cocoon Concept - The term "information cocoon" was introduced by Cass Sunstein in 2006, highlighting how individuals tend to consume information that aligns with their existing beliefs, leading to a narrow perspective [16][18]. - The phenomenon of information cocoons existed before the rise of algorithms, but the advent of social media and algorithmic recommendations has exacerbated the issue, creating "filter bubbles" [19][24]. - The article outlines the differences between "echo chambers," "information cocoons," and "filter bubbles," emphasizing how each concept relates to user behavior and algorithmic influence [22][23]. Group 2: Algorithmic Influence - Algorithms play a crucial role in shaping user experiences by personalizing content based on user preferences, which can lead to a lack of exposure to diverse viewpoints [30][31]. - The design of algorithms aims to maximize user engagement, often resulting in a feedback loop that reinforces existing interests and limits the discovery of new information [30][31]. - Various types of algorithms, such as collaborative filtering and content-based filtering, are identified as significantly contributing to the formation of information cocoons [27][28]. Group 3: Information Beehive Concept - The "information beehive" concept is proposed as a countermeasure to information cocoons, promoting a more open and diverse information ecosystem [59][60]. - The beehive metaphor encourages users to actively seek out varied information sources and engage with different perspectives, contrasting with the closed nature of cocoons [12][61]. - The article suggests that fostering an information beehive requires collaboration among content producers, platforms, and consumers to ensure high-quality content is accessible to a broader audience [12][13].