Academic researchers often have multiple projects in development at one time, often surrounding the same topic and/or use case. Allowing researchers to have multiple research projects at once or to close out their single project and apply for a new one would help researchers to harness Twitter's data and derive insights more efficiently.

Instead of returning two differents objects "data" and "includes" when calling endpoints like search tweets, it would be much simplier to have all the data inside one object.
Currently we have :

  {
  data:[
        {
           // tweet 1
        },
        {
           // tweet 2
        }
     ],
  includes:{
        users:[
           {
              // user 1
           },
           {
              // user 2
           }
        ]
  }

better solution would be :
{
data:[
{
// tweet 1
// user 1
},
{
// tweet 2
// user 2
}
}

Instead of just having minute hour or day, give the possibility to use week and month granularity.

Twitter’s terms of service requires us to delete tweets we have collected when they are removed from Twitter. This makes academic research on certain topics practically impossible, as continually monitoring whether a tweet is still present is unfeasible, and much time could be spent on research that would to be have to have to be abandoned because using the data would violate Twitter’s terms of service. Could an exception requiring the deletion of tweets to the terms of service be made for academic research?

The sample operator that exists for Enterprise would be very useful for v2 Academic Access too: https://developer.twitter.com/en/docs/twitter-api/premium/rules-and-filtering/operators-by-product

Current tweet volume for academic research is high. But sometime a need may arise to access larger volume of data. This may not happen throughout the year, but at some point in the research cycle. To support this need:
1) Cumulative volume cap can be kept for four months or six months instead of per month.
2) Certain research projects where the objectives are well defined and transparently reported through means like pre-registration of studies, higher volume access may be granted, through certain means.

As an academic researcher, I would like to be able to search tweets using the API v2 and return, along with the tweet content itself, any misinformation labels associated with the returned tweets.

I would like to study who networks of bots tend to follow, but since they are quickly suspended this data doesn't seem to be available anywhere.

Measuring change over time is a fundamental research design. Having timestamped data on follows (A follows B at t) and likes (A likes X at t) would be immensely valuable alongside existing timestamped data on tweets and retweets.

Is there a tool to find out the engagement rates of promoted tweets by each twitter handle? This can be used to understand whether a paid/promoted tweet has better engagement than an organic tweet from the same twitter handle? Thanks!

I would love to have an endpoint which will allow me to search for accounts based on criteria like keywords in bio, location, min follower count, etc.

Real-time Stock Predicate

Privacy features for example on a public account being able to hide my followers or vice versa. Privacy screens like they have on Reddit for porn. It would be nice to tweet from my account stream with Porn being blurred when it needs to be blurred.

I request the Twitter to allow access to all the replies of a tweet (all the conversations of a thread). It would be very useful for academics working in user behavior analysis, argumentation mining, hate speech detection because comment or reply reflects the actual thought process of a replying user.

Twitter has launched some labels for Tweets that contain misleading information. For example, under those suspicious tweets, a label named as "Get the facts about.." will show up. However, these labels are not visible in the current json file. These labels are actually pretty interesting and valuable features because we can study the impact of it on the cascade of the misleading information, which will contribute to the current information cascading literature.