This is because content enforcement is not typically immediate — it can take days, weeks or longer. As you search back in time from the present, it becomes more likely content has been altered or removed. So you're drawing a conclusion on data whose completeness is not evenly distributed over time. This (among other things) led to some speculation regarding spam people found during recent COVID protests in major Chinese cities.

It appeared that adult spam using Chinese city names as hashtags was suddenly surging as the protests took off — making people suspect the CCP was trying to drown out information about the protests. Not initially implausible, but there are a number of problems with that assumption — for one, look at this plot showing volume of tweets mentioning these cities (which were overwhelmingly spam):

The graph above was from a search conducted on Nov 29th. It sure looks like tweets really took off on the 27th, increasing toward the present, right? That's the quick conclusion you would draw if you looked at just the previous few days. Problem is, this isn't enough data, and it's biased. Here's another one week query conducted after the protests had wrapped up:

This was conducted Dec 4th. Sure looks like...an increase in the past couple days, again. This is content moderation survivor bias in action, and if you did a similar search today, you'd probably find the same thing. Conversely, if you searched one of the original weeks today, you'd see the curve flatten partially. There are a few other problems that also led to these likely mistaken conclusions:

One is just plain recency illusion. Most people weren't searching for these hashtags before, so the surge *seemed* sudden to them. Secondly, people didn't go far back enough. Twitter's API isn't blazing fast and has rate limits as well as query caps when using elevated API access. Gathering enough data to make strong conclusions isn't free or fast. If you expand that initial search to go back 2 weeks instead of one, a different picture emerges:

Third, social media analysis and studies of CIB have often focused on state-backed actions, so we've kind of conditioned ourselves to attribute shady behavior to the governments of Russia or China. This is not entirely unreasonable given the inauthentic online operations those countries have engaged in, but it is a bias. And lastly, journalists and even academics want to publish fast about emerging events. Alas, sometimes we should be slow and as correct as possible, given messy data.