csvconfyasmin2017_05_03

csvconfyasmin2017_05_03
Using Web Archives to Enrich the Live Web Experience Through Storytelling Yasmin AlNoamany University of California, Berkeley Web Science & Digital Libraries Research Group at ODU @yasmina_anwar @WebSciDL Research Funded by IMLS LG-71-15-0077-15 csv,conf,v3 Portland, OR, 2017-05-03
My son, Yousof, was 2 on January 17, 2011 hQps://www.facebook.com/elshaheeed.co.uk/ 2
No worries! MulHple iniHaHves for documenHng the EgypHan RevoluHon 403 photos with informaVon about the lives of the martyrs 3,525 images and 2,387 videos posted by people for the January demonstraVons artwork produced during the EgypVan RevoluVon 3
Several studies and books about the EgypHan RevoluHon 4 4
These repositories do not exist any more! 5 5
Luckily these sites are archived at Archive-It in the EgypHan RevoluHon collecHon hQp://wayback.archive-it.org/2358/20110314134348/hQp://iamjan25.com/ hQp://wayback.archive-it.org/2358/20110211072306/hQp://1000memories.com/egypt/ hQps://wayback.archive-it.org/2358/20111128095924/hQp://iamtahrir.com/ 6
Archived collecHons are important for posterity, but there are problems with archived collecHons 7
AOer 10 years, Yousof knows about Archive-It > 3,500 collecVons ~340 insVtuVons > 10B archived pages Archive-It, a subscripVon-based service, hosts curated web collecVons 8
There is more than one collecHon about the EgypHan RevoluHon •  “2010-2011 Arab Spring” hQps://archive-it.org/collecVons/3101 •  “North Africa & the Middle East 2011-2013” hQps://archive-it.org/collecVons/2349 •  “Egypt RevoluVon and PoliVcs” hQps://archive-it.org/collecVons/2358 9
Current browsing and searching services for the “Egypt RevoluHon and PoliHcs” collecHon 10
CollecHon understanding and collecHon summarizaHon are not currently supported Not easy to answer “what’s in that collecHon?” or “how is this collecHon different from others”? 13
Our early aYempts at collecHon understanding tried to include everything… 14 “Visualizing digital collec5ons at Archive-It”, JCDL 2012. hQp://ws-dl.blogspot.com/2012/08/2012-08-10-ms-thesis-visualizing.html
1000s of seeds X 1000s of archived pages == Conven,onal Vis Methods Not Applicable 15
Idea: Storytelling 16
Stories in literature Story elements: sedng, characters, sequence, exposiVon, conflict, climax, resoluVon Once upon a Vme hQp://www.learner.org/interacVves/story/ 17
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 Vme 18
“Storytelling” is becoming a popular technique in social media 19
What are the limitaHons of storytelling services? 20
The EgypHan RevoluHon on Storify 21
Bookmarking, not preserving! 22
Use interface people already know how to use to summarize collecHons Archived collec5ons Storytelling services Archived enriched stories 23
Hand-craOed stories to summarize the EgypHan RevoluHon collecHon for Yousof hQps://storify.com/yasmina_anwar/the-egypVan-revoluVon- on-archive-it-collecVon hQps://storify.com/yasmina_anwar/the-story-of-the-egypVan- revoluVon-from-archive- 24
How do we generate this automaHcally? 25
CollecHons have two dimensions: {Fixed, Sliding} X {Page, Time} t1 t3 t2 t5 t4 tk … URI Time t6 26 … …
Fixed Page, Fixed Time A desktop Chrome user-agent hQp://www.cnn.com/2014/02/24/world/africa/egypt-poliVcs/ index.html?hpt=wo_c2 Android Chrome user-agent hQp://www.cnn.com/2014/02/24/world/africa/egypt- poliVcs/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 IdenVfying Personalized RepresentaVons in Web Archives”, D-Lib Magazine 2013 . 27
Feb 1 Feb 1 Feb 2 Feb 4 Feb 5 Feb 7 Feb 9 Feb 11 Feb 11 28 Fixed Page, Sliding Time
Feb. 11, 2011 Mubarak resigns 29 Sliding Page, Fixed Time
Jan 27 Jan 31 Feb 7 Feb 4 Feb 11 Feb 11 Feb 2 Jan 25 Feb 10 30 Sliding Page, Sliding Time
The Dark and Stormy Archives (DSA) framework Establish a baseline Reduce the candidate pool of archived pages Select good representative pages Characteristics of human-generated Stories Characteristics of 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 31 hQps://pbs.twimg.com/media/BQcpj7ACMAAHRp4.jpg
Establish a baseline of social media stories "Characteris5cs of Social Media Stories”, TPDL 2015, IJDL 2016. 32
What is the length of a story (the number of resources per story)? This story has 31 resources 1 3 2 33
What are the types of resources that compose a story? Quotes Video 34 This story has •  19 quotes •  8 images •  4 videos
We found that 28 mementos is a good number for the resources in the stories. 35
DetecHng off-topic pages "Detec5ng Off-Topic Pages in Web Archives”, TPDL 2015, IJDL 2016. 36
More than 60% of archive copies 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. hQp://wayback.archive-it.org/2358/*/hQp://hamdeensabahy.com 37
Based on evaluaHng 6 similarity methods, we applied the best performing method to automaHcally detect off-topic pages 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. hQp://wayback.archive-it.org/2358/*/hQp://hamdeensabahy.com 38
9 mementos for news.egypt.com, but 5 are duplicates 39
SelecHng representaHve pages for generaHng stories 40 ”Genera5ng Stories from Archived Collec5ons”, WebSci 2017.
Quality metrics for selecHng mementos •  In the DSA, memento quality Mq is calculated as following: Mq = (1 − wm*Dm) + wql*Sql + wqc*Sqc •  Dm is the memento damage (Brunelle, JCDL 2014) •  Sql is the snippet quality based on the URI level •  Sqc is the snippet quality based on URI category •  wm, wql, wqc are the weights of memento damage, level, and category 41
We prefer a higher quality memento (Dm) hQp://wayback.archive-it.org/2358/20110201231457/ hQp://news.blogs.cnn.com/category/world/egypt-world-latest-news/ hQp://wayback.archive-it.org/2358/20110201231622/ hQp://www.bbc.co.uk/news/world/middle_east/ 42 Brunelle et al. Not All Mementos Are Created Equal: Measuring The Impact Of Missing Resources, JCDL 2014
We prefer pages with aYracHve snippets hQps://wayback.archive-it.org/2358/20110207193404/hQp://news.blogs.cnn.com/2011/02/07/egypt-crisis-country- to-aucVon-treasury-bills/ hQps://wayback.archive-it.org/2358/20110207194425/hQp://www.cnn.com/2011/WORLD/africa/02/07/ egypt.google.execuVve/index.html?hpt=T1 43
Visualizing stories in Storify 44 ”Genera5ng Stories from Archived Collec5ons”, WebSci 2017.
We extract the metadata of the pages and order them chronologically { "elements":[ { "permalink":"http://wayback.archive-it.org/694/20070523182134/http://www.usatoday.com/news/nation/2007-04-16- virginia-tech_N.htm", "type":"link", "source":{"href":"http://www.usatoday.com", "name":"www.usatoday.com @ 23, May 2007"} }, { "permalink":"http://wayback.archive-it.org/694/20070530182159/http://www.time.com/time/specials/2007/ vatech_victims", "type":"link", "source":{"href":"http://www.time.com", "name":"www.time.com @ 30, May 2007" } }, { "permalink":"http://wayback.archive-it.org/694/20070530182206/http://www.collegiatetimes.com/", "type":"link", "source":{"href":"http://www.collegiatetimes.com", "name":"www.collegiatetimes.com @ 30, May 2007" } }, { "permalink":"http://wayback.archive-it.org/694/20070606234248/http://hokies416.wordpress.com/", "type":"link", "source":{ "href":"http://hokies416.wordpress.com", "name":"hokies416.wordpress.com @ 06, Jun 2007" } }, … { "permalink":"http://wayback.archive-it.org/694/20070620234329/http://www.hokiesports.com/april16/", "type":"link", "source":{"href":"http://www.hokiesports.com", "name":"www.hokiesports.com @ 20, Jun 2007" } }, ], "description":"This is an automatically generated story from Archive-It collection.", "title":"April 16 Archive ” } 45 Using the Storify API, we override the default metadata to generate more attractive snippets
Example of an automaHcally generated story 46 Notice the good metadata: images, titles with dates, favicons
EvaluaHng the Dark and Stormy Archive framework (how good are the automaHcally generated stories?) 47
EvaluaHon is tricky! (two perfectly good stories could have non-overlapping k=28 elements!) •  Successful evaluaVon means: •  Human and DSA stories are indisVnguishable •  Human and DSA stories are beQer than Random •  We use human evaluators (via Amazon's Mechanical Turk) to compare: •  Human-generated stories •  DSA (automaVcally) generated stories •  Randomly generated stories 48
Our guidelines for expert archivists at Archive-It for generaHng stories from the collecHons 49
A sample HIT 50
DSA == Human (Human,DSA) > Random Automatic/Human Automatic/Random Human/Random Percentage 020406080100 Automatic Human Random 51
Understanding the archived collecHons is important for posterity 52
Understanding the archived collecHons is important for posterity Thanks mom for the DSA! 53
Use interface people already know how to use to summarize collecHons Archived collec5ons Storytelling services Archived enriched stories 54 All the code, datasets, papers, slides, etc.: hQp://bit.ly/YasminPhD @yasmina_anwar
Backup Slides 55
We sample k mementos from N pages of the collecHon (k << N) to create a summary story Collec5on Y Collec5on Z Collec5on X 56
How do we automaHcally detect off-topic pages? 57
Textual content cosine similarity, intersecHon of the most frequent terms, Jaccard similarity Method Similarity cosine 0.7 TF-IntersecVon 0.6 Jaccard 0.5 Method Similarity cosine 0.0 TF-IntersecVon 0.0 Jaccard 0.0 58
SemanHcs of the text Web based kernel funcHon using the search engine (SE) Method Similarity SE-Kernel 0.7 59 Sahami and Heilman, A Web-based Kernel FuncVon for Measuring the Similarity of Short Text Snippets, WWW 2006
Structural methods no. of words, content-length 100 109 100 5 Method % change WordCount 0.09 Method % change WordCount -0.95 60
We built a gold standard data set to evaluate the methods 61
We manually labeled 15,760 mementos Egypt Revolu5on and Poli5cs 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 collec5on URI-Rs: 198 URI-Ms: 2,304 Off-topic URI-Ms: 94 62
Evaluated 6 methods on manually labeled 15,760 •  Textual content •  cosine similarity •  intersecVon of the most frequent terms •  Jaccard similarity •  SemanVcs of the text •  Web based kernel funcVon using the search engine (SE) •  Structural methods •  no. of words •  content-length 63 "Detec5ng Off-Topic Pages in Web Archives”, TPDL 2015, IJDL 2016.
Cosine Similarity performed well 64 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-IntersecVon 0.00 49 104 153 0.967 0.537 0.740
Average precision of 0.89 on 18 different Archive-It collecHons 65 (Cosine,WordCount) with (0.10,-0.85) thresholds
EvaluaHng the DSA 66
MT experiment setup •  Three HITs for each story (69 HITs to evaluate 23 stories); two comparisons per HIT: •  HIT1: human vs. automaVc, human vs. poor •  HIT2: human vs. random, human vs. poor •  HIT3: random vs. automaVc, automaVc vs. poor •  15 disVnct turkers with master qualificaVon (i.e., high acceptance rate) for each HIT •  We rejected the submissions contained poorly-generated stories and the HITs that were completed in less than 10 seconds (mean Vme per HIT = 7 minutes) •  989 out of 1,035 (69*15) valid HITs •  We awarded the turker $0.50 per HIT 67 hQps://www.mturk.com/mturk/help?helpPage=worker#what_is_master_worker
We prefer deep links over high level domains (Sql) Feb. 11, 2011: the homepage of BBC on Storify Feb. 11, 2011: the homepage of BBC Middle East secVon on Storify Feb. 11, 2011: the arVcle of BBC on Storify hQps://wayback.archive-it.org/2358/20110211191429/hQp://www.bbc.co.uk/ hQps://wayback.archive-it.org/2358/20110211192204/hQp://www.bbc.co.uk/news/world-middle-east-12433045 hQps://wayback.archive-it.org/2358/20110211191942/hQp://www.bbc.co.uk/news/world/middle_east/ 68
Social media pages may not produce good snippets (Sqc) hQp://wayback.archive-it.org/1784/20100131023240/hQp:/twiQer.com/HaiVfeed/ hQp://wayback.archive-it.org/2358/20141225080305/hQps:/www.facebook.com/elshaheeed.co.uk 69
How do we dynamically divide the collecHons into appropriate slices? (in other words, how do we pick just 28?) 70 ”Genera5ng Stories from Archived Collec5ons”, WebSci 2017.
We expected most collecHons to look like this… ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ●●● ●●● ●●● ●●● ●●● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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