1. What Fresh Media Are You Looking
For? Extracting Media Items from
Multiple Social Networks
Giuseppe Rizzo1, Thomas Steiner2, Raphaël Troncy1,
Ruben Verborgh3, José Luis Redondo Garcia1
and Rik Van de Walle3
<raphael.troncy@eurecom.fr> / @rtroncy
1 EURECOM, France
2 Google & University Politècnica de Catalunya, Spain
3 IBBT Ghent, Belgium

2. Conferences and natural disaster
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7. Some definitions
 Media Item: a photo or a video that is shared on a social
network
 Micropost: a text status message that can optionally
accompany a media item
 Social Network: an online service that focuses on
building and reflecting social relationships among
people sharing interests or activities
 Media Sharing Platforms: emphasis on sharing media but blurred
boundaries with social networks since users are encouraged to react
on media content (like, comment, favorite, etc.)
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8. Social networks and media items
 First-order support:
 Posting requires the inclusion of a media item
 Example: Flickr, YouTube
 Second-order support:
 Possibility to post media items but also text-only messages
 Example: Facebook
 Third-order support:
 No direct support for media items but rely on third party applications
to host them
 Example: Twitter before the introduction of native photo support
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9. Media Collector (Server)
 Composition of media item extractors (12 SNs)
 Rely on search APIs + a fix 30s timeout window to provide results
 Fallback on screen scraping when necessary (Twitter ecosystem)
 Implemented as a NodeJS server
 Serialize results in a common schema (JSON)
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10. Deep link
Permalink
Clean text for NLP
processing
Aggregate view of ALL
social interactions
12 Social Networks
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11. Media Finder
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12. Evaluation (1/3)
 9 events occurring between 10 and 19 January 2012
 Assad speech, CES Las Vegas, Costa Concordia Disaster, Cut the
Rope Launch, Dixville Notch, Free mobile launch, Blackout SOPA,
Ubuntu TV launch, Christian Wulff case
 448 images + 143 videos
 Photo-Sweeper CBIR-based image duplication detection software
 Dataset heterogeneity:
 Leaderboard banner (728x90) to a standard 3.1 mega pixels
(2048x1536) cell phone photo … no quadratic bitmaps shrinking
 Hard problem!
 Best settings for each event, no generic configuration, in order to
limit the number of duplicate misses and false positives
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13. Evaluation (2/3)
 User study to compare the relevance and
illustrativeness of the media galleries
 One event: Google IO (“google i/o” + “io12”)
http://en.wikipedia.org/wiki/Google_io
 Three systems:
 Media Finder, Twitter Gallery, Teleportd
 7 participants (6 male, 1 female) in 2 groups
MediaFinder Teleportd Twitter
Google i/o 108 (49%) 20 (9%) 96 (44%)
io12 69 (37%) 20 (10%) 98 (53%)
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14. Evaluation (3/3)
 Q1: How illustrative this gallery is for this event?
 Q2: How visually diverse this gallery is for this event?
 Lickert 7-scale: result http://goo.gl/QzSM6 + http://goo.gl/7ov6Q
google i/o io12
relevance Q1 Q2 relevance Q1 Q2
Media 0,28 2,35 2,72 0,21 2,05 2,24
Finder
Teleportd 0,05 0,30 0,37 0,04 0,35 0,59
Twitter 0,28 2,64 2,64 0,34 3,44 2,91
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15. Demo: Grid view
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16. Demo: Timeline view
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17. Conclusion
 Fresh media available on social networks
 Ignored by general search engines …
 … but ideal for building stories of events of our life
 Media Server: a NodeJS server collecting media
items shared on social networks
 Media Finder: a client-server architecture that
generates views of those media items
http://mediafinder.eurecom.fr/
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18. Future Work
 Image de-duplication:
 Simple off-the-shelf tools using color, texture and shape
(Ramaiah and Mohan, IEEE RAICS’11)
 Named Entity Recognition:
 NERD: http://nerd.eurecom.fr/
 Clustering and Storyfying:
 Source and Temporal clustering
 Visual clustering
 Semantic clustering:
using named entities extracted in microposts
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19. http://www.slideshare.net/troncy
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