Ph.D. defense presentation that was presented on June 16, 2016 in Computer Science Department at ODU.

1. Using Web Archives to Enrich
the Live Web Experience
Through Storytelling
Yasmin AlNoamany
Doctor of Philosophy
Dissertation Defense
06/16/2016
Committee Members:
§ Michael L. Nelson
§ Michele C. Weigle
§ Hussein Abdel-Wahab
§ M'Hammed Abdous
1

2. My son, Yousof, was 2 on
January 17, 2011
https://www.facebook.com/elshaheeed.co.uk/
2

3. No worries! Multiple initiatives for
documenting the Egyptian Revolution
403 photos with
information about the
lives of the martyrs
3,525 images and
2,387 videos posted by
people for the January
demonstrations
artwork produced
during the Egyptian
Revolution
3

4. Several studies and books about the
Egyptian Revolution
44

5. These repositories do not exist any more!
55

6. A year after the Egyptian Revolution, 11% of
the social media documentation is gone
Source: http://ws-dl.blogspot.com/2012/02/2012-02-11-losing-my-revolution-year.html
SalahEldeenet al. “Losing My Revolution: How Many Resources Shared on Social Media Have Been Lost?”, TPDL 2013
6

7. Luckily these sites are archived at Archive-It
in the Egyptian Revolution collection
http://wayback.archive-it.org/2358/20110314134348/http://iamjan25.com/
http://wayback.archive-it.org/2358/20110211072306/http://1000memories.com/egypt/
https://wayback.archive-it.org/2358/20111128095924/http://iamtahrir.com/
7

8. Archived collections are important
for posterity, but there are
problems with archived collections
8

9. After 10 years, Yousof knows
about Archive-It
> 3,500
collections
~340
institutions
> 10B archived
pages
Archive-It, a subscription-based service, hosts curated web collections
9

10. There is more than one collection about
the Egyptian Revolution
• “2010-2011 Arab Spring” https://archive-it.org/collections/3101
• “North Africa & the Middle East 2011-2013” https://archive-it.org/collections/2349
• “Egypt Revolution and Politics” https://archive-it.org/collections/2358
10

11. Current browsing and searching services for
the “Egypt Revolution and Politics” collection
11

12. Current browsing and searching services for
the “Egypt Revolution and Politics” collection
12

13. Current browsing and searching services for
the “Egypt Revolution and Politics” collection
13

14. Collection understanding is not
supported currently
Not easy to answer “what’s in that collection?”
14

15. There are ~17
collections about
human rights
15

16. Our early attempts at collection
understanding tried to include
everything…
“Visualizing digital collections at Archive-It”, JCDL 2012.
http://ws-dl.blogspot.com/2012/08/2012-08-10-ms-thesis-visualizing.html
16

17. Here’s everything in the collection
17

18. 1000s of Seeds X 1000s of archived copies ==
Conventional Viz Methods Not Applicable
18

19. Idea:
Storytelling
19

20. Stories in literature
Story elements: setting, characters, sequence, exposition, conflict,
climax, resolution
Once upon a time
http://www.learner.org/interactives/story/
20

21. Stories in social media
“It's hard to define a story, but I know it when I see it” (Alexander, 2008)
basically, just arranging web pages in time
21

22. “Storytelling” is becoming a popular
technique in social media
22

23. There is interest in choosing k
items from N where k << N
23

24. Summarizing the tweets, posts, and
uploads from you and others
•
24
1 minute video
Twitter stories

25. What are the limitations of
storytelling services?
25

26. The Egyptian Revolution on Storify
26

27. Bookmarking, not preserving!
27

28. Despite these limitations, how do we
combine storytelling & archives?
28

29. We sample k mementos from N pages of
the collection to create a summary story
S
1
S
2
S
3
S
4
S
2
S
1
S
3
Collection Y
S
3
S
2
S
1
Collection Z
Collection X
29

30. I hand-crafted these stories to summarize the
Egyptian Revolution collection for Yousof
https://storify.com/yasmina_anwar/the-egyptian-revolution-
on-archive-it-collection
https://storify.com/yasmina_anwar/the-story-of-the-egyptian-
revolution-from-archive- 30

31. How do we generate this automatically?
31

32. Research questions
• RQ1. How do people browse the past web?
• RQ2. Can we automatically generate stories that
convey different perspectives of the collection?
• RQ3. How do we build quantitative, descriptive
models of human-generated stories and collections in
Archive-It?
• RQ4. How to detect the off-topic web pages in the
archives?
• RQ5. How do we identify, evaluate, and select
candidate (archived) web pages to support the story?
Chapter 4
Chapter 5
Chapters 6,7
Chapter 8
Chapter 9
32

33. Background
Memento Terminology
33

34. URI-R is the page on live web
34

35. Memento (URI-M) is a snapshot of a URI-R
at a specific time
2002
2005
2009
2011 35

36. TimeMap is a list of mementos for a URI-R
36
<http://www.cnn.com>;rel="original", <http://web.archive.org/web/20000620180259/http://cnn.com/>;rel="memento";
datetime="Tue, 20 Jun 2000 14:02:59 GMT", <http://web.archive.org/web/20000621011731/http://cnn.com/>;rel="memento";
datetime="Tue, 20 Jun 2000 21:17:31 GMT", <http://web.archive.org/web/20000621140928/http://cnn.com/>;rel="memento";
datetime="Wed, 21 Jun 2000 10:09:28 GMT", <http://web.archive.org/web/20000706192838/http://cnn.com/>;rel="memento";
datetime="Thu, 06 Jul 2000 15:28:38 GMT", <http://web.archive.org/web/20000706211534/http://cnn.com/>;rel="memento";
datetime="Thu, 06 Jul 2000 17:15:34 GMT", <http://web.archive.org/web/20000707005406/http://cnn.com/>;rel="memento";
datetime="Thu, 06 Jul 2000 20:54:06 GMT", ,
<http://web.archive.org/web/20000711042840/http://cnn.com/?>;rel="memento"; datetime="Tue, 11 Jul 2000 00:28:40 GMT",
<http://web.archive.org/web/20000711063001/http://cnn.com/>;rel="memento"; datetime="Tue, 11 Jul 2000 02:30:01 GMT",
<http://web.archive.org/web/20000804165812/http://cnn.com/>;rel="memento"; datetime="Fri, 04 Aug 2000 12:58:12 GMT",
…

37. Related Work
• Collection understanding
• Encoded Archival
Description
• Francisco-Revilla 2014
• Chang 2004
• Visualizing Document
Collections
• Deal 2015
• Kramer-Smyth 2007
• Gorg 2007
• Image Collection
Summarization
• Chu 2008
• Sinha 2011
• Graham 2002
• Video Abstraction
• Zhang 1997
• Jungoh 2004
• Truong2007
• Telling Stories with Data
• Narrative Visualizations
• Tufte 1983
• Hullman
• Time Series Visualizations
• Tanahashi 2012
• Dou 2012
• Information Retrieval
Measures
• The notion of aboutness
• Jatowt 2004
• Klein 2011
• Topic Detection and
Tracking (TDT)
• Lavrenko 2002
• Mori 2006
• Similarity Measures
• Salton 1975
• Tan 2005
• Ester 1996
• TF-IDF
• Robertson 2004
• Performance Measures
• Croft 2009
• Manning 2008
• Web Usage Mining
• Kumar 2010
• Doran 2010
• Trends in Web Archiving
• The Usage of Web
Archives
• Costa 2011
• Carmel 2008
• Mining the Past Web
• Jatowt 2007
• Jatowt 2011
• Determining Datetime of
Web Pages
• SalahEldeen 2013
• Jatowt 2007
37
Further details : refer to Chapter 3

38. Research questions
• RQ1. How do people browse the past web?
• RQ2. Can we automatically generate stories that convey
different perspectives of the collection?
• RQ3. How do we build quantitative, descriptive models of
human-generated stories and collections in Archive-It?
• RQ4. How to detect the off-topic web pages in the archives?
• RQ5. How do we identify, evaluate, and select candidate
(archived) web pages to support the story?
38

39. How do people access web archives?
0.11.160.135 [02/Feb/2012:00:01:03] "GET
http://web.archive.org/web/20070519015308i
m_/http://www.jcdl.org/images/jcdl2007-
edie.jpg HTTP/1.1" 200 2137 "-"
"Mozilla/5.0"
0.11.160.135 [02/Feb/2012:00:01:03] "GET
http://staticweb.archive.org/images/toolba
r/wayback-toolbar-logo.png HTTP/1.1" 200
3700 "–" "Mozilla/5.0"
0.151.147.108 [02/Feb/2012:00:01:03] "GET
http://web.archive.org/web/20100102003557/
about:blank HTTP/1.1" 302 0 "www.xx.com"
"Mozilla/4.0"
"Access Patterns for Robots and Humans in Web Archives”, JCDL 2013.
39

40. People do not know about web archives
Robots outnumber
humans
10
1
64% of the time that humans visit the Internet Archive's Wayback
Machine, they visit a single page and then leave (Dip)
40
Based on the access logs, the Internet Archive's Wayback Machine receives more than
82 million requests per day

41. Who and what links to the Internet Archive
(Best Student Paper)
• Who links to the archive?
• How do people reach web archives?
• Why they link to the Archive?
• Why do people come to web archives?
• What they are looking for in terms of
the language of the browsed pages?
• Does the destination affect the number
of pages the users browse?
"Who and What Links to the Internet Archive", TPDL 2013, IJDL 2014.
41

42. Reaching web archives is not easy
82% of human sessions
connect to the Wayback
Machine via referrals from
the live Web
42

43. Web archives are difficult to use
43

44. Research questions
• RQ1. How do people browse the past web?
• RQ2. Can we automatically generate stories that convey
different perspectives of the collection?
• RQ3. How do we build quantitative, descriptive models of
human-generated stories and collections in Archive-It?
• RQ4. How to detect the off-topic web pages in the archives?
• RQ5. How do we identify, evaluate, and select candidate
(archived) web pages to support the story?
44

45. The archived collection has two dimensions
Time
URI
45

46. The two dimensions applied to stories
Fixed Page – Fixed Time:
differences in GeoIP,
mobile, etc.
Fixed Page – Sliding Time:
evolution of a single page
(or domain) through time
SlidingPage – Fixed Time:
different perspectives on a
point in time
Sliding Page – Sliding Time:
broadest possible coverage
of a collection
fixed
Time
sliding
URI
fixed
sliding
46

47. Fixed Page, Fixed Time
R1
R1
R1
R1
t1 t3t2 t5t4 t6
47

48. Fixed Page, Fixed Time
A desktop Chrome user-agent
http://www.cnn.com/2014/02/24/world/africa/egypt-
politics/index.html?hpt=wo_c2
Andriod Chrome user-agent
http://www.cnn.com/2014/02/24/world/africa/egypt-
politics/index.html?hpt=wo_c2
Schneider and McCown, “First Steps in Archiving the Mobile Web: Automated Discovery of Mobile Websites”, JCDL 2013.
Kelly et al. “A Method for Identifying Personalized Representations in Web Archives”, D-Lib Magazine 2013 .
48

49. Fixed Page, Sliding Time
R R R R R R
t1 t3t2 t5t4 t6
49

50. Feb 1 Feb 1 Feb 2
Feb 4 Feb 5 Feb 7
Feb 9 Feb 11 Feb 11
50

51. Sliding Page, Fixed Time
R1
R2
R3
R4
t1 t3t2 t5t4 t6
51

52. Feb. 11, 2011
Mubarak resigns
52

53. Sliding Page, Sliding Time
R1
R2
R1
R3
R4
R2
t1 t3t2 t5t4 t6
53

54. Jan 27 Jan 31
Feb 7Feb 4
Feb 11 Feb 11
Feb 2
Jan 25
Feb 10
54

55. The Dark and Stormy Archives (DSA) framework
Establish a
baseline
Reduce the candidate
pool of archived pages
Select good
representative
pages
Characteristicsof
human-generated
Stories
Characteristicsof
Archive-It
collections
Exclude duplicates
Exclude off-topic pages
Exclude non-English Language
Dynamically slice the collection
Cluster the pages
in each slice
Select high-quality
pages from each
cluster
Order pages
by time
Visualize
55
https://pbs.twimg.com/media/BQcpj7ACMAAHRp4.jpg

56. Research questions
• RQ1. How do people browse the past web?
• RQ2. Can we automatically generate stories that convey
different perspectives of the collection?
• RQ3. How do we build quantitative, descriptive models of
human-generated stories and collections in Archive-It?
• RQ4. How to detect the off-topic web pages in the archives?
• RQ5. How do we identify, evaluate, and select candidate
(archived) web pages to support the story?
56

57. The Dark and Stormy Archives (DSA) framework
Establish a
baseline
Reduce the candidate
pool of archived pages
Select good
representative
pages
Characteristicsof
human-generated
Stories
Characteristicsof
Archive-It
collections
Exclude duplicates
Exclude off-topic pages
Exclude non-English Language
Dynamically slice the collection
Cluster the pages
in each slice
Select high-quality
pages from each
cluster
Order pages
by time
Visualize
57

58. Establish a baseline of
social media stories
"Characteristics of Social Media Stories”, TPDL 2015, IJDL 2016.
58

59. What is the length of a story
(the number of resources per story)?
This story has
31 resources
1
3
2
59

60. What are the types of resources that
compose a story?
Quotes
Video
60
This story has
• 19 quotes
• 8 images
• 4 videos

61. What are the most frequently used domains?
Twitter.com
Twitter.com
Twitter.com
61
This story has
• 90% twitter.com
• 7% instagram.com
• 3% facebook.com

62. 92% of all resources included in stories
come from these 25 domains
62

63. What differentiates a popular story?
(popular = stories with the top 25% of views)
19,795 views 64 views
63

64. The distributions for the features of the stories
• Based on Kruskal-Wallis test, at the p ≤ 0.05 significance level, the popular and the
unpopular stories are different in terms of most of the features
• Popular stories tend to have:
• more web elements (medians of 28 vs. 21)
• longer timespan (5 hours vs. 2 hours) than the unpopular stories
64

65. Do popular stories have a lower decay rate?
The 75th percentile of decay rate per popular story is 10% of the resources,
while it is 15% in the unpopular stories
65

66. We found that 28 mementos is a good
number for the resources in the stories.
66

67. Establish a baseline of
current collections
"Characteristics of Social Media Stories. What makes a good story?", International Journal on Digital Libraries 2016.
67

68. The mean and median number of
URIs in a collection
68
This collection
has 435 URIs

69. The mean and median number of
mementos per URI This seed URI has
16 mementos
69

70. The most frequent used domains
abcnews.go.com
blogspot.com
This collection has
30% abcnews.com,
10% blogspot.com,
3% facebook.com
70

71. Archive-It top 25 is fundamentally
different than Storify top 25
71

72. Archive-It top 25 is fundamentally
different than Storify top 25
72
Twitter
is #10
not #1

73. What we archive and what we put in our
stories are different subsets of the web
73

74. Research questions
• RQ1. How do people browse the past web?
• RQ2. Can we automatically generate stories that convey
different perspectives of the collection?
• RQ3. How do we build quantitative, descriptive models of
human-generated stories and collections in Archive-It?
• RQ4. How to detect the off-topic web pages in the archives?
• RQ5. How do we identify, evaluate, and select candidate
(archived) Web pages to support the story?
74

75. Establish a
baseline
Reduce the candidate
pool of archived pages
Select good
representative
pages
Characteristicsof
human-generated
Stories
Characteristicsof
Archive-It
collections
Exclude duplicates
Exclude off-topic pages
Exclude non-English Language
Dynamically slice the collection
Cluster the pages
in each slice
Select high-quality
pages from each
cluster
Order pages
by time
Visualize
75
The Dark and Stormy Archives (DSA) framework

76. Detecting the off-topic pages
"Detecting Off-Topic Pages in Web Archives”, TPDL 2015, IJDL 2016.
76

77. Archive-It provides their partners with tools
that allow them to build themed collections
77

78. Archive-It tools are about HTTP
events/mechanics, not “content”
78

79. Over 60% of archived versions of
hamdeensabahy.com are off-topic
May 13, 2012: The page started as
on-topic.
May 24, 2012: Off-topic due to a
database error.
Mar. 21, 2013: Not working because of
financial problems.
May 21, 2013: On-topic again June 5, 2014: The site has been hacked Oct. 10, 2014: The domain has expired.
http://wayback.archive-it.org/2358/*/http://hamdeensabahy.com
79

80. How do we automatically detect
off-topic pages?
80

81. We investigated 6 similarity metrics
• Textual Content
• cosine similarity of TF-IDF
• intersection of the 20 most frequent terms
• Jaccard similarity coefficient
• Semantics
• Web-based kernel function using a search engine (SE)
• Structural
• the change in number of words
• the change in content length
81

82. Textual content
cosine similarity, intersection of the most frequent terms,
Jaccard similarity
Method Similarity
cosine 0.7
TF-Intersection 0.6
Jaccard 0.5
82

83. Textual content
cosine similarity, intersection of the most frequent terms,
Jaccard similarity
Method Similarity
cosine 0.7
TF-Intersection 0.6
Jaccard 0.5
Method Similarity
cosine 0.0
TF-Intersection 0.0
Jaccard 0.0
83

84. Semantics of the text
Web based kernel function using the search engine (SE)
84
Sahami and Heilman, A Web-based Kernel Function for Measuring the Similarity of Short Text Snippets, WWW 2006

85. Semantics of the text
Web based kernel function using the search engine (SE)
Method Similarity
SE-Kernel 0.7
85
Sahami and Heilman, A Web-based Kernel Function for Measuring the Similarity of Short Text Snippets, WWW 2006

86. Structural methods
no. of words, content-length
100 109
Method % change
WordCount 0.09
86

87. Structural methods
no. of words, content-length
100 109
100 5
Method % change
WordCount 0.09
Method % change
WordCount -0.95
87

88. We built a gold standard data set to
evaluate the methods
88

89. We manually labeled 15,760 mementos
Egypt Revolution and Politics
URI-Rs: 136
URI-Ms: 6,886
Off-topic URI-Ms: 384
Occupy Movement
URI-Rs: 255
URI-Ms: 6,570
Off-topic URI-Ms: 458
Columbia Univ. Human Rights collection
URI-Rs: 198
URI-Ms: 2,304
Off-topic URI-Ms: 94 89

90. Example of manually labeled set
90
id date URI
label
9 20120124014240 http://wayback.archive-it.org/2950/20120124014240/http://occupysarasota.com/ ontopic
9 20120131014118 http://wayback.archive-it.org/2950/20120131014118/http://occupysarasota.com/ ontopic
9 20120207014119 http://wayback.archive-it.org/2950/20120207014119/http://occupysarasota.com/ ontopic
9 20120501041141 http://wayback.archive-it.org/2950/20120501041141/http://occupysarasota.com/ offtopic
9 20120508032644 http://wayback.archive-it.org/2950/20120508032644/http://occupysarasota.com/ offtopic
9 20120515034720 http://wayback.archive-it.org/2950/20120515034720/http://occupysarasota.com/ offtopic
I would never want to
do this again

91. Evaluated 6 methods at 21 thresholds
• Assumed first memento was on-topic
• Combined two methods ('OR') to find best
combination method
• 15 combinations
• 6,615 tests (15 combinations x 21 thresholds x 21
thresholds)
• Averaged the results at each threshold over the three
collections
91

92. Cosine Similarity performed well
Similarity Measure Threshold FP FN FP+FN ACC F1 AUC
(Cosine,WordCount) (0.10,-0.85) 24 10 34 0.987 0.906 0.968
(Cosine,SEKernel) (0.10,0.00) 6 35 40 0.990 0.901 0.934
Cosine 0.15 31 22 53 0.983 0.881 0.961
(WordCount,SEKernel) (-0.80,0.00) 14 27 42 0.985 0.818 0.885
WordCount -0.85 6 44 50 0.982 0.806 0.870
SEKernel 0.05 64 83 147 0.965 0.683 0.865
Bytes -0.65 28 133 161 0.962 0.584 0.746
Jaccard 0.05 74 86 159 0.962 0.538 0.809
TF-Intersection 0.00 49 104 153 0.967 0.537 0.740
92

93. Applied best method to 18
Archive-It collections
• (Cosine,WordCount) with (0.10,-0.85) thresholds
• Collection Characteristics
• governmental, event-based, theme-based
• time spans of 1 week - 7 years
• 35 - 1459 URI-Rs
• 118 - 10,283 URI-Ms
93

94. Average precision of 0.89 on 18
Archive-It collections
94

95. Detecting duplicates in a TimeMap
95

96. 9 mementos for news.egypt.com,
but 5 are duplicates
96

97. Eliminate duplicates
Created: Jan. 28, 2011
Crawled: Aug. 3, 2011
Created: Jan. 28, 2011
Crawled: Jan. 2, 2012
97

98. The algorithm of eliminating (near-)duplicates
in an individual TimeMap
98

99. Research questions
• RQ1. How do people browse the past web?
• RQ2. Can we automatically generate stories that convey
different perspectives of the collection?
• RQ3. How do we build quantitative, descriptive models of
human-generated stories and collections in Archive-It?
• RQ4. How to detect the off-topic web pages in the archives?
• RQ5. How do we identify, evaluate, and select candidate
(archived) Web pages to support the story?
99

100. Establish a
baseline
Reduce the candidate
pool of archived pages
Select good
representative
pages
Characteristicsof
human-generated
Stories
Characteristicsof
Archive-It
collections
Exclude duplicates
Exclude off-topic pages
Exclude non-English Language
Dynamically slice the collection
Cluster the pages
in each slice
Select high-quality
pages from each
cluster
Order pages
by time
Visualize
100
The Dark and Stormy Archives (DSA) framework

101. How do we dynamically divide the
collections into appropriate slices?
101

102. We expected to see more like this…
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
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2012
020406080100120
Memento−Datetime
URIs
The Global Food Crisis collection at Archive-It
102