Where should social media go when the age of recommended media arrives?
Last year, Instagram announced that videos shorter than 15 minutes would be shared as Reels. This change was met with immediate backlash, with many users protesting the shift to full-screen photos and videos. Additionally, Instagram’s use of algorithms to recommend content from accounts that users don't follow led to frustration, as many users felt overwhelmed by random entertainment content appearing on their homepages.
In response, photographer Tati Bruening initiated a campaign on Instagram calling for "Make Instagram Instagram again," urging the platform not to become TikTok, saying, "I just want to see my friends' cute photos." This campaign received support from several celebrities, notably Kylie Jenner from the Kardashian family, who is the most-followed woman on Instagram with 361 million followers, the second-largest individual account on the platform. By the end of July, Instagram’s parent company Meta officially announced that Facebook’s news feed would also transition to an algorithm-based content recommendation system.
Currently, the content distribution logic of social media is shifting from being based on social graphs to algorithm-driven interest-based recommendations. The emergence of the "recommended media" concept has sparked public debate about whether this signals the end of "social media."
Despite the global wave of the "Make Instagram Instagram again" movement and Facebook’s shift toward algorithmic recommendations, I believe that the situation in Chinese social media is quite different. I will use Weibo, WeChat, and Douyin as examples to explain.
Among these three platforms, Weibo is the most aligned with the essence of "social media." However, Weibo’s media attributes are stronger than its social attributes. Weibo focuses on "what's happening," positioning itself as a public opinion space centered around reposting content. Its core is the timeliness of news, where users are more interested in fresh updates rather than content production itself. Although Weibo also uses a "follow" model, the excessive and often forced following weakens the social connections. As a result, Weibo’s focus remains on public domain traffic created through weak relationships, which leads to relatively low user engagement. While there are many opportunities for private domain traffic operations through official accounts, fan groups, and live-streaming, Weibo’s social and content properties are insufficient to make these efforts effective, which contributes to its lackluster revenue performance.
Currently, Weibo has not yet focused on shifting toward an algorithmic recommendation model like Facebook. However, I suspect that its fate may ultimately resemble Facebook's if it does not reform. Industry data suggests that Weibo’s user engagement and revenue situation are concerning. Without reform, it will face pressure from platforms like Douyin, which are based on content recommendations. That being said, Weibo is unique in that it still serves as a platform for "public information dissemination." Its "hot search" list has become an important channel for many young people to access news. Therefore, Weibo's path forward might not necessarily involve chasing the "recommended media" trend, but rather optimizing its "media function" and differentiating itself.
WeChat, on the other hand, is a unique case. Fundamentally, WeChat is an instant messaging app. Its strong relationship-based connections and various life functions based on these social ties make it more of a practical "tool," which is the primary reason behind its high user stickiness. WeChat’s slogan, "A Way of Life," reflects this orientation. From an industry perspective, WeChat has already established an unshakable position in China. It’s reasonable to assume that, for a long time, social media platforms based on strong relationships won’t be disrupted or replaced by recommendation-based media.
Social media, in essence, is about competition for "popularity," rather than necessarily the quality of the content. It favors creators with the largest number of friends or followers, as the more followers you have, the greater the potential for spreading and influencing content. Through this network effect, social media platforms can rapidly expand. This is why Kylie Jenner opposed the changes to Instagram; in an algorithm-driven platform rather than one driven by followers, her 361 million followers lose their value. If a platform can establish a sufficiently large social graph, it already has an automatic content distribution system, delivering highly relevant and attractive content to a large number of users.
Unlike Weibo, Douyin/TikTok doesn't care whether your friends or acquaintances are using the platform. In recommendation-based media, the social graph is no longer the main tool for content distribution. Instead, content distribution is primarily driven by opaque, platform-defined algorithms, aiming to capture users' attention and maximize engagement. In contrast to social media, recommended media does not compete based on popularity. Rather, it competes based on the absolute quality of the content.
A key feature of social media/new media is that it gives users the power to choose the information they want to see. However, over time, as more "lazy users" emerge, people are increasingly concerned with the cost of socializing and accessing information. In this age of information overload and fragmentation, many people have begun to long for the traditional media era, where content was “fed” to them without the need to search for it. Algorithms cater to our need for content quality and efficiency. From the platform's perspective, it’s a competition for people’s "attention," and in the attention economy, social media platforms may be compelled to transition toward a recommendation-based model for profit motives.
However, this shift does not mean the end of social media or the rise of an era dominated by recommended media. Recommendation-based media that relies on social graphs is still essential for social interactions. Whether social media and recommended media will replace or merge with each other depends on the positioning of different social media platforms in various countries and the strategic decisions these platforms make based on their own interests.
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