Here we present the entire analysis process and generated provenance visualization in our usage scenarios. We show this information as a video for Sec. 5.1, while presenting it with images for Sec. 5.2. Also, the video for Sec. 5.1 demonstrates how our prototype sytem works.
Section 5.1. Usage Scenario - School Aggression Network (Video)
Section 5.2: Usage Scenario - Twitter Information Communication Network (Images)
a) Original Interaction Steps
*A larger image can be seen by hovering mouse over or clicking each image.
Step 0.
Step 1: Adding `User' nodes.
Step 2: Adding `Patriot' nodes.
Step 3: Adding `Retweeted user' nodes.
Step 4: Filtering out `Retweeted user' nodes.
Step 5: Aggregating `User' nodes whose the patriotism identity is `No Patriot' as `Not Patriots'.
Step 6: Aggregating `User' nodes whose the patriotism identity is `Patriot' as `Patriots'.
Step 7: Aggregating `User' nodes whose the patriotism identity is `NA' as `Unknown'.
Step 8: Adding `Retweeted user' nodes.
Step 9: Filtering out `User' nodes.
Step 10: Aggregating `Retweeted user' nodes whose the patriotism identity is `No Patriot' as `Not Patriots'.
Step 11: Aggregating `Retweeted user' nodes whose the patriotism identity is `Patriot' as `Patriots'.
Step 12: Aggregating `Retweeted user' nodes whose the patriotism identity is `NA' as `Unknown'.
Step 13: Adding `User' nodes.
Step 14: Filtering out `Patriot' nodes.
Step 15: Annotating a finding.
Step 16: Filtering out `Retweeted user' nodes.
Step 17: Adding `Website Category' nodes.
Step 18: Aggregating `quality newspaper', `TV news station', `financial paper', and `foreign media' as `hard news'.
Step 19: Aggregating `web-only news media', `entertainment newspaper', and `news curation website' as `soft news'.
Step 20: Aggregating `personal media', `textboards', `Q&A sites', `image-hosting service', and `video-sharing service' as a `social media'.
Step 21: Aggregating `government', `political party or politician', `activist organization', and `neutral organization', as `political organization'.
Step 22: Aggregating `ad', `scam', and `unknown' as `other'.
Step 23: Filtering out `other' node of `Website Category' nodes.
Step 24: Filtering out `unknown' node of `User' nodes.
Step 25: Annotating a finding.
b) Reduced Interaction Steps
Step 0.
Step 1: Adding the related nodes with the finding in Step 8 below.
Step 2: Aggregating `User' nodes whose the patriotism identity is `No Patriot' as `Not Patriots'.
Step 3: Aggregating `User' nodes whose the patriotism identity is `Patriot' as `Patriots'.
Step 4: Aggregating `User' nodes whose the patriotism identity is `NA' as `Unknown'.
Step 5: Aggregating `Retweeted user' nodes whose the patriotism identity is `No Patriot' as `Not Patriots'.
Step 6: Aggregating `Retweeted user' nodes whose the patriotism identity is `Patriot' as `Patriots'.
Step 7: Aggregating `Retweeted user' nodes whose the patriotism identity is `NA' as `Unknown'.
Step 8: Annotating a finding.
Step 9: Adding the related nodes with a finding in Step 16.
Step 10: Aggregating `quality newspaper', `TV news station', `financial paper', and `foreign media' as `hard news'.
Step 11: Aggregating `web-only news media', `entertainment newspaper', and `news curation website' as `soft news'.
Step 12: Aggregating `personal media', `textboards', `Q&A sites', `image-hosting service', and `video-sharing service' as a `social media'.
Step 13: Aggregating `government', `political party or politician', `activist organization', and `neutral organization', as `political organization'.
Step 14: Aggregating `ad', `scam', and `unknown' as `other'.
Step 15: Filtering out `other' node of `Website Category' nodes.
Step 16: Annotatign a finding.