The dataset provides a list of Twitter accounts of researchers based on a seed set of computer science conferences, and matched against their profile in DBLP (a computer science bibliography hosted at the University of Trier, https://dblp.uni-trier.de/).
The goal is to provide (young researcher) a mean to identify relevant and reliable expert researchers on specific topics. At the moment, data are related to computer scientists, though an extension to other disciplines is planned.
Example Use Case
Let us consider, we would like to know the opinions of experts on a topic in Computer Science that is trending e.g. Artificial Intelligence (AI). Some people may have the opinion that AI will replace us humans while others hold that this is exaggerated and it is far fetched and instead we should focus on how to build better AI systems that work for everyone.
One of the ways to find out about experts is by looking up digital libraries such as the DBLP computer science bibliography to see the people who are publishing on a topic, https://dblp.uni-trier.de/. E.g., let's say we know Andrew Ng., a professor at Stanford is one expert whose opinions will matter because he does publish articles on AI.
Using the list of researchers on Twitter from the Twitter-researcher dataset
https://raw.githubusercontent.com/L3S/twitter-researcher/master/data/candidates_classified.tsv,
one can find the corresponding Twitter account - https://twitter.com/AndrewYNg.
By scanning his recent tweets, one can see the following tweet :
https://twitter.com/AndrewYNg/status/1006204761543081984
AI+ethics is important, but has been partly hijacked by the AGI (artificial general intelligence) hype.
Let's cut out the AGI nonsense and spend more time on the urgent problems: Job loss/stagnant wages, undermining democracy, discrimination/bias, wealth inequality.
This way, students can get well informed opinions from people who are actually experts on a subject matter. This method can be applied for different topics such as Cryptocurrency, Autonomous Driving, Robotics etc.
Similarly, one can create a list of researchers to follow on Twitter using the list,
https://raw.githubusercontent.com/L3S/twitter-researcher/master/data/candidates_classified.tsv,
based on their topic to stay up to date with a constant stream of opinion feeds from these topical experts.
REFERENCES
Asmelash Teka Hadgu and Robert Jäschke. 2014. Identifying and Analyzing Researchers on Twitter. In Proceedings of the 6th Annual ACM Web Science Conference (WebSci '14). 23-30. ACM, New York, NY, USA.
DOI: 10.1145/2615569.2615676. https://dl.acm.org/citation.cfm?doid=2615569.2615676