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Social Data and Multimedia Analytics for News and Events Applications
Dr. Yiannis Kompatsiaris, [email protected], Knowledge and Social Media Analytics Lab, HeadCERTH-ITI
Multimodal Social Data Management (MSDM) Workshop
MSDM 2014, Athens Social Data and Multimedia Analytics #2
Overview
• Introduction– Motivation – Challenges
• SocialSensor Project and Use Cases• Research Approaches
– Large-Scale visual search– Clustering– Verification
• Demos – Applications– MM News Demo– Clusttour– Thessfest
• Conclusions
MSDM 2014, Athens Social Data and Multimedia Analytics #3
IntroductionMotivationExample ApplicationsConceptual ArchitectureChallenges
MSDM 2014, Athens Social Data and Multimedia Analytics
http://www.puzzlemarketer.com/digital-social-brands-in-60-seconds/ (Apr, 2012)
MSDM 2014, Athens Social Data and Multimedia Analytics
Social Networks as Real-Life Sensors• Social Networks is a data source with an
extremely dynamic nature that reflects events and the evolution of community focus (user’s interests)
• Huge smartphones and mobile devices penetration provides real-time and location-based user feedback
• Transform individually rare but collectively frequent media to meaningful topics, events, points of interest, emotional states and social connections
• Present in an efficient way for a variety of applications (news, marketing, entertainment)
MSDM 2014, Athens Social Data and Multimedia Analytics #6
Pope Francis
Pope Benedict
2007: iPhone release
2008: Android release
2010: iPad release
http://petapixel.com/2013/03/14/a-starry-sea-of-cameras-at-the-unveiling-of-pope-francis/
MSDM 2014, Athens Social Data and Multimedia Analytics
Social Networks as Graphs
MSDM 2014, Athens Social Data and Multimedia Analytics #8
Social Networks as Graphs
“Social networks have emergent properties. Emergent properties are new attributes of a whole that arise from the interaction and interconnection of the parts”
•Emotions, Health, Sexual relationships do not depend just on our connections (e.g. number of them) but on our position - structure in the social graph
– Central – Hub– Outlier– Transitivity (connections between
friends)
MSDM 2014, Athens Social Data and Multimedia Analytics
Examples - Science
Xin Jin, Andrew Gallagher, Liangliang Cao, Jiebo Luo, and Jiawei Han. The wisdom of social multimedia: using flickr for prediction and forecast, International conference on Multimedia (MM '10). ACM.
9
“…if you're more than 100 km away from the epicenter [of an earthquake] you can read about the quake on twitter before it hits you…”
MSDM 2014, Athens Social Data and Multimedia Analytics
Example – News (Boston bombing)
#10
“Following the Boston Marathon bombings, one quarter of Americans reportedly looked to Facebook, Twitter and other social networking sites for information, according to The Pew Research Center. When the Boston Police Department posted its final “CAPTURED!!!” tweet of the manhunt, more than 140,000 people retweeted it.”
“Authorities have recognized that one the first places people go in events like this is to social media, to see what the crowd is saying about what to do next”
"I have been following my friend's Facebook [account] who is near the scene and she is updating everyone before it even gets to the news”
MSDM 2014, Athens Social Data and Multimedia Analytics
Events - Festivals
#11http://www.eventmanagerblog.com/uploads/2012/12/event-technology-infographic.jpg
MSDM 2014, Athens Social Data and Multimedia Analytics
API Wrapper
Website Wrapper
Scheduler
CRAWLING
Visual Indexing
Near-duplicates
Text Indexing
INDEXING
Media Fetcher
SNA
Sentiment - Influence
Trends - Topics
MINING
Model Building
Concepts
Relevance
Diversity
Popularity
RANKING
Veracity
Crawling Specs
Sources
Interaction
Responsiveness
Aggregation
VISUALIZATION
Aesthetics
Conceptual Architecture
MSDM 2014, Athens Social Data and Multimedia Analytics
Challenges – Content (Mining)
• Multi-modality: e.g. image + tags
• Rich social context: spatio-temporal, social connections, relations and social graph
• Inconsistent quality: noise, spam, ambiguity, fake, propaganda
• Huge volume: Massively produced and disseminated
• Multi-source: may be generated by different applications and user communities
• Also connected to other sources (e.g. LOD, web)
• Dynamic: Fast updates, real-time
MSDM 2014, Athens Social Data and Multimedia Analytics
Policy – Licensing – Legal challenges
• Fragmented access to data– Separate wrappers/APIs for each source (Twitter, Facebook, etc.)– Different data collection/crawling policies
• Limitations imposed by API providers (“Walled Gardens”)• Full access to data impossible or extremely expensive (e.g. see data
licensing plans for GNIP and DataSift• Non-transparent data access practices (e.g. access is provided to an
organization/person if they have a contact in Twitter) • Constant change of model and ToS of social APIs
– No backwards compatibility, additional development costs• Ephemeral nature of content
• Social search results often lead to removed content inconsistent and unreliable referencing
• User Privacy & Purpose of use• Fuzzy regulatory framework regarding mining user-contributed data
MSDM 2014, Athens Social Data and Multimedia Analytics #15
Social Sensor ProjectUse Cases
MSDM 2014, Athens Social Data and Multimedia Analytics
SocialSensor Project Objective
SocialSensor quickly surfaces trusted and relevant material from social media – with context.
DySCODySCO
behaviour
location
timecontent
usage
social context
Massive social mediaand unstructured web
Social media miningAggregation & indexing
News - InfotainmentPersonalised access
Ad-hoc P2P networks
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The SocialSensor Vision
SocialSensor quickly surfaces trusted and relevant material from social media – with context.
•“quickly”: in real time•“surfaces”: automatically discovers, clusters and searches •“trusted”: automatic support in verification process•“relevant”: to the users, personalized•“material”: any material (text, image, audio, video = multimedia), aggregated with other sources (e.g. web)•“social media”: across all relevant social media platforms•“with context”: location, time, sentiment, influence
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Conceptual Architecture and Main components
SEMANTIC MIDDLEWARE
Public Data
In-project Data
SEARCH & RECOMMENDATION
USER MODELLING & PRESENTATION
INDEXINGMINING
STORAGE
DATA COLLECTION / CRAWLING
• Real time dynamic topic and event clustering
• Trend, popularity and sentiment analysis
• Calculate trust/influence scores around people
• Personalized search, access & presentation based on social network interactions
• Semantic enrichment and discovery of services
MSDM 2014, Athens Social Data and Multimedia Analytics
Use Cases
Casual News application
Casual News Readers
Professional News application
Journalists, Editors, etc.
NEWS
EventLiveDashboard
Festival organizers
INFOTAINMENT
Social Media Walls
Festival attendants
MSDM 2014, Athens Social Data and Multimedia Analytics #20
“It has changed the way we do news”(MSN)
“Social media is the key place for emerging stories – internationally, nationally, locally” (BBC)
“Social media is transforming the way we do journalism”(New York Times)
Source: picture alliance / dpa
MSDM 2014, Athens Social Data and Multimedia Analytics #21
Source: Getty Images
“It’s really hard to find the nuggets of useful stuff in an ocean of content” (BBC)
“Things that aren’t relevant crowd out the content you are looking for” (MSN)
“The filters aren’t configurable enough” (CNN)
MSDM 2014, Athens Social Data and Multimedia Analytics
Verification was simpler in the past...
Source: Frank Grätz
#22
MSDM 2014, Athens Social Data and Multimedia Analytics #23
Infotainment• Events with large numbers
of visitors• Thessaloniki International
Film Festival – 80,000 viewers / 100,000
visitors in 10 days– 150 films, 350 screenings
• Discovery and presentation of relevant aggregated social media– Trending Topics– Sentiment– Tweet – film matching– Visualization (Social Walls)
MSDM 2014, Athens Social Data and Multimedia Analytics #24
Research ApproachesLarge-Scale Visual SearchClustering – Community DetectionSocial Media Verification
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Scalable visual feature aggregation & indexing• Problem: Example-based image search
– Find images that represent same or similar object or scene with a given query image
– Viewed from different viewpoints, occlusions, clutter
• Challenge: Large-scale– Searching databases with tens of millions of images– Objectives to be full-filed:
• Sufficient discriminative power• Fast response times• Efficient memory usage
MSDM 2014, Athens Social Data and Multimedia Analytics #26
Large-scale visual search
image collection from social media/
Web
image local feature extraction
feature aggregation
feature indexingkNN visual similarity search
concept-based image annotation
image clustering
image (geo)tagging
concept-based search/filtering
duplicate detection
MSDM 2014, Athens Social Data and Multimedia Analytics #27
Framework
• Implementation and evaluation of the effectiveness of VLAD in combination with SURF
• Scalable image indexing
E. Spyromitros-Xioufis, et al. An Empirical Study on the Combination of SURF Features with VLAD Vectors for Image Search. In WIAMIS 2012, Dublin, Ireland, May 2012.
imagelocal
descriptor extraction
descriptor aggregation
dimensionality reductionset of local
descriptorsfixed size
vector
encoding & indexing
low dimensional vector
SIFT / SURF BOW / VLAD PCA
PQ + ADC/IVFADC
MSDM 2014, Athens Social Data and Multimedia Analytics #28
Scalable indexing of features
• ADC 16x8 requires 16 bytes per image– ~67M images per GB
• IVFADC requires 4 additional bytes per image– ~53.6M images per GB
• In current implementation we achieve only half of above numbers due to using short int[] instead of byte[], but possible to improve.
• Ideally, 1 billion images could be indexed on a server with 20GB of RAM (projection).
• Query time (for 1M vectors):– Exhaustive search of VLAD vectors (d’=128): 0.50 sec– Product Quantization with ADC 16x8: 0.10 sec (x5 faster)– Product Quantization with IVFADC 16x8: 0.02 sec (x25 faster)
MSDM 2014, Athens Social Data and Multimedia Analytics #29
VLAD+SIFT vs. VLAD+SURFAccuracy vs. dimensionality• VLAD+SURF improves VLAD+SIFT and FV+SIFT across all dimensions in
both Holidays and Oxford datasets
Results in rows starting with * are taken from Jégou et al., 2011, hence the missing values for some entries.SIFT corresponds to PCA reduced SIFT which yielded better results than standard SIFT in Jegou et al., 2011
MSDM 2014, Athens Social Data and Multimedia Analytics
Large-scale graph-based clustering• Problem: Discover
structure in large-scale datasets by exploiting their relations
• Challenges - Approach: – Large-scale– Fast response times– Efficient memory usage– Noise Resilient– Number of clusters not
known• Structural similarity +
local expansion community detection techniques
MSDM 2014, Athens Social Data and Multimedia Analytics
• Structural similarity + Local expansion
(highly efficient and scalable approach)
• Not necessary to know the number of clusters
• Noise resilient(not all nodes need to be part of a community)
• Generic approach adaptable to many applications
(depending on node – edge representation)
+
S. Papadopoulos, Y. Kompatsiaris, A. Vakali. “A Graph-based Clustering Scheme for Identifying Related Tags in Folksonomies”. In Proceedings of DaWaK'10, Springer-Verlag, 65-76
Large-scale graph-based clustering
MSDM 2014, Athens Social Data and Multimedia Analytics
Computational Verification in Social Media
• Create a computational verification framework to classify tweets with unreliable media content.
• Events used for experimentation
#32
Fake images posted during Hurricane Sandy natural disaster Fake images posted during Boston Marathon bombings
MSDM 2014, Athens Social Data and Multimedia Analytics
Methodology
#33
MSDM 2014, Athens Social Data and Multimedia Analytics
Results• Tweet Statistics
• Approaches
#34
Tweets with URLs 343939
Tweets with fake images 10758
Tweets with real images 3540
Hurricane Sandy Boston Marathon
Tweets with URLs 112449
Tweets with fake images 281
Tweets with real images 460
Classifier Classified correctly(%)
Content features
User features
Total features
J48 tree 81.41 67.72 80.68
KStar 81.28 71.16 81.38
Random Forest
80.59 70.15 80.94
Detection accuracy using cross – validation approach
Classifier Classified correctly(%)
Content features
User features
Total features
J48 tree 76.45 70.81 81.25
KStar 81.28 74.12 75.78
Random Forest
78.59 76.15 79.10
Hurricane Sandy Boston Marathon
MSDM 2014, Athens Social Data and Multimedia Analytics
Results(2)
#35
Classifier Classified correctly(%)
Content features
User features
Total features
J48 tree 73.79 51.06 65.06
KStar 75.30 62.29 53.31
Random Forest
74.02 63.10 65.96
Detection accuracy using different training and testing set in Hurricane Sandy
Classifier Classified correctly(%)
Content features
User features
Total features
J48 tree 55.05 50.12 54.10
KStar 50.01 50.10 50.97
Random Forest
58.75 51.03 58.78
Detection accuracy using Hurricane Sandy for training and Boston Marathon for testing
MSDM 2014, Athens Social Data and Multimedia Analytics #36
Other approaches
• Graph-based multimodal clustering for social event detection in large collections of images– automatic organization of a multimedia collection into
groups of items, each (group) of which corresponds to a distinct event.
• Unsupervised concept learning detection using social media as training data
• Text analysis for entities matching and sentiment analysis
• Placing images based on content-features• Retrieving diverse images for same entity
MSDM 2014, Athens Social Data and Multimedia Analytics #37
Demos - ApplicationsMM News DemoClusttourThesFest
MSDM 2014, Athens Social Data and Multimedia Analytics
Multimedia Demo
MSDM 2014, Athens Social Data and Multimedia Analytics #39
Multimedia Demo Architecture
#39
StreamManager
Twitter Facebook Flickr YouTube RSS Instagram160.xx.xx.207
MongoDBWrapper160.xx.xx.207
TextIndexer (Solr)160.xx.xx.207
160.xx.xx.207
MediaFetcher, FeatureExtractor (HDFS)160.xx.xx.58 160.xx.xx.107
Social Focused Crawler (HDFS)160.xx.xx.187
Nutch
Nutch VLAD
FeatureIndexer (HDFS)160.xx.xx.207
IVFADC
Data Mining160.xx.xx.191
Visual Clust. Geo Clust. Statistics
Web server160.xx.xx.116
API (3)API (4)
API (1) API (2)
MSDM 2014, Athens Social Data and Multimedia Analytics
tags: sagrada familia, cathedral, barcelona
taken: 12 May 2009lat: 41.4036, lon: 2.1743
PHOTOS & METADATASPATIAL CLUSTERING + TEMPORAL ANALYSIS
COMMUNITY DETECTION
CLASSIFICATION TO LANDMARKS/EVENTS
VISUAL
TAGHYBRID
[2 years, 50 users / 120 photos]
#users / #photos
duration[1 day, 2 users / 10 photos]
S. Papadopoulos, C. Zigkolis, Y. Kompatsiaris, A. Vakali. “Cluster-based Landmark and Event Detection on Tagged Photo Collections”. In IEEE Multimedia Magazine 18(1), pp. 52-63, 2011
City profile creation (Clusttour)
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City profile creation (Clusttour)
Community detection on image similarity graphs
Nodes: photosEdges: visual and tag
similarity
MSDM 2014, Athens Social Data and Multimedia Analytics
MSDM 2014, Athens Social Data and Multimedia Analytics #43
ThessFest
• Thessaloniki International Film Festival
• Support twitter/comment usage within the app
• Ratings and comments per film
• Feedback aggregation• Votes• Tweets
• Real-time feedback to the organisation and visitors
ThessFest
MSDM 2014, Athens Social Data and Multimedia Analytics
Fête de la Musique Berlin app• FETEberlin in App Store and Google Play• More than 100K visitors• About 5K musicians• More than 5K app downloads, 25K
sessions
App features•Browse and filter detailed program•Interactive maps and routing •Social Sharing•Artists’ and Stages Details•Social MonitoringMain benefits for attendants•Visitors can browse through maps and don’t get lost as stages are numerous•Event schedule is available always and per stage
– Very useful when the server was down and there was no access to the online schedule
#44
MSDM 2014, Athens Social Data and Multimedia Analytics #45
Topic analysis
• Top-10 topics• Manual inspection
of clusters:– 53.8% of topic titles
considered informative
– 98.5% of clusters were found to be “clean”
• Topics in time
MSDM 2014, Athens Social Data and Multimedia Analytics
Other Application Areas
• Science– Sociology, machine learning (machine as a teacher), computer vision
(annotation)• Tourism – Leisure – Culture
– Off-the-beaten path POI extraction• Marketing
– Brand monitoring, personalised ads• Prediction
– Politics: election results• News
– Topics, trends event detection• Others
– Environment, emergency response, energy saving, etc
MSDM 2014, Athens Social Data and Multimedia Analytics
Conclusions – Further topics• Social media data useful in many applications• Not all data always available (e.g. User queries, fb)
– Infrastructure– Policy - Privacy issues
• Real-time and scalable approaches– Efficiency of semantics and analysis vs. performance vs. infrastructure
• Fusion of various modalities– Content, social, temporal, location
• Verification & Linking other sources (web, Linked Open Data)• Visualization - Interfaces• Applications and commercialization• User engagement
MSDM 2014, Athens Social Data and Multimedia Analytics
Reusable results
• Starting point: http://www.socialsensor.eu/results – Deliverables– Publications – Datasets– Software– e-letter: http://stcsn.ieee.net/e-letter/vol-1-no-3
• Open-source projects (Apache License v2): https://github.com/socialsensor
– Data collection (stream-manager, storm-focused-crawler)– Indexing (framework-client, multimedia-indexing)– Mining (topic-detection, multimedia-analysis, community-evolution-
analysis, social-event-detection)
MSDM 2014, Athens Social Data and Multimedia Analytics
European Centre for Social Media
• Topics– Social media analytics– Verification– Visualisation– Applications in different domains
• Activities– Listings of project, results, institutions, events– Community building– Support/organise events– Common social media presence (e.g. LinkedIn)– Funding from subscriptions, training, commercialisation
– Supporting projects: SocialSensor, Reveal, MULTISENSOR, PHEME, DecarboNet, MWCC, uComp,
– Website: http://www.socialmediacentre.eu/ – Research-academic: STCSN http://stcsn.ieee.net/
MSDM 2014, Athens Social Data and Multimedia Analytics
Contributions from• Dr. Symeon Papadopoulos
• Leading R&D in Social Media Mining• Large-Scale visual search• Community detection – Clusttour
• Dr. Sotirios Diplaris• SocialSensor Technical Project Manager
• Lefteris Spyromitros (PhD Student, AUTH)• Large-Scale visual search
• Christina Boididou • Social Media Verification
• Lazaros Apostolidis• Visualization - User Interface MM News Dem0
• Manos Schinas• Topic Analysis• Back-end Thessfest – Clusttour• MM News Demo
• Juxhin Bakalli • iOS Applications development (ThessFest - Clusttour)
• Antonis Latas• Android Application Development (Thessfest)