"The Evolution of Graph Design in Social Media and the Rise of TikTok Hacks"
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Aug 06, 2023
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"The Evolution of Graph Design in Social Media and the Rise of TikTok Hacks"
Introduction: And You Will Know Us by the Company We Keep — Remains of the Day
In the ever-evolving landscape of social media, the design and structure of platforms play a crucial role in shaping user experiences. One design mistake that often hampers the effectiveness of social media apps is the problem of graph design. This issue arises when an app relies heavily on a user's social graph to determine their interactions and content feed. However, this approach often overlooks the importance of the social context in which users find themselves. The social graph, consisting of who a user follows and who follows them, becomes a significant contextual influence in social media.
The Problem with Social Graphs and Content Feeds
Many popular Western social media apps intertwine the social graph with the content feed. While this approach has been widely adopted, it raises questions about how to ensure users derive maximum value from the product or service. The challenge lies in creating an optimal graph for each user without them having to manually assemble a social graph. Additionally, the graph should adapt to their evolving interests in real-time.
Negative Network Effects and Context Collapse
One critical issue with approximating an interest graph with a social graph is the negative network effects that arise at scale. For example, on platforms like Twitter, users follow others to build their social graph, but this does not guarantee interest alignment. The relevance and quality of content on Western social media are heavily influenced by who users follow, leading to a disproportionate effect on what they see. Furthermore, having a large number of followers from various spheres of life can result in a context collapse, where different social contexts collide.
The Danger of Graph Design Mistakes
Graph design problems pose a significant challenge to social media companies as they are often irreversible or difficult to reverse. Users tend to make these mistakes by conforming to social norms and rarely unfollowing people once they have followed them. Consequently, social media apps may face phase shifts and negative network effects as their graphs scale.
Alternative Approaches to Graph Design
To address the limitations of social graphs, some platforms have explored alternative approaches. China's social infrastructure, exemplified by WeChat, demonstrates a logical separation between the social graph and the rest of the internet. Furthermore, apps can leverage interest graphs rather than social graphs to curate content feeds. TikTok, for instance, constructs a relevant feed by observing user reactions and utilizing a two-stage screening process for videos.
Actionable Advice for Graph Design
- 1. Remove the burden of unfollowing: Apps can alleviate the need for users to manually unfollow accounts that no longer interest them, thus simplifying graph management.
- 2. Decouple content feeds from social graphs: Platforms can consider separating content feeds from social graphs, allowing users to explore their interests without relying solely on the people they follow.
- 3. Embrace algorithmic feeds intelligently: Instead of merely patching graph design problems, social media apps should leverage algorithmic feeds to deliver relevant content based on users' interests, not just their social connections.
Conclusion
Graph design has a profound impact on the user experience and success of social media platforms. By understanding the limitations of social graphs and exploring alternative approaches like interest graphs, apps can create more personalized and engaging experiences for their users. Additionally, platforms should prioritize empowering creators and finding ways to compensate them for their valuable contributions to the online community. Ultimately, the future of social media lies in striking a balance between user interests, contextual relevance, and sustainable economic ecosystems.
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