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
<[email protected]> / @rtroncy 1 EURECOM, France 2 Google & University Politècnica de Catalunya, Spain 3 IBBT Ghent, Belgium
Conferences and natural disaster
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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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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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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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Deep link Permalink
Clean text for NLP processing
Aggregate view of ALL social interactions
12 Social Networks
Media Finder
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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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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
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MediaFinder Teleportd Twitter Google i/o 108 (49%) 20 (9%) 96 (44%) io12 69 (37%) 20 (10%) 98 (53%)
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
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google i/o io12 relevance Q1 Q2 relevance Q1 Q2
Media Finder
0,28 2,35 2,72 0,21 2,05 2,24
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
Demo: Grid view
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Demo: Timeline view
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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
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http://mediafinder.eurecom.fr/
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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http://www.slideshare.net/troncy
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