Data quilting: Art and science of analyzing disparate data

Figures & data

Figure 1. Data quilting framework.

Table 1. Types of visual categories

Types	Values	How It is Determined
Contact (Gaze)	Demand	Gaze at the viewer
	Offer	Absence of the gaze
Social Distance	Intimate/Personal	Head and Shoulders or Less
	Social	Cutting off human figure between waist and knee
	Impersonal	Full figure in frame
Modality	Real	Exactly seen as reality by the naked eye
	Imprints of reality	Abstract
Attitude (Angle)	Frontal	Viewer and subject are looking head on
	Oblique	Viewer and subject are not looking head on
	High	Viewer is looking down on subject
	Eye Level	Viewer and subject are at eye level
	Top-Down	World from a god-like perspective
	Rear	The shoulder of one person and the rest of the scene
	Low	Viewer is looking up on subject

Figure 2. Stitching threads.

Table 2. Metafunction definitions for video analytics (Source: Carmen D. Maier)

Representation	Orientation	Organization
We analyze what meanings represent visually, verbally, musically or sound-wise	We analyze how meanings position characters and readers-viewers	We analyze how meanings are sequenced and integrated into dynamic texts
What is the subject which the shots portray? What it, he, she or they is/are doing? What kind of music track is used? What kinds of associations are suggested through it? What kind of verbal and/or speech track are employed? What is the verbal or speech track about?	Do we see people in close up or long shot? Does the camera use a high angle, a low angle, a side-on or oblique angle? Does the camera move with the subject and in that way constructs dynamism, urgency and immediacy? Does the camera keep its distance and stand steady?	How are meanings linked together? In what order are the meanings linked? In what kind of rhythmic units?

Table 3. Summary of visual and music analytics

Shot	Frames	Representational	Orientation	Organization	Voice/Music
1		D is working out in an at-home gym. His voice is that of the thief who stole his identity and is using his cards for themselves. D’s gaze is straight toward viewer	Gaze: Demand/affiliation Frame: Close/Social Angle: Frontal/Eye level Modality: Real Color: High sensory	Elaboration: Long to close Extension temporal: Simultaneous Event	Speaking voice: High and frantic
2		Shot gets closer. D, gazing straight at viewer, only broken when he begins to sing.	Gaze: Demand/affiliation Frame: Far/Personal Angle: Frontal/Eye level Modality: Real Color: High sensory	Elaboration Extension temporal	Speaking voice: High and frantic
3		D is singing, almost possessed by thief. He ends singing sequence with a straight face as if he became his normal self. White text appears and says “Darrel P. Identity Theft Victim”	Gaze: Demand/affiliation Frame: Far/Personal Angle: Frontal/Eye level Modality: Real Color: High sensory	Elaboration Extension temporal	Mode: Minor Tempo: Moderate Pitch: High Harmony: Consonant Dynamics: Moderately loud
4		Screen is white with statement that says “Citi Identity Theft Solutions: Talk to a real person” flips to a CitiBank Card, then checkbook, then back to Citi logo with citi.com on the bottom.			Mode: Major Tempo: Moderate Pitch: High (whistling) Harmony: Consonant Dynamics: Moderately loud Speaking voice: moderate and calm

Figure 3. Video comment text word cloud.

Figure 4. Sentiment classification using AFINN, bing and NRC lexicons.

Figure 5. Video views regression plot.

Figure 6. YouTube search interest (google trends) in “identity theft” from 1/1/2008–7/6/2018.

Figure 7. YouTube Search Interest (google trends) in “identity theft” from 1/1/2008–7/6/2018, US only.

Figure 8. a. Data quilting example layers. b. Data quilting example layers. c. Data quilting example layers.

Figure 8. Continued.