Crowdsourcing & Citizen Science

FROM CROWD KNOWLEDGE TO MACHINE KNOWLEDGE use cases with semantics & user interaction in cultural heritage collections Lora AroyoVU University Amsterdam @laroyo

OBSERVATION I: VIDEO ANNOTATIONS Video: rich in meaning expressed with objects,people, events & symbolsAnnotation: tedious, time- consuming, incompleteProfessional annotations:coarse-grained & refer to entire video and topics

OBSERVATION II: SEARCH BEHAVIOR People predominantlyrequest video fragments:broadcast (33%), stories (17%), fragments (49%) Finding fragments takes much longer than ﬁnding broadcasts:stories (2x), fragment (3x)

OBSERVATION III: VOCABULARY MISMATCHexperts use a speciﬁc vocabulary that is unknown to general audiences35% of clicked resultsare not found by title or thesaurus term

OBSERVATION IV: LIMITED ANNOTATION RESOURCESthe professionals canno longer cope with the demand formanual annotation on the ever growingmultimedia content in their collections

SO WE WANT TO ...Make a massive set of videos accessible to end users Improve video search for end users Maintain a growing community of engaged users Support professional annotators

TO IMPROVE VIDEO SEARCH: fragment retrieval within video navigation

REQUIRES CHANGES IN ANNOTATIONS: Including time-based annotationsBridging the vocabulary gap of searcher &. cataloguer

exploit crowdsourcing on the webintegrate in content management system

CULTURAL HERITAGE WITH INVOLVED CONSUMERS & PROVIDERSdata: open, shared & accessibleinfrastructures: interoperablesmart services: relevant contentin right context

Rijksmuseum Amsterdamhttp://chip-project.orghttp://e-culture.multimedian.nl/pk/annotate?uri=RP-P-OB-77.320

Netherlands Institute for Sound & Visionhttp://openimages.euhttp://academia.nlMUNCH projectCHOICE project

Europeana Cultural Searchhttp://e-culture.multimedian.nlhttp://europeana.eu/portal/thoughtlab.htmlhttp://europeana.eu

WAYS TO USE THE CROWD Tagging & classiﬁcation Editing & transcribing Contextualising Acquisition Co-curation Crowdfunding

WHAT WE DID: THE WORKFLOWcollect-analyse-manage-integrate user-generated metadata

domain expertsgamers collection managers

Winner EuroITV CompetitionBest Archives on the Web Award COLLECTUser Generated Metadata

TWO PILOTS Waisda? 1.0 (8 months)focus group with lay users for motivation aspects cognitive walk through with professionals usability testing with lay users and professionals tag analysis - primarily WHAT & WHOWaisda? 2.0 (starting this September) currently running initial user & tag analysis

Waisda? 1.0(8 months)Participation 44,000 pageviews2,000 different players500 registered players thousands anonymous playersTime-based annotations 612 videos tagged 420,000 tags added 46,000 unique tagsVocabulary gapUser tags added the user perspective on the collection Community consensus as a measure of ‘validity’ Players score points when tag exactly matches a tag entered by another within 10 secs

Waisda? 2.0(4 weeksnot public)Participation ~1500 users 81 registered 1435 anonymoususers 2344 gamesTime-based annotations 11,109 videos in the game 32,200 tags --> 25,600 ﬁrst time tags 322 were played with 19% are one time tags (81% appear more than once) 12,000 validated tags (36%) / (4000 of them ﬁrst time tag) 1,900 match in vocabs - 257 GTAA people/83 validated; 1,661 GTAA geo/666 validated 9,796 validated, but no match in GTAA

MANAGEAnalyze Waisda? Collected Tags; Quality Metrics

WHAT ARE THE RELATIONSBETWEEN TAGS & PROFESSIONAL ANNOTATIONS? in terms of the vocabulary used? in terms of the topics they describe? Initially: manual analysis Output goal: to be expressed in a quality metric

WAISDA? TAG ANALYSIS Study 1Quantitative analysis of all 46,762 Waisda? tags Tag coverage w.r.t different vocabularies Study 2 Qualitative analysis of selected video fragments Manual classiﬁcation of 1,343 “veriﬁed” tags

DURING FIRST 9 MONTHS 400000 300000 > 600 videos 13 TV seriesNumber of tags 200000 100000 > 420,000 tags collected 0 46,792 unique tags 1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 500 Day each top 5 most-tagged video has > 23,000 tags 375 Number of arrivals 250 avg. tag density > 8 tags/sec 125 0 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 Day

1 MAPPING TAGS TO VOCABULARIES Vocabulary of S&V GTAA 160 000 terms in six Thesaurus disjoint facets: keyword, to ap pe d location, maker, genre, m person, named entities mapped Dutch lexical database m ap pe d to collective vocabulary of internet users

RESULTSZero-hits (4404) 11% Google (41388) 89% Corneto (29815) (8723) 63% 18% (2216) 5% GTAA (1634) 3%

RESULTSZero-hits (4404) 11% Facet Tags Subject 1199 Google (41388) Location 613 89% Genre 52 Corneto (8723) (29815) Person 118 63% 18% (2216) Maker 4 5%GTAA GTAA Name 673 (3850) (1634) 8%3%

RESULTSZero-hits (4404) 11% Types Tags Noun 7222 Google (41388) 89% Verb 2090 Corneto Adjective 1693 Corneto (8723) (29815) (10939) 63% 18% 23%(2216) Adverb 171 5% GTAA (1634) 3%

RESULTS Zero-hitsZero-hits (4404) (4404) 11% 11% Zero-hit tags Google • garbled text (41388) 89% • seriously mistyped words Corneto • grammatically incorrect (29815) (8723) 18% 63% sentences (2216) 5% GTAA (1634) 3%

RESULTSZero-hits (4404) 11% Google “Meaningful” Tags • multi-word tags Google (41388) • slang 89% • names Corneto Pos-hits (8723) (29815) (29815) 63% • morphological variations 18% 63% (2216) • minor typos 5% GTAA • etc… (1634) 3%

2 TAG CLASSIFICATION What video aspects are described by user tags? Manual classification of tag sample 5 videosnon-visual (0), perceptual (11), the rest conceptual Object (most of the tags) vs. Scene tags (only 30) 3 levels and 4 facets: abstract, general & specific who, what, when, where Tag sample: all verified tags from random fragments (182 non-descriptive tags removed) #1,343

2 RESULTS Only 30 scene-level tags Panofsky-Shatford matrix for object-level tags Abstract General Speciﬁc Total Who 10 166 177 31% What 73 5 563 12 57% Where 0 68 8 7% When 4 31 6 5% Total 7% 74% 9% 195 tags (typically adverbs & adjectives) couldn’t be classiﬁed in any of the facets[1] Classification of user image descriptions. L. Hollink, G. Schreiber, B. Wielinga & M. Worring

2 RESULTS Only 30 scene-level tags Panofsky-Shatford matrix for object-level tags Abstract General Speciﬁc Total Who 10 166 177 31% What 73 5 563 12 57% Where 0 68 8 7% When 4 31 6 5% Total 7% 74% 74% 9% 195 tags (typically adverbs & adjectives) couldn’t be classiﬁed in any of the facets[1] Classification of user image descriptions. L. Hollink, G. Schreiber, B. Wielinga & M. Worring

2 RESULTS Only 30 scene-level tags Panofsky-Shatford matrix for object-level tags Abstract General Speciﬁc Total Who 10 166 177 31% What 73 5 563 12 57% Where 0 68 8 7% When 4 31 6 5% Total 7% 74% 9% 195 tags (typically adverbs & adjectives) couldn’t be classiﬁed in any of the facets[1] Classification of user image descriptions. L. Hollink, G. Schreiber, B. Wielinga & M. Worring

2 RESULTS Only 30 scene-level tags Panofsky-Shatford matrix for object-level tags Abstract General Speciﬁc Total Who 10 166 177 31% 31% What 73 5 563 12 57% 57% Where 0 68 8 7% When 4 31 6 5% Total 7% 74% 9% 195 tags (typically adverbs & adjectives) couldn’t be classiﬁed in any of the facets[1] Classification of user image descriptions. L. Hollink, G. Schreiber, B. Wielinga & M. Worring

EXPERIMENTAL SET5 videos Episodes All tags Veriﬁed Genrewell tagged Reality show 25,965 5837 amusementwell tagged Reality show 22,792 6153 amusement medium Missing people 1007 274 informativelow tagged Reporter 403 73 informativelow tagged The Walk 257 45 religious

genre might inﬂuence the usefulness of the user tagsusers focus on what can be seen or heard (directly perceivable objects) because of the fast pace of the game USEFULNESS according to professionals

SUBTITLES USEFUL? on the average 26% of all the tags (35% of the veriﬁed) are matched in subtitles used to run bot-based gamesotherwise, don’t really bring much new to the annotation

OBSERVATIONS FROM ANALYSIS:taggers & professionals use differentvocabularies user tags complement professional annotations user tags could bridge the searcher vs. cataloger gaptaggers tend to use general concepts moretaggers focus mainly on the What and Whotaggers describe predominantly objects & rarelyscenes

OBSERVATIONS FROM ANALYSIS:need for validation for quality & correctnesscrowd-sourcing users need continuous support& motivation to supply more and bettercontributions

THE QUESTION IS:Can user tags be used for fragment search?How to derive user tag quality metrics?

THE QUALITY OF TAGS?

MANAGEModerate Waisda? Analyzed Tags

Tag gardening annotations for video, fragments & scenes adding types & roles to user tagsmapping tags to concepts, disambiguation spelling correctionssingle & batch tags/video editing

sorted byfrequency all frames per tag

Inspired by Google Reﬁne

INITIAL (SMALL) EXPERIMENT•1 video, 36 tags, 4 validators• validatorsselects the most appropriate concept for each tag (if present)• frames within the video available - linked to the tags• reconciliation agains GTAA, Cornetto, Freebase• (1) select a source, (2) start reconciliation (3) choose from suggested concepts

krippendorffcoverage 33 out of 36 tags reliability coefﬁcientNumber of tags reconciled by the four participants more than 50% of concepts Ranks of the selected tags

Lessons• Cornetto is good for subject terms • Selecting most suitable concept is time consuming as the differences sometimes are difﬁcult to understand• For all NE users could select a concept from Freebase & GTAA • Vocabularies like GTAA have less additional information, thus it’s more difﬁcult to select the right concept • Freebase works well for English (Dutch might be a problem)• Full coverage - prevented by spelling errors and variations• Users adapt quickly, e.g. Freebase is good for people & locations

Recommendations• reconcile agains multiple data sources at the same time for best coverage • merge duplicates into a single suggestion • organize results in categories, e.g. people, locations and subjects (to avoid suggestions from the wrong type)• pre-processing to merge similar concepts (deal with spelling errors and variations)• rank the suggested concepts by precision, then by recall (as alternative)

INTEGRATEthe improved (gardened) user annotations

FRAGMENT RETRIEVAL

WITHIN VIDEO NAVIGATION

WITHIN VIDEO NAVIGATIONgroup by type (faceted)

WITHIN VIDEO NAVIGATION concepts uniquely identiﬁed

WITHIN VIDEO NAVIGATION background info

THE TEAM Riste Gligorov @mbrinkerink @GuusSchreiber Marteen BrinkerinkGuus Schreiber @johanoomen Johan Oomen @laroyo Lora Aroyo @McHildebrand @jrvosse Michiel Hildebrand @lottebeliceJacco van Ossenbruggen Lotte Belice Baltussen

LINKS http://waisda.nlhttp://www.prestoprime.org/ @waisdahttp://www.cs.vu.nl/~laroyohttp://slideshare.com/laroyo

http://paper.li	55
http://a0.twimg.com	6
http://www.onlydoo.com	2
http://ceipcandidonogales.es	1
http://us-w1.rockmelt.com	1

Crowdsourcing & Citizen Science

by Lora Aroyo on Oct 13, 2011

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Crowdsourcing & Citizen Science — Presentation Transcript