This document summarizes a study that analyzed images related to gun control shared on Twitter to understand what characteristics predict sharing behavior. The researchers developed a coding framework to classify images based on appeals, frames, emotional valence, and intensity. They found that rational appeals and positive valence were most likely to be shared. Attribute frames and positively valenced images had the highest average number of retweets. The study aims to build a predictive model for how image characteristics influence diffusion on social media.

1. What Makes an Image Worth a Thousand Words?
A Content Analysis of #guncontrol-related Image Characteristics That
Predict Sharing Behavior
• Dr. Mike Egnoto, Visiting Assistant Professor, Media Arts, Sciences and Studies, Ithaca College
• Weiai (Wayne) Xu, PhD Candidate, Department of Communication, SUNY-Buffalo
• Dr. Gregory D. Saxton, Associate Professor, Department of Communication, SUNY-Buffalo
• Dr. Michael A. Stefanone, Associate Professor, Department of Communication, SUNY-Buffalo

2. Not all images are created equal.

3. Why Study Images and Virality?
• Network
• Content
• Source
Textual characteristics
Visual characteristics
What Image Characteristics Predict Sharing Behavior ?

4. A Typology of Image Characteristics
Appeal
fear
One/two
sided
sex
metaphor
threat
emotional
rational
ethos
humor
Frame
Valence
Attribute
Goal
other
Intensity
No
Low
Medium
High
Intended
valence
No
Negative
Positive
Human
presence
No
Yes

5. Examples of Appeals
Fear One/two sided Emotion (other than fear)

6. Examples of Appeals
Metaphor Sex Rational

7. Examples of Appeals
Ethos
Humor
Threat

8. Examples of Frames
Goal frame
Attribute frame
Risky choice frame

9. Research questions
RQ1: What proportion of image-based appeals are emotional, rational, or mixed?
RQ2: Which image-based appeals are most effective in terms of message propagation?
RQ3: What is the proportion of risk, attribute, and goal framing in these images?
RQ4: Which frames are most effective in terms of message propagation?
RQ5: What is the proportion of positive, neutral, and negative emotional valence in these
images?
RQ6: Which emotional valences are most effective in terms of message propagation?
RQ7: What is the proportion of low, medium, and high emotional intensity images?
RQ8: Is there an optimum level of emotional intensity regarding the propagation of these
images?

10. Data Description
• Timeframe: October 1st through 15th of 2013
• Twitter hashtag: #guncontrol
• 8,306 of which were original tweets
• 486 tweets contain image
• 138 images were selected, which yielded 101 usable images for coding

11. Results: frequency count
Appeals Frequency Combined total
Fear 9
Emotional 12
Ethos 2 23
Threat 0
Rational 28
Metaphor 6
1 / 2 sided argument 4 38
Humor 23
Sex 2 25
Other / no appeal 15
All frequencies n =101.

12. Results: frequency count
Frame Frequency Combined total
Risk frame 2
Attribute frame 17
Goal frame 24
Other/no frame 58
Valence Frequency Combined total
Negative 42
Positive 23
Neutral 36
Intensity Frequency Combined total
Low 56
Medium 9
High 0
No valence 36
All frequencies n =101.

13. # Retweets
Results: retweet count
count mean sd min max
All messages 101 1 2.149 0 18
Valence Categories
Negative Valence 42 .929 1.257 0 5
Neutral Valence 36 .722 1.466 0 7
Positive Valence 23 1.565 3.764 0 18
Frame Categories
No Frame 58 .862 1.1615 0 4
Valence Frame 2 1 1.414 0 2
Attribute Frame 17 2.059 4.507 0 18
Goal Frame 24 .583 1.213 0 5
Intensity Categories
none 36 .722 1.466 0 7
low 56 1.179 2.57 0 18
medium 9 1 1.5 0 4

14. Results: Retweet Count

15. Results: Retweet Count

16. Results: Retweet Count

17. Results: Retweet Count

18. Results: Correlation Matrix
1 2 3 4 5 6 7 8 9 10
1. Retweet count 1
2. Risky Choice Frame 0 1
3. Attribute Frame 0.22* -0.06 1
4. Goal Frame -0.11 -0.08 -0.25* 1
5. Negative Valence -0.03 -0.12 -0.004 0.33*** 1
6. Positive Valence 0.14 0.26** 0.01 -0.30** -0.46*** 1
7. Follower count 0.15 -0.07 0.07 -0.08 -0.06 0.08 1
8. Human Presence 0 -0.14 0.13 -0.05 0.27** -0.12 0.30** 1
9. Hashtag Count 0.01 0.02 0.09 0.07 0.15 -0.07 -0.03 0.06 1
10. Mentions Count 0.00
5
-0.11 -0.02 -0.10 -0.0004 -0.01 0.27** 0.18 -0.14 1
Zero-Order Correlation Matrix
t statistics in parentheses
* p < 0.05, ** p < 0.01, *** p < 0.001

19. Results: Negative Binomial Regression
# Retweets for different types of Frames #Retweets for different types of Valence
Valence Frame 0.16
(1.23)
Attribute Frame 0.77+
(0.46)
Goal Frame -0.48
(0.47)
Negative Valence 0.39
(0.44)
Positive Valence 0.91+
(0.49)
# followers 0.00
(0.00)
0.00+
(0.00)
Human Presence in image -0.18
(0.39)
-0.20
(0.42)
# hashtags 0.05
(0.07)
0.06
(0.07)
# user mentions 0.04
(0.19)
0.04
(0.20)
_cons -0.43
(0.39)
-0.83+
(0.49)
N 101 101
Pseudo R2 0.084 0.067
Model Significance (2) 8.83 7.00
Log likelihood -131.69 -132.61

20. The big picture
• A theory-guided coding framework for images
• An exploratory predictive model for image diffusion based on image
characteristics
Supported by the grant from Air Force Office of Scientific Research (AFOSR)
Title: Socio-Cultural Media Sharing as Conversations: Sensing and Modeling
Behavior in Response to Environmental Changes

21. THANK YOU!
Dr. Mike Egnoto, megnoto@ithaca.edu
Weiai (Wayne) Xu, weiaixu@buffalo.edu
Supported by the grant from Air Force Office of Scientific Research (AFOSR)
Title: Socio-Cultural Media Sharing as Conversations: Sensing and Modeling
Behavior in Response to Environmental Changes