Improving knowledge management on Xiaohongshu: Data-driven procedures for a more astute digital ecosystem
Abstract
Xiaohongshu (Little Red Book) has developed into a vital computerized platform, mixing social media, e-commerce, and user-generated content, making it a key space for millions of users to share and find information. With its blend of true content and community-driven intuition, Xiaohongshu gives important bits of knowledge about lifestyle, items, travel, and individual encounters. In any case, its massive amount of unstructured information brings challenges like information overload, credibility issues, and trouble in finding pertinent data. This paper investigates imaginative, data-driven techniques to progress knowledge management on Xiaohongshu, pointing to creating a more organized, solid, and user-friendly platform for a wide range of users.
1. Introduction
Xiaohongshu has ended up a popular center for sharing information, with 300 million monthly dynamic users, and Gen Z accounts for 17.34%1. The platform is popular since it combines real-life stories, peer suggestions, and individual encounters that are frequently more relatable than the information on conventional websites or apps. Be that as it may, Xiaohongshu’s current framework for overseeing data has restrictions because of its unstructured nature, which influences the platform’s potential as a dependable source of information. This article looks at how Xiaohongshu can utilize data-driven approaches to improve its knowledge management, making a difference in users discovering solid, important, and well-organized content.
2. The current state of knowledge management on Xiaohongshu
Xiaohongshu functions as an expansive, casual collection of user-generated content, covering everything from magnificence tips to monetary advice. Excessive use of user-generated content, however, has posed a risk to Xiaohongshu2, which was removed from China’s app store for two months in 2019 for content violations and resulted in the loss of nearly 20 million monthly active users. In addition, the decentralized nature of the user-generated content regularly leads to information overload, challenges in finding pertinent information, and concerns about the validity of information.
The platform’s calculations regularly advance popular content, which can eclipse specialty but profitable posts. This comes about in users over and over seeing similar content, making a kind of information bubble that limits the differing qualities of information accessible. To achieve the most of Xiaohongshu’s potential as a knowledge-sharing platform, there must be a vital advancement in how it oversees and organizes data.
3. Key challenges in knowledge management
3.1 Information overload and excess
With billions of posts and millions of everyday updates, Xiaohongshu’s massive content environment can be overpowering. Billions of posts are created every day on the Xiaohongshu platform, which has raised concerns about user fatigue caused by information overload3. Tedious content—multiple posts giving similar advice—makes it difficult for users to discover special bits of knowledge. The users may feel overpowered by tedious posts, making it harder to get to new data.
3.2 Validity and misinformation
Since anybody can contribute to Xiaohongshu, the quality of data will be variable. Driven by profits, some key opinion leaders may publish false advertisements and comments, thus misleading consumers and affecting the healthy development of the market4. A large number of users are likely to completely believe the information on the platform, and this is particularly concerning in areas like health, finance, and education, where misinformation can have serious consequences.
3.3 Wasteful information retrieval
The platform’s searchability is restricted by a need for legitimate categorization and labeling, leading to ineffective searches. Xiaohongshu users are likely to have to spend more time searching for particular information compared to the users of more organized platforms like Quora or LinkedIn.
3.4 Obsolete content and need for update components
Themes such as travel rules, visa requirements, or popular diets alter regularly, but Xiaohongshu does not have an orderly way to update or archive outdated content. As a result, users might depend on obsolete advice, influencing the platform’s quality as a source of current knowledge.
4. Data-driven methodologies for compelling knowledge management
To address these challenges, Xiaohongshu can use data-driven techniques to progress how content is organized, confirmed, and accessed, creating a more user-friendly information environment.
4.1 AI-powered content curation and personalization
Xiaohongshu can utilize progressed AI calculations to better organize and prescribe content. Machine learning can analyze client behavior to prioritize high-quality, lock in posts, lessen excess, and progress personalization. Natural language processing (NLP) can offer assistance in categorizing posts into particular subjects, making searches less demanding and more natural. For example5, personalized content nourishment that adjusts to what users are connected with can highlight modern, pertinent data rather than rehashing the same well-known posts.
4.2 Credibility index and verified user systems
Presenting a credibility index can offer assistance to users in judging the quality of Xiaohongshu’s content. This framework can score posts based on user feedback, engagement, and the author’s validity. Confirmed identifications for specialists or experienced users would flag dependable content, similar to how LinkedIn underwrites aptitudes.
4.3 Structured knowledge hubs and improved labeling
Xiaohongshu can set up committed information center points for well-known topics like “Sustainable Living,” “Investment Tips,” or “Health & Wellness,” where related content is centrally organized. Advanced labeling frameworks would offer assistance to users, channeling content more viably, and AI might upgrade these centers with the most recent patterns, ensuring users continuously see the foremost current data.
4.4 Dynamic content management and upgrade alerts
Adding update cautions for time-sensitive content can keep data important. For example, posts around changing travel restrictions or skincare tips may provoke creators or the community to update frequently.
4.5 Gamification to empower quality commitments
Including game-like components such as focuses, identifications, and rewards for high-quality posts can propel users to share exact and important data. Using gamification in marketing6 can increase customer engagement by 48% or even double, while gamification interactions can increase time spent on a website by 30%. This means higher user engagement and content quality. For Xiaohongshu, fulfilling keen and well-researched commitments can altogether move forward the general quality of its information base.
5. Broader implications for businesses, analysts, and teachers
Moving forward, Xiaohongshu’s knowledge management can better benefit more users. For businesses, way better information organization permits more profound bits of knowledge into consumer behavior, driving more successful showcasing methodologies. Analysts can utilize Xiaohongshu’s structured knowledge hubs as important data sources, and teachers can consolidate real-world cases from the platform into their education, upgrading intuitive and peer-based learning.
By receiving these data-driven advancements, Xiaohongshu might improve client maintenance and set itself apart from competitors. A more solid and energetic knowledge-sharing platform would reinforce Xiaohongshu’s position as a driving asset and grow its reach to modern groups of users, including experts and academics.
6. Conclusion
Xiaohongshu’s victory comes from its dynamic community and true user-generated content. Even so, to completely tap into its potential, the platform must address its restrictions by actualizing data-driven enhancements in content administration, confirmation, and recovery. With AI-driven channels, validity frameworks, and energetic content updates, Xiaohongshu can advance into a more organized, dependable, and user-centered information biological system. This change will improve user encounters and build up Xiaohongshu as a driving platform for information sharing, bridging the gap between social media and organized, dependable data.
Article source: Adapted from Improving Knowledge Management on Xiaohongshu: Data-Driven Procedures for a More Astute Digital Ecosystem prepared as part of the requirements for completion of course KM6304 Knowledge Management Strategies and Policies in the Nanyang Technological University Singapore Master of Science in Knowledge Management (KM).
Header image source: Aline Viana Prado on Pexels.
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