NUS Research Webinar TODAY 3pm - "Estimating Geographic Subjective Well-being from Twitter: A Comparison of Dictionary and Data-Driven Language Methods" by Dr Kokil Jaidka

[Kokil Jaidka]

Hello members,

My upcoming talk today might be interesting to some people on this mailing list who work with social media data and in the computational social science area. 

Happy to talk about it further with anyone who is interested in future projects!

Thanks and regards

Kokil

 

 

 

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Dr. Kokil Jaidka

Assistant Professor, Computational Communication

Program Coordinator, Data and Communication (Masters by Coursework)

Principal Investigator, NUS Centre for Trusted Internet and Community

 

National University of Singapore

https://kokiljaidka.wordpress.com

 

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You are welcomed to join us at the upcoming CNM Research Talk:

 

Estimating Geographic Subjective Well-being from Twitter: A Comparison of Dictionary and Data-Driven Language Methods
21^st August 2020 | 3.00pm | Online Webinar | Registration is Free! Limited Seats Available. Register Online

In this talk, Dr. Jaidka presents her recent findings that were published in the Proceedings of the National Academy of Sciences in May 2020. Spatial aggregation of Twitter language may make it possible to monitor the subjective well-being of populations on a large scale.

Text analysis methods need to yield robust estimates to be dependable. On the one hand, we find that data-driven machine learning-based methods offer accurate and robust measurements of regional well-being across the United States when evaluated against gold-standard Gallup survey measures.

On the other hand, we find that standard English word-level methods (such as Linguistic Inquiry and Word Count 2015’s Positive emotion dictionary and Language Assessment by Mechanical Turk) can yield estimates of county well-being inversely correlated with survey estimates, due to regional cultural and socioeconomic differences in language use. Some of the most frequent misleading words can be removed to improve the accuracy of these word-level methods.