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

Not all images are created equal.

Why Study Images and Virality?

• Network

• Content

• Source

Textual characteristics

Visual characteristics

What Image Characteristics Predict Sharing Behavior ?

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

Examples of Appeals

Fear One/two sided Emotion (other than fear)

Examples of Appeals

Metaphor Sex Rational

Examples of Appeals

Ethos

Humor

Threat

Examples of Frames

Goal frame

Attribute frameRisky choice frame

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?

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

Results: frequency count

All frequencies n =101.

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

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

Results: frequency count

All frequencies n =101.

# Retweets

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

Results: retweet count

Results: Retweet Count

Results: Retweet Count

Results: Retweet Count

Results: Retweet Count

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

Results: Correlation Matrix

# 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

Results: Negative Binomial Regression

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

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