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Here’s a presentation I just gave at the BibleTech 2010 conference about how people tweet the Bible:

Also: PowerPoint, PDF.

I distributed the following handout at the presentation, showing the popularity of Bible chapters and verses cited on Twitter. It displays a lot of data: darker chapters are more popular, the number in the middle of each box is the most popular verse in the chapter, and sparklines in each box show the distribution of the popularity in each chapter. (Genesis 1:1 is by far the most popular verse in Genesis 1, while Genesis 3:15 is only a little more popular than other verses in the chapter.)

Let’s look a little more at some of the data on what Twitterers are giving up for Lent.

Categories of Things Given up by Location

As I only track in English what people are giving up, there are concentrations in English-speaking countries.

Size indicates the relative number of Twitterers in each country giving up something for Lent.

Categories of Things Given up by State

These visualizations show the differences (or lack thereof) in what people are giving up among U.S. states.

Size indicates the relative number of Twitterers in each state giving up something for Lent. Sorry, Alaska and Hawaii.

The composition of each state’s categories of tweets shows mostly minor variations among states. Some states (like Wyoming on the far right) have small numbers of tweets. I would have liked to use opacity or width to indicate this disparity but couldn’t figure out how to do it.

Comparison between 2009 and 2010

This treemap shows how the data changed between 2009 and 2010. The size of the box shows the number of people giving up each category and thing, while color indicates the percentage change from last year: dark blue indicates the steepest drop; dark orange indicates the steepest rise. The second chart shows the same data more conventionally expressed.

About the Visualizations

I created these charts mostly to explore how the new data-analysis software Tableau Public works. One of its claims to fame is that you can publish interactive visualizations to the web, a feature I didn’t take advantage of here. Tableau doesn’t do treemaps, so I used Many Eyes to create the treemap; the closest Tableau equivalent appears below the treemap.

Snow makes the list this year, understandable given the Snowpocalypse and Snowmageddon that gripped much of the Eastern U.S. in the weeks preceding Ash Wednesday. IPods also made the list after the Bishop of Liverpool asked people to consider praying instead of listening to them. This year a celebrity, Justin Bieber, cracks the top 100. He beat out the Jonas Brothers, 64 votes to 11; draw your own conclusions.

The list largely tracks last year’s list. It draws from 40,000 tweets retrieved February 14-20, 2010.

Complete List of the Top 100

Rank	Word	Count	Change from last year's rank
1.	Twitter	2089	+1
2.	Facebook	1874	-1
3.	Chocolate	1323	0
4.	Alcohol	1258	+1
5.	Swearing	1158	+5
6.	Soda	1126	0
7.	Lent	792	-3
8.	Meat	720	0
9.	Sex	701	+7
10.	Fast food	695	+7
11.	Sweets	627	0
12.	Coffee	445	-5
13.	iPod	437
14.	Candy	325	+18
15.	Religion	305	-6
16.	Catholicism	264	-4
17.	Smoking	254	+5
18.	Junk food	251	+34
19.	Giving up things	241	-6
20.	Beer	241	-5
21.	Chips	234	+24
22.	You	233	+13
23.	Stuff	217	-3
24.	Fried food	199	+33
25.	Red meat	193	+19
26.	Bread	187	+13
27.	Sugar	183	-8
28.	Work	176	-14
29.	Shopping	174	+11
30.	Food	162	-7
31.	Shame	150
32.	Social networking	147	-2
33.	Caffeine	136	-6
34.	Rice	136	+44
35.	Procrastination	127	-11
36.	Internet	126	-11
37.	Cheese	120	+1
38.	Coke	120	+41
39.	Starbucks	119	+14
40.	School	118	+36
41.	Ice cream	118	+13
42.	Booze	117	-21
43.	Texting	114	+28
44.	Masturbation	111
45.	Cookies	110	+11
46.	TV	97	-18
47.	Christianity	96	0
48.	Snow	96
49.	Wine	92	-13
50.	Pizza	91	+12
51.	MySpace	91	+4
52.	Men	90	+31
53.	Giving up	89	-19
54.	Sobriety	89	-13
55.	Liquor	87
56.	Desserts	87
57.	Lint	87	-20
58.	Pancakes	82	-29
59.	Homework	81	+28
60.	Marijuana	80
61.	Diet Coke	80	-28
62.	Hope	78	+15
63.	Virginity	76
64.	French fries	75	-15
65.	Laziness	71	+5
66.	Boys	67
67.	Nothing	67	-19
68.	Carbs	66	-4
69.	Justin Bieber	64
70.	Pork	64
71.	Porn	63	+9
72.	Me	62	0
73.	Sleep	61	-42
74.	Complaining	58	-16
75.	Eating out	58	-8
76.	Jesus	55	-26
77.	McDonald's	55
78.	Beef	54	+18
79.	Church	54	+6
80.	God	53	-21
81.	Abstinence	53	-39
82.	Cake	52
83.	Negativity	52
84.	Him	49
85.	Juice	47
86.	Celibacy	44	+13
87.	Chicken	42
88.	Lying	42
89.	New Year's resolutions	42	-29
90.	Sarcasm	42	-39
91.	Snacking	41
92.	My wife	39
93.	Tea	37
94.	iPhone	37
95.	Exercise	36	-6
96.	Sweet tea	35
97.	People	35
98.	Vegetables	34
99.	Pasta	33
100.	Self control	33

Image created using Wordle.

Melik Kaylan writes in today’s Wall Street Journal about how the detailed historical settings in the videogame Assassin’s Creed II allow the player to time-travel to Renaissance Italy (link works now but may not always):

[T]he game is set in Florence, Venice and Rome over a number of decades leading up to the year 1499. The game's producer-authors… labored lovingly to re-create the environs as exactly as possible. They hired Renaissance scholars to advise on period garb, architecture, urban planning, weaponry and the like. They took tens of thousands of photographs of interiors and streets. They used Google Earth liberally to piece together the ground-up and sky-down perspectives through which the action flows…. The hazy colors and the distant sound of river birds are uncannily correct. Nowadays, the tourist hordes can blot out all sense of history. Once you've navigated it on AC2, when you visit the Ponte Vecchio in person the illusion persists of a highly intensified sense of place. In other words, the video brings the place sharply back to life.

Recreating history comes at a price: the budget for the game is something north of $20 million. I hope that the publishers will find a way to put some of their investment to educational use; I for one would love to visit Renaissance Italy without having to assassinate people once I get there.

Someday I hope to see a recreation of ancient Jerusalem this detailed, though I can’t imagine what kind of game could justify the pricetag. In the future, maybe the cost of creating virtual time travel will drop far enough to be within reach of small schools, companies, or individuals.

(Note: I haven’t played the game and don’t intend to. As you might guess from the title, it appears to involve lots of killing. If you’re OK with seeing that kind of thing, on YouTube one of the developers walks through some of the gameplay.)

Search over 1.2 million Bible verses on Twitter–nearly every tweet that has mentioned a Bible verse since April 2009. You can also see a list of the most popular verses on Twitter over the past few hours (“Trending Verses”).

This project uses several APIs from Twitter and is still in a beta stage. It could evolve in several directions, but I want to see how people use it before developing it further.

It’s not quite realtime, but the most recent tweet is rarely more than a few minutes old.

Behind the scenes, it processes tweets to try to ensure their relevance; it has about a 92% accuracy rate based on a training corpus of around 45,000 tweets. Use the “relevant” and “not relevant” buttons in the interface if you see a tweet that you think should or shouldn’t belong. (I’m mostly interested in the latter, but it seems weird not to have both–like Facebook’s lack of an unlike button.)

It currently uses Logos RefTagger to link the Bible references in the tweets.

Feel free to leave a comment here if you have a feature idea or want to make any suggestions.

Edit February 2016: This feature is no longer available; with over 200 million tweets, it’s just too much data to serve reliably. Instead, the latest tweets are visible.

When people tweet about Bible verses on Twitter, what words do they use? Here are the top 100:

1. bible
2. lord
3. christ
4. gospel
5. psalm
6. god
7. psalms
8. corinthians
9. preach
10. shall
11. heaven
12. readings
13. church
14. spirit
15. righteous
16. verse
17. lectionary
18. verses
19. spiritual
20. ministry
21. pray
22. enemies
23. thou
24. tongue
25. creation
26. wisdom
27. deuteronomy
28. testament
29. strength
30. refuge
31. therefore
32. kingdom
33. romans
34. holy
35. thankful
36. thy
37. reading
38. rejoice
39. understanding
40. faithful
41. message
42. earth
43. blessed
44. exodus
45. deut
46. faith
47. wise
48. beginning
49. pastor
50. chapel
51. chapter
52. survey
53. anger
54. resurrection
55. risen
56. read
57. hearts
58. chronicles
59. salvation
60. flesh
61. servant
62. glory
63. praying
64. kings
65. sheep
66. praise
67. trust
68. prosperity
69. bless
70. heavens
71. deeds
72. toward
73. discussion
74. whoever
75. speaks
76. ye
77. hath
78. amen
79. teaching
80. thess
81. apostles
82. preparing
83. eph
84. eccl
85. path
86. fear
87. upon
88. presence
89. inspire
90. search
91. zechariah
92. seek
93. teach
94. wrath
95. commandments
96. believers
97. humility
98. spoke
99. thee
100. devo

Background

Extracting Bible references from text means identifying whether a given piece of text is referring to a Bible verse or something else. For example, the meaning of Acts 2 depends on context:

• Referring to Bible passage: Acts 2 recounts the early church.
• Not referring to Bible passage: She’s 5 years old but acts 2.

When you encounter a phrase that could be a Bible reference, you have to look at context to determine whether the phrase is a Bible reference. Humans can make this leap pretty easily, but computers need rigorous models and lots of training data to guess whether an ambiguous phrase is a Bible reference. In the above example, the phrase “early church” is a strong indicator that the phrase “Acts 2” is a Bible reference, while the phrase “years old” is an indicator the other way.

Twitter, with its high volume of content and decent search engine, provides lots of training data.

Methodology

Using the Twitter Search API, I downloaded 30,000 tweets possibly containing Bible references (e.g., [john 3], [jeremiah 29]) and then categorized them by hand as referring to a Bible verse or not.

I then ran a Naive Bayes algorithm on the resulting tweets to produce the above list, which contains the words that most strongly indicate the presence of a Bible reference.

This list suffers from sample bias, of course: a different set of tweets would produce a different list. In addition, the list is Twitter-centric; the results may not carry over into blogs or other media. (People substitute the number “2” for the word “to” and “4” for “for” on Twitter more frequently than they do elsewhere, for example, which oversamples content like “I’m meeting Matthew 4 dinner.”)

See It in Action

Search for Bible references on Twitter. Use the relevant and not relevant buttons to improve the filtering. I haven’t formally announced this new feature of OpenBible.info yet; consider the link a preview.

The new Bible Translation Personas experiment uses Aaron Zinman’s Personas project to scrape the web for mentions of Bible translations and then assigns a list of categories to each one, ultimately resulting in a colored strip that supposedly provides insight into the translation. As other commenters have noted, the animation process is more interesting than the result. In the case of Bibles, I’m not sure the result reveals much of interest.

Yingyan Huang, who created the two visualizations on the unity of Scripture from the last post, graciously agreed to answer a few questions for me about her work:

Where did you get the inspiration for your pieces? What was your motivation to create them?

Initially I had started with ideas of creating an information visualizations on Paul’s journeys and the spread of Christianity. As I explored the topic further, I realized that the focus, as a Christian, should not be on Paul but really on Christ Himself. Since the objective of Paul’s journeys and ministry was Christ. So I began exploring the Gospels and the role of Christ in the Bible. Ultimately my motivation is to visually demonstrate, given the Bible as a historical relic and literature, Christ’s centrality in the Bible and His reality because of the sheer number of references to Him found in the OT even before His birth.

What was the process you used to create them? Where did you get the data? What tools did you use?

My data was obtained through many reading and primarily comes from the Tyndale Life Application Study Bible, ESV Study Bible, and The Christ of the Covenants by O. Palmer Robertson. I cross referenced the passages from ESV’s history of salvation and Palmer’s book against the 250 events in the life of Christ from the Tyndale study bible. Some computer programs I used include Adobe Illustrator, Acrobat, and Microsoft Excel.

With infographics, there’s often a tension between making something attractive and clearly portraying the information you want to communicate. As a designer, how did you resolve that tension with these pieces?

As mentioned above, I basically cross-referenced the bible verses/passages to create data points. I did not create my own data, merely synthesized available data that have been around for centuries to create the data sets.

Why do graphic designers create infographics about the Bible?

Perhaps the Bible remains a choice subject matter because it is rich with intricacies and complexities that come together so harmoniously.

What are you up to these days?

I am currently interning/working at a magazine as a art/photo assistant and assisting in teaching an information design class at Parsons. I am also cooking and baking to test and create recipes. Additionally I am involved in starting a new college ministry (City Campus Ministry) and a teaching assistant for the Children’s Ministry at my church, Redeemer Presbyterian Church in NYC.

Yingyan Huang, a graphic designer, recently created two Bible visualizations that emphasize the unity of Scripture.

The first visualization, “The Harmony of the Gospels—250 Events in the Life of Christ,” identifies 250 events recorded in the Gospels, arranges them chronologically, and plots them to reveal which events occurred in which Gospels. In effect, it’s a visualization of the Composite Gospel Index, though the visualization is apparently using a different data set.

The second visualization, “A Single Story—The Bible through the Lens of 250 Events,” starts with the same 250 events but extends references to them into the Old Testament and the remainder of the New Testament. This visualization shows the centrality of Christ in the Bible.

Both visualizations convey useful information. I wish they were available in larger sizes, but even the low-resolution versions give you a sense of the message behind the images.

Download 750 verses (or short combination of verses) for your memory work or as the basis for your own list of verses. The Bible text in the file is the ESV and is therefore copyrighted, but the compilation of actual verse references is available under the usual (for OpenBible.info) CC-BY license. Do what you like with them.

The verses appear in descending order of popularity (as determined by an analysis of verses on this site), so if you’re only looking for 100 verses, you can just grab the first 100. There are 750 total to give you more than two years’ worth of daily verses, if you want.

Background

When I was putting together a list of daily memory verses a few years ago, I was surprised to discover that I couldn’t find a freely usable list of verses: I ended up combing through a bunch of books and combining various sources to produce a list of about 400 verses. If you’re doing something similar, why should you go through the same pain?