Social media platforms have made it possible for information to reach millions of people within hours or even minutes. Unfortunately, false information can spread just as quickly. Instagram and TikTok are two platforms I use regularly, and both rely heavily on visual content and recommendation algorithms. A misleading video, screenshot, or graphic can therefore reach people who never intentionally searched for the topic. Both platforms have developed systems for dealing with misinformation, but they take somewhat different approaches. After researching their current policies, I found that both platforms have useful strategies, although there are still areas where their efforts could be improved.
Instagram’s Approach to Misinformation
Instagram is owned by Meta, which also operates Facebook and Threads. One of the biggest recent changes to Meta’s misinformation strategy was its decision to end its traditional third-party fact-checking program in the United States. In 2025, Meta began replacing that system with Community Notes on Facebook, Instagram, and Threads.
Community Notes lets users add context to a post that may be misleading. Contributors may write notes and add supporting sources, also rate notes written by other contributors. Meta says a note must be agreed upon by people who have previously disagreed in their ratings before it becomes public. But the goal is to prevent one group or perspective from setting the standard for what constitutes misleading.
One benefit of Community Notes is that they can provide context rather than simply telling users what they should or should not believe. A note can give users another source to examine and allow them to investigate a claim themselves. This relates to lateral reading because users are encouraged to look beyond the original post and consider outside evidence.
However, I think there is also an obvious weakness. Instagram content can spread incredibly quickly. By the time contributors write and approve a Community Note, a misleading Reel or post could already have thousands or even millions of views. Meta has also said that receiving a Community Note does not automatically cause a post’s distribution to be reduced.
Instagram also addresses AI-generated and manipulated media. Meta uses “AI info” labels on some content when it detects technical indicators that AI was used or when users disclose the use of AI. This is becoming increasingly important because realistic AI-generated images, audio, and videos can make it harder for an average social media user to determine what is authentic.
I think Instagram could improve its approach by making context and source information even more noticeable. Community Notes are useful, but the system still depends on contributors responding after questionable information has appeared. Instagram could also make it easier to trace reposted images and videos to their original source. This would encourage users to investigate where information actually came from rather than automatically trusting a screenshot or repost.
TikTok’s Approach to Misinformation
TikTok also uses several methods to address misinformation, including moderation, labels, recommendation restrictions, fact-checking, and media-literacy efforts. TikTok’s policies allow it to remove harmful misinformation in certain areas, while other questionable content can be restricted from recommendation. The company has also worked with independent fact-checking organizations to evaluate claims.
One part of TikTok’s approach that I find particularly interesting involves the For You feed. TikTok has explained that when content is undergoing fact-checking or a claim cannot be substantiated, the content can become ineligible for recommendation in the For You feed. TikTok has also used warning prompts designed to make users reconsider sharing unverified content.
I think this approach is important because of how TikTok actually works. From my own experience using the platform, I rarely search for every video that appears on my screen. Most of the content I see is recommended through the For You feed. This means TikTok’s algorithm has significant influence over which information gets attention. Limiting the recommendation of questionable content while it is being investigated could reduce the number of people exposed to it.
TikTok has also introduced Footnotes, a crowdsourced feature that allows eligible contributors to add additional context to videos. Similar to Meta’s Community Notes, TikTok’s system looks for agreement among contributors with different perspectives before a footnote becomes visible. Importantly, TikTok has presented Footnotes as an additional tool rather than a complete replacement for its existing fact-checking and moderation systems.
TikTok — Footnotes in the United States
TikTok still faces many of the same problems as Instagram. A false or misleading video can spread extremely quickly, while fact-checkers and contributors need time to investigate it. The massive amount of content uploaded to TikTok also makes it unrealistic for human moderators to evaluate every factual statement.
One thing I would improve is to put source information more directly into the viewing experience. For example, videos making factual claims about health, major breaking news or other important topics could offer a simple way for users to see relevant authoritative information. TikTok could also put in place stronger prompts before users repost content that is under review or has been identified as unverified. If we can set up a few seconds of friction, maybe someone will think twice before immediately sharing dubious information.
Comparing the Two Platforms
In comparing Instagram and TikTok, what stood out to me is that misinformation policies aren’t simply about deleting posts. The two companies are also testing crowdsourced context systems, but they also differ in their approach to distribution of questionable information.
Community Notes contributors are more central to Meta’s current U.S. strategy than its earlier third-party fact-checking system. Like Facebook and Twitter, TikTok also uses crowdsourced Footnotes, but it has stuck to the narrative that independence testing, moderation, and recommendation restrictions are part of its bigger approach.
This distinction matters because recommendation algorithms can influence whether misinformation reaches 100 people or one million people. In my opinion, simply attaching context to a misleading post may not always be enough if an algorithm continues recommending that post to large numbers of users.
Conclusion
I’ve done some research on Instagram and Tiktok I think one of the biggest challenges is speed. Misinformation can travel faster than fact-checkers, moderators or Community Notes contributors can respond. Both platforms have developed tools that can add context, detect manipulated content and limit some forms of misinformation, but none of these approaches can flag every misleading post.
A stronger approach would integrate reliable external information, crowdsourced context, less amplification of questionable data by algorithms, and media literacy education. Platforms should also make it easier for people to find and investigate original sources, rather than asking them to either trust a warning label blindly or figure everything out on their own.
Users still have responsibility for deciding what they believe and share, but platforms determine much of what appears in front of those users in the first place. Because of that, the design of recommendation systems and misinformation policies can have a major effect on what information spreads. Instagram and TikTok’s current efforts show that platforms can intervene, but continuing to improve the speed, transparency, and visibility of those interventions will be important as misinformation becomes more difficult to recognize.
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