DiscoverText

  • Home
  • About
    • Tutorials
    • Customers
    • Testimonials
    • Text Analytics
    • Publications DiscoverText
    • Publications CAT
    • Terms of Service
    • Privacy Policy
    • French Explainer Video
    • Arabic Explainer Video
    • Spanish Explainer Video
  • Products
    • Solutions
    • Features
    • Support
    • NodeXL
  • Industries
    • Consumer
    • Education
    • Government
    • Human Resources
    • Legal
    • Medical & Pharma
  • Pricing
    • Feature Comparison
    • Subscribe Now
  • Blog
  • Support
  • Demo
  • Subscribe
  • Contact Us
  • Login

Using Twitter to Study Perceptions of Vaccine and GMO Safety

January 13, 2017 by Stuart Shulman Leave a Comment

Tweet

We often get field reports from graduate students who are deep into the exploration of Twitter data using DiscoverText. This excerpt below from Stanford’s Anita Tseng further illustrates why so many academics are using DiscoverText to collect and analyze Twitter data to better understand public sphere discourse.

“I am currently conducting a large-scale content analysis of Tweets on controversial science issues. My dissertation focuses on misinformation about science in new user-generated media, and one of my projects deals with illustrating the scope and nature of misinformation about controversial science on social media. For seven months, I collected Tweets on “vaccination safety” and “GMO safety” using multiple variations on each search term, collected at various times each day on a weekly basis. I aim to analyze these Tweets for sentiment, as well as the presence of errors in scientific reasoning, based on an existing framework in recent research on philosophy of science. After trying a number of data collection and analysis tools, I came across DiscoverText during a workshop on campus last year and was thoroughly impressed by the functionality and most importantly for me, user experience. After dealing with a number of other badly programmed analysis tools that were both slow and unintuitive, DiscoverText was fast for me to pick up, Web-based and did the grunt work of collecting onwards to 56,000 Tweets for me over the course of several months in 2016. I’m now in the analysis portion of this project and excited for the findings to develop — I am qualitatively coding a subset of my data and training the built-in machine learning algorithm to code the remainder so I can have a broader picture of the data. This spring, I will be presenting the project as a Computational Social Science Fellow at Stanford University, and at the National Association for Research in Science Teaching as part of the Informal Education division, which includes research on social media and its impact on public understanding of science.”

Anita is a Doctoral Candidate at Stanford University’s Graduate School of Education. Even though she is now an experienced user, we look forward to seeing her at the upcoming DiscoverText workshops January 17, 2017 on the Stanford campus. 

       

 

Filed Under: Coding, DiscoverText, research, Twitter

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Start a Free Trial Request a Web Demo Subscribe Today

 

(413) 992-8513

Recent Posts

  • 82 Reviews and Counting
  • Multilingual Capable Text Analytics Software
  • What we do better than anyone else…
  • Fake News & Other AI Challenges
  • DiscoverText Explained in Arabic, English, French & Spanish

Archives

  • February 2019
  • January 2019
  • December 2018
  • November 2018
  • October 2018
  • August 2018
  • July 2018
  • May 2018
  • April 2018
  • March 2018
  • January 2018
  • December 2017
  • October 2017
  • April 2017
  • March 2017
  • February 2017
  • January 2017
  • December 2016
  • September 2016
  • June 2016
  • January 2016
  • December 2015
  • November 2015
  • October 2015
  • August 2015
  • May 2015
  • April 2015
  • February 2015
  • January 2015
  • December 2014
  • November 2014
  • October 2014
  • September 2014
  • August 2014
  • July 2014
  • June 2014
  • May 2014
  • April 2014
  • March 2014
  • February 2014
  • January 2014
  • December 2013
  • October 2013
  • April 2013
  • December 2012
  • October 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • May 2011
​

Boolean defined search, n-grams, word clouds, and custom topic dictionaries are power tools for text analysis and machine-learning

Discover central topics and also elusive but valuable unexpected or rare concepts. Use this information to train machine-learning classifiers to recognize relevant text and social media data. Jump into data using an interactive word CloudExplorer or build a mini topic dictionary using “defined” search. Try our new listview for seeing the top 300 bigrams and trigrams in your data
Start a Free Trial

 

DiscoverText is Powered by uClassify

Create gold standard training sets using our state of the art collaborative annotation system and then use our machine learning web service (uClassify) to create and apply your own custom-trained text classifiers. Check out the great work being done by the uClassify community.

Copyright © 2019 · DiscoverText · 237 Shutesbury Rd.
Amherst, MA 01002 · Powered by ThriveHive