Presentation at SocInfo2014

1. U.S. Religious Landscape on Twitter
U.S. Religious Landscape on Twitter
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Lu Chen
chen@knoesis.org
Ingmar Weber
iweber@qf.org.qa
Adam Okulicz-Kozaryn
adam.okulicz.kozaryn@gmail.com
This work was done while the first author was an intern at Qatar Computing Research Institute.

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Religiosity is a powerful force
shaping human societies.
source: http://www.pewforum.org/2012/12/18/global-religious-landscape-exec/#

3. U.S. Religious Landscape on Twitter
•A key feature of any belief system such as religion is replication.
–Vertically: to new generations
–Horizontally: to new adherents
•As more religious leaders, organizations as well as believers start using social networking sites, online activities become important extensions to traditional religious rituals and practices.
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Social networking facilitates
the replication of Religion.
What can we learn about religion from social media?
“74% of online adults use social networking sites.”
January 2014
source: http://bit.ly/1qBBhgq

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Collecting Twitter users who
self-reported their religions in bios
https://followerwonk.com/bio/?q=Christian&q_type=bio
searching Twitter user bios with religion-specific keywords
Username @screen_name
Username @screen_name

5. U.S. Religious Landscape on Twitter
•The “undeclared” user group: random set of users who do not report any of the six religions/beliefs in their bios.
•The dataset comprises 250,840 U.S. Twitter users, the lists of their friends/followers, and 96,902,499 tweets.
•On average, Atheists appear to be more active than religious users, while the undeclared group generally appears to be less active than other groups.
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The dataset comprises 250,840
U.S. Twitter users.

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Data Validation
Twitter Bio Examples

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How does the fraction of
religious people of any belief within a given state
on Twitter correlate with that in surveys?
r = .79 (p < .0001)

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0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
Twitter
Pew Research
Atheist
r = 0.56 ****
ρ = 0.62 ****
65.00%
70.00%
75.00%
80.00%
85.00%
90.00%
95.00%
100.00%
80.00%
85.00%
90.00%
95.00%
100.00%
Twitter
Pew research
Christian
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
0.00%
1.00%
2.00%
3.00%
Twitter
Pew Research
Muslim
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
0.00%
2.00%
4.00%
6.00%
8.00%
Twitter
Pew Research
Jew
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.00%
1.00%
2.00%
3.00%
Twitter
Pew Research
Hindu
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
0.00%
2.00%
4.00%
6.00%
8.00%
Twitter
Pew Research
Buddhist
r = 0.23
ρ = 0.75 ****
How does the distribution of
religious people of a given belief across U.S. states
on Twitter correlate with that in surveys?
r = 0.73 **** ρ = 0.77 ****
r = 0.30 *
ρ = 0.48 ***
r = 0.77 ****
ρ = 0.79 ****
r = 0.16
ρ = 0.49 ***
source: http://religions.pewforum.org/
* significant at p<0.05; ** significant at p<0.005; *** significant at p<0.001; **** significant at p < .0001

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Do various denominations differ
in terms of their content?
The top 15 most discriminative words of each denomination based on a chi-square test
Each group is represented by a different color, and the font size of a word is determined by its chi- square score.
•The discriminative words are largely religion-specific.
•Non-religious terms also appear as discriminative features.

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Do various denominations differ
in terms of their friends?
The top 15 most discriminative Twitter accounts being followed by each denomination based on a chi-square test
Each group is represented by a different color, and the font size of an account is determined by its chi- square score.
•The discriminative Twitter accounts are also largely religion-specific.

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The top 15 most frequent words for each denomination.
The top 15 Twitter accounts being followed by most users of each denomination
•In a sense, people differ more in whom they follow rather than what they tweet about.

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Can we build classifiers to accurately identify believers of different religions?
•Tweet-based:
–Each user is represented as a vector of unigrams and bigrams (df >= 100) extracted from their tweets.
–An entry of the vector refers to the frequency of that ngram in the user's tweets.
•Friend-based:
–Each user is represented as a vector of their friends.
–An entry of the vector refers to whether the user follows an account.
•Binary classification: each denomination vs. undeclared user group
–SVM classifiers
–10-fold cross validation

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Can we build classifiers to accurately identify believers of different religions?
From easiest to hardest (based on F1 Score):
•Tweet-based: Atheist, Jew, Christian, Buddhist, Muslim, Hindu
•Friend-based: Muslim, Atheist, Buddhist, Jew, Christian, Hindu
•Network “following” features appear to be superior to content features.

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•Assortativity is a preference for a network's nodes to attach to others that are similar in some way. -- Wikipedia
•Connections
–following, being-followed-by, mentioning, and retweeting
•Raw proportions:
–For each user in our dataset, calculate the proportions of the in-group connections and the connections to users from other groups
–Get the average proportions of in-group and out-group connections for each group
•Expected proportions:
–Estimated by the fraction of users of a certain religion in a random user sample
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Does network assortativity exist ?

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Atheist
Buddhist
Christian
Hindu
Jew
Muslim
Atheist
46.6788
-0.0955
-0.8012
-0.294
1.0077
-0.7597
Buddhist
0.3621
82.8597
-0.8737
1.6318
0.179
-0.7264
Christian
-0.6304
-0.8647
2.2415
-0.917
0.0458
-0.8206
Hindu
0.1886
0.6009
-0.8651
737.2847
0.7622
-0.8373
Jew
-0.1657
-0.5392
-0.674
-0.7853
392.3228
-0.2192
Muslim
-0.6393
-0.8425
-0.8442
-0.6871
0.9668
60.6716
Undeclared
-0.4572
-0.7357
-0.6621
-0.8843
0.0908
-0.8164
Does network assortativity exist ?
The relative difference of the proportion of following a denomination to its expected value
Yes, users are much more likely to follow other users of the same religion/belief than of a different religion/belief.

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Does network assortativity exist ?
0.0466%
0.0259%
1.3358%
0.0013%
0.0207%
0.0414%
Yes, the assortativity exists in all types of connections across all the religious groups.
The proportion of same-religion relations of each religious group.

17. U.S. Religious Landscape on Twitter
•There is a moderate correlation between survey results and Twitter data.
–the macro-average Spearman's rank correlation of all the denominations is .65, regarding the distribution of religious believers of a given denomination across states
–Pearson Correlation is .79 (p < .0001), regarding the fraction of religious people of any belief within a given state
•Twitter users of a particular religion differ in what they discuss or whom they follow compared to undeclared users
•The network “following” features are more robust than tweet content features in identifying believers.
•Assortativity exists in all types of connections across all the religious groups.
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summary
Note: only the Twitter users who publicly declare their religion are included in our data, while vast majority of believers may not disclose their religion in bios and thus not included.

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There is interest in the topic!

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Thank you !
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CONTACT
Lu Chen:
http://knoesis.wright.edu/researchers/luchen/
Ingmar Weber: http://www.qcri.com/page?a=117&pid=67&lang=en-CA
Social Computing @QCRI:
http://qcri.com/our-research/social-computing
Kno.e.sis Center: http://knoesis.wright.edu/
source: http://bit.ly/1o4bcV5