Implement an API / Bulk access call to notify users that their tweets were / are used in a study, with the option to opt-out, or opt-in to more information from the researcher. Maybe a DM with links to IRB / Home Institution / Researcher contact details?

Ideally, i'd like to notify and ask permission from people - in the past, i've implemented this as a Twitter list, adding people to give them some notification - but this doesn't give people an easy opt-out.

Given the number of times data is used, maybe an equivalent to the list of advertisers is also viable? Similar idea, API to submit User / Tweet IDs, and a section in the Settings showing a list of all the researchers that have used your data - exactly the same as the Advertisers list in "Tailored audiences" report.

In order to increase the engagement of Twitter with academic community, a fellowship program could be a valuable strategy. Basically what a I propose is that Twitter can have a program in which invites researches and academics to submit project ideas using the Twitter Data. Twitter will be then select the best ones to provide support (giving exclusive data access, internships, scholarships for PhD students, etc) in the areas required by the project. This of course can be (please!!!) a parallel initiative to the expansion of access for academic purposes. This is not a new idea. Facebook already have something like this, and I, as PhD student, am using the Academic Programs of Google Cloud Platform to store and process data.

You probably have something like this running, but I haven't see it. In that case make it visible so we can apply.

This is advantageous for you cause better PR, but also cause there is many scientist than want to tackle some online problems that the public opinion want companies to solve, like online trolling and hate speech, polarisation, Fake news, etc. By allowing research to partner with Twitter, you can improve your PR, help science, and have some clues of to resolve some pressing issues in your platform.

I'd like access to predefined datasets that provide ideas for my research and make it easy to get started.

This falls as a more-involved way for Twitter as a cooperation to support academic research on its platform (rather than just extending API limits for .edu emails etc..) but one avenue for very supportive, engaged involvement would be curation and publication of topics.

That is to say, imagine Twitter itself publishes corpora of tweet IDs corresponding to major events like natural disasters, political events, etc... This creates a streamlined way for those studying the social dynamics surrounding major events to implement real-time study of ongoing events, and broaden the scope of existing analyses.

Currently, the study of past events relies on hydrating tweet IDs from previous major events all compiled by data-horders and college librarians and archivists. And because of the non-trivial steps involved in accessing the data, to my knowledge there are only about 15-20 examples of 'high quality' datasets which would be suitable for use in any major, longitudinal study. Twitter leading the way in publishing these events would serve not only as a long-term marker in history, but as a reference for researchers the world over.

There are certainly individuals out there who would rather compile their own topics-- this is a rich research area in itself-- however there are many disciplines where the current barrier to entry for studying the twitter discourse around an event is prohibitively high. A Twitter-curated list of Tweet IDs surrounding an event (perhaps compiled based on keywords, location, and networks properties?) could be shared as a de-facto resource in the Online Social Network field.

It would represent a significant effort on Twitter's part to actively maintain and curate these databases. However it would go beyond what any other social media site has ever done in terms of supporting academic research.

What I would like is the ability to use an API to alert Twitter to the presence of malicious actors interfering in elections. Ideally, it would be process whereby we crowd source and gamify the detection pieces, with Twitter's decision to suspend (or not) as the ultimate validation determining who is best at the game.

Recently, I sent 500 Canadian election Twitter IDs our model saw as bots/automated to a third party scholar: Kurt Wirth <kurtawirth@gmail.com>. I asked him if I could share his response to what I sent, he said yes. His response is below:

---------- Forwarded message ---------
From: Kurt Wirth <kurtawirth@gmail.com>
Date: Fri, Sep 27, 2019 at 4:18 PM
Subject: Re: Bots
To: Shulman, Stu <stu@texifter.com>

Hi Stu!

Here are the results for the first set. In this set, 28.7% of the accounts are estimated to be completely automated (as shown in cap.universal) and 66.4% are estimated to be at least partially automated (as shown in scores.universal). This number is very high. In other research, I've found approximately 6-6.5% of all accounts are bots and in political discussions the number drops to around 3%. I will get your second set started now.

Idea: The content of the Twitter "data library” to provide data about topics in high demand among researchers and enables to assess a variety of subtopics.

Example: I’m studying about tweets of an election to understand the population and presidential candidate sentiment on green card and public assistance. My first stab API query would be “election”, “green card”, and “welfare”. The larger topic here would be the US Presidential Election. Instead of posting the specific API request, the Twitter "data library” (hypothetically) would already have a substantially sized dataset of all the tweets related to the US Presidential Election.

Even if the Twitter "data library” did not satisfy the exact inquiry, it is still helpful to have similar content. The researcher can do an exploratory analysis of the available data, evaluate if the content is likely available or worth pursuing, and refine the research question.
Example: Initial research question asks “Do Social Media Influencers encourage recycling?”. The “Twitter data library” has a similar dataset on replies/engagement tweets of Social Influencers that post about climate change initiatives. The researcher evaluates the available dataset and finds that most replies don’t engage with the subject and only compliment the looks of the Social Influencer. With this preliminary insight, the researcher can improve the question.

Last, researchers also think “with this available dataset, what are the questions that I can solve?”. Given limited availability and quality of data, researchers would often use the same dataset.