Capturing the Conference Conversation: Part 1 of 3

Conference attendance is a significant part of professional development for graduate students, researchers, and faculty members. Conference attendees are increasingly using Twitter as a medium for discussion as well as to share resources, tools, and research at these events. However there is a challenge in consulting the “backchannel” in the days after the conference – especially if the tweet count runs into the thousands.

Some scholars have used Storify to capture and share Twitter conversations and while it has a clean interface the tools to navigate large amounts of data are limited. This post proposes an alternative method for capturing Twitter data using ScraperWiki and Viewshare to build rich, interactive, and shareable records of conference conversations.

ScraperWiki offers a powerful suite of pre-made data scraping tools and an environment for creating your own tools. This post focuses on using a pre-made Twitter data scraper.

To begin using Scraperwiki:

1. Sign up for an account
2. Click, ‘Create new dataset’.
3. Click, ‘Search for tweets’ (this will prompt you to authorize the app using your Twitter account)
4. Enter search term(s)
5. Click, ‘Download as spreadsheet’
6. Download data by clicking ‘all_tables.xlsx’

Here is an example of what your data will look like:

The next step is to sign up for a Viewshare account.

Viewshare is a free web-based digital collection visualization platform maintained by the Library of Congress. Viewshare offers a number of visualization options such as list view, timeline, map, gallery, and scatter plot. Setting Viewshare apart from similar tools are ease of use and the ability to share the visualization as well as the underlying dataset. To begin using Viewshare:

1. Sign up for an account
2. Click, ‘Upload Data’
3. Click, ‘From a file in your computer’ under Simple Spreadsheets
4. Click, ‘Choose file’, select your file, click, ‘Upload’
5. Remove data you do not want in your visualization by clicking the red box to the right of each row
6. Use the pull down menu to assign type of data to the row
7. Save the dataset, assign a title, provide a description, and choose the public or private publication option
8. Click, ‘Build’ and pick a canvas to ‘Create a Data View’ (this will determine where widgets will be placed)
9. Click, ‘Add a View’ and select view type(your view options will be limited by the type of data you have – if you do not have numerical data for example, the scatterplot view will not be available)
10. Adjust View Settings
    
11. Control what data you want to show in the View by clicking the checkbox and arrange the order of the data by dragging the rows into the desired order
12. Click ‘Add a Widget’ (widgets add features like search and filtering to your views)
13. Share/Embed your views

While this post focused on capturing and visualizing Twitter data, ScraperWiki and Viewshare are not limited to this data source nor is their full functionality expressed. Browse the ScraperWiki documentation for examples of other possibilities and take a look at Viewshare blog posts (one, two, three) and the examples below for inspiration.

Digital Humanities Oxford Summer School 2013
Western Soundscape Archive
TAMU Math Department