1. Quantifying Deception Propagation on Social Networks
Maria Glenski † and Svitlana Volkova
Data Sciences and Analytics Group, National Security Directorate
† Computer Science and Engineering, University of Notre Dame
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Data Collection
Inferring User Attributes
Predicted User Demographics
282 Twitter accounts previously identified1 as spreading verified (V) or deceptive news. Deceptive news sources are categorized as clickbait (CB), conspiracy (CS), propaganda (P), or disinformation (D).
Twitter data: collected all tweets posted in 2016 directly retweeting or mentioning verified or deceptive sources.
145,688 users with ≥ 5 English-interactions with deceptive sources.
Each user classified as person or organization using the Humanizr classifier2.
66,171 person-users with ≥200 public tweets available.
Learned attribute-specific CNN models
initialized with 200-dim GLOVE embeddings
using annotated data3: AUC ROC
Gender 0.89
Age 0.72
Income 0.72
Education 0.76
Inferred attributes of each user from their 200 most recent tweets.
Summary of attributes and the number of users classified as each demographic by our models:
Attribute
Class N
Gender
M : Male
63,259
F : Female
2,912
Age
Y : 24 and younger
3,336
O : 25 and older
62,835
Income
B : Below $35k
12,885
A : At least $35k
53,286
Education
H : High School
11,807
D : Bachelor’s Degree
54,364 V CB CS P D
% Person
91.47
92.33
91.50
93.03
89.76
# Users
83,785
8,367
16,520
57,314
30,456
Intent to Deceive V CB CS P D
. . .
Dense Layer
(100 units) Convolutional Layer (100 units)
Embedding Layer (200 units)
Word Sequence
Input
Attribute Class
Activation Layer
(soft max)
CNN architecture
Distributions of each attribute within each population and all users overall:
% Population
Population
Type
# sources
# tweets
# users posting
# users mentioned V
182
6,567,002
1,423,227
390,164 CB 11
40,347
19,361
6,002 CS 13
126,246
35,799
9,171 P 26
609,251
233,799
34,532 D 50
3,487,732
292,437
82,638
Total
282
10,819,357
1,784,655
471,967
Who is more likely to propagate?
Is there some group of users responsible for most of the propagation?
Mann Whitney U tests comparing binary features of whether users shared from sources of each type at least once, find who is more likely (p < 0.01) to propagate from each type:
We look at the Palma Ratios for the distributions of retweet frequencies of user-source pairs within each population where Palma Ratio= 𝑇𝑜𝑝 10% 𝐵𝑜𝑡𝑡𝑜𝑚 40% . If all users share an equal number of tweets, Palma Ratio= 1 4
The most active 10% of younger users retweeting disinformation sources share times what the least active 40% do.
Propagation of disinformation sources is more often spread by the same top 10% of users with higher incomes than lower incomes. V CB CS P D
Gender
Age
Income
Education
Women retweet conspiracy theory more evenly than men. #1
Overall, disinformation is propagated most unevenly, followed by conspiracy theory.
The biggest differences in behavior also occur with disinformation and conspiracy theory sources.
Key
M: Male F: Female
There is always a group of users sharing more than others.
All Palma Ratios > 0.25
Users in different age brackets have the greatest difference in Palma Ratios for verified
sources.
Y: 24 and younger O: 25 and older V CB CS P D
M-F
1.39
0.32
5.14
0.54
7.03
Y-O
-2.30
-0.35
-4.30
-0.67
23.19
B-A
0.72
2.75
1.02
26.50
H-D
-0.40
0.51
2.86
24.86 V CB CS P D
M/F
1.45
1.23
1.84
1.18
1.39
Y/O
0.49
0.80
0.35
0.81
2.28
B/A
1.07
1.43
1.46
1.3
3.95
H/D
0.91
1.30
1.48
1.21
3.33
B: Below $35k A: At least $35k
H: High school D: Bachelor’s Degree
Comparing populations using deltas (left) and ratios (right) of the Palma Ratios of each, by attribute.
Volkova, S., et al. "Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter." ACL Volkova, S. and Bachrach, Y. Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast. ACL 2016.
2. McCorriston, J., et al. "Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter." ICWSM 2015.
This research was conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy