— Philip Mai (@PhMai) January 26, 2017
It seems that even an old dog can learn new tricks. I was experimenting recently with the list view settings in DiscoverText and was pleased to find a new way to review Twitter data. This 2-minute video outlines the steps you can take to get an optimal list view display for Twitter data in DiscoverText.
We have been very busy creating a set of unique Twitter data samples for the fake news student detection challenge. There are new collections of bot-mentioning Tweets, also by users with >100,000 followers, other influencer segments, top RT segments, and a 1.1 million item sample representing the seed of every duplicate group in the original 20 million Tweet collection. To learn about the metadata enhancements that make this and the influencer segments valuable, we have prepared a short video. Watch the video below to learn more about the free student data challenge and to get tips on why the best data samples are in fact great tricks for doing other kinds of important social media research. New teams are still getting organized. Write to firstname.lastname@example.org for more information. The deadline for submissions is April 1, 2017.
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.
The workshops at Stanford are always fun and challenging. So many of the brightest students are percolating great ideas in one place. Stanford students, staff, or faculty can sign up today for the upcoming January 17, 2017 sessions.
— DiscoverText (@discovertext) January 12, 2017