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.

Some you’d expect (alcohol, chocolate), some are ironic (giving up Lent for Lent, giving up giving up things), some are odd (pants, lint), some are anti-religious (religion, Catholicism), and some are tech-related (Facebook, Twitter—even “Facebook and Twitter” makes the list).

Complete List

1. Facebook (654)
2. Twitter (317)
3. Chocolate (272)
4. Lent (216)
5. Alcohol (187)
6. Soda (139)
7. Coffee (129)
8. Meat (126)
9. Religion (102)
10. Swearing (94)
11. Sweets (92)
12. Catholicism (90)
13. Giving up things (80)
14. Work (70)
15. Beer (60)
16. Sex (59)
17. Fast food (57)
18. Facebook and twitter (57)
19. Sugar (45)
20. Stuff (43)
21. Booze (41)
22. Smoking (39)
23. Food (39)
24. Procrastination (38)
25. Internet (37)
26. Cursing (36)
27. Caffeine (35)
28. TV (33)
29. Pancakes (33)
30. Social networking (33)
31. Sleep (32)
32. Candy (32)
33. Diet Coke (29)
34. Giving up (29)
35. You (28)
36. Wine (28)
37. Lint (28)
38. Cheese (28)
39. Bread (26)
40. Shopping (26)
41. Sobriety (26)
42. Abstinence (24)
43. Cussing (24)
44. Red meat (24)
45. Chips (23)
46. Internet porn (22)
47. Christianity (22)
48. Nothing (21)
49. French fries (21)
50. Jesus (21)
51. Sarcasm (19)
52. Junk food (19)
53. Starbucks (18)
54. Ice cream (18)
55. MySpace (18)
56. Cookies (18)
57. Fried food (17)
58. Complaining (17)
59. God (16)
60. New years resolutions (15)
61. Social media (15)
62. Pizza (14)
63. Tweeting (14)
64. Carbs (13)
65. MySpace and Facebook (13)
66. Carbon (13)
67. Eating out (13)
68. Stress (13)
69. Flaky guys (12)
70. Laziness (12)
71. Texting (12)
72. Me (11)
73. Some of your money (11)
74. Annoying me (11)
75. Sacrifice (11)
76. School (11)
77. Hope (10)
78. Rice (10)
79. Coke (10)
80. Porn (10)
81. The snooze button (10)
82. Guilt (10)
83. Men (9)
84. Obama (9)
85. Church (9)
86. My job (9)
87. Homework (9)
88. Self denial (9)
89. Moderation (9)
90. Exercise (8)
91. Bacon (8)
92. Dieting (8)
93. Paying taxes (8)
94. Dr Pepper (8)
95. Gossip (8)
96. Beef (8)
97. Pants (7)
98. My sanity (7)
99. Celibacy (7)
100. Shaving (7)

About

Created using the Twitter Search API and Wordle. Data based on analysis of 15,000 tweets from February 22-26, 2009.