By the end of this tutorial you will learn how to create videos of dynamic networks like this one:
PART 1: Network Discovery in Netlytic
Data source: Twitter
Step 1: Connect your Twitter account to Netlytic
Note: Alternately, you can download the “SMSociety15” dataset that will be used for this tutorial from this cvs file from https://netlytic.org/home/wp-content/uploads/2015/07/smsociety151.csv
Step 2: Go to https://twitter.com/search-advanced and create a test search query. For today’s tutorial, type “#SMSociety15,” into the “Any of these words” field and select “English” from the “Written in” drop down menu.
Note: Confirm that your search query returns some results; otherwise, go back and revise your search criteria.
Step 3: Once you are satisfied with the search results, go back to Netlytic, select the Twitter option under the “New Dataset” menu, copy & paste the search query from Step 2 (“#SMSociety15 lang:en”) into the second text box (Twitter Search Terms), give any name to this dataset (for example, “#SMSociety15 Tutorial” and click the “Import” button.
Note: If you downloaded the “#SMSociety15” dataset from the link provided in Step 1, then use the “Text file” tab instead of “Twitter” to import your data into Netlytic.
The Import command will retrieve 1000 most recent tweets from Twitter that correspond to your search query. In our case, it is going to be any Twitter message that mentions the ”#smsociety15” hashtag and is written in English.
The next screen will confirm the number of messages that Twitter returned based on your chosen search query.
Step 4: Click “Next Step” to preview your dataset. This step is designed to confirm that your dataset was imported properly.
Note: Here you can select what fields to preview by clicking on the “Row Label Fields” drop down menu as shown below:
Step 5: Go to the “4. Network Analysis” menu, find the “Name Network” section and click the “Analyze” button.
Note: If you uploaded your dataset to Netlytic and did not import it directly from Twitter, select the dataset type as “Twitter” from the drop down menu before clicking on the Analyze button.
Step 6: Once the network is built, click on the “Visualize” button. The pop-up window will display the discovered network that represents “who mentions/replies/retweets whom”.
Note: Try changing the Layout, Node Size and Colors options in the left side menu. Discuss the changes and how they affect the interpretation of the network (help/hinder).
Step 7: Review some of the most connected members in the network, as indicated by the larger node size, and then read some of the messages exchanged among them and other Twitter users to understand the formation of connections in this network.
To access individual tweets, click on the node/person in question and then click on any of the connecting nodes/names listed in the left pane.
Step 8: Using the Notes feature (see below), annotate 3-5 different clusters/areas in the network that are indicated by different colors.
To annotate information about various clusters or individuals in the network visualization, use the yellow “Sticky Notes” feature. To activate this feature, click the yellow box containing a plus sign located in the bottom right hand corner of the network visualization window (see below).
Note: You can add “sticky notes” at the different levels of zoom in the network visualization. To navigate between zoom-levels, you can use “sticky notes” bookmarks that will appear at the bottom lower right hand corner of the network visualization screen along with information about the zoom level associated with each set of notes.(e.g., 0%, 25%, 50%, etc.)
Hint: To capture a snapshot of your network (and any sticky notes about your network), click on the “Export image” button in the left pane. You can only save and publicly share up to three snapshots at a time in the system. If you want to take and save additional snapshots of your network, you will need to save them to your computer first and delete them from the system to make room for new snapshots. For example, you might want to take additional snapshots because you want to show/document something interesting about the interactions of a particular clusters of users in the network.