Social Data and Multimedia Analytics for News and Events Applications

Social Data and Multimedia Analytics for News
and Events Applications
Dr. Yiannis Kompatsiaris, ikom@iti.gr
Multimedia, Knowledge and Social Media Analytics Lab, Head
CERTH-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

Introduction
Motivation
Example Applications
Conceptual Architecture
Challenges

MSDM 2014, Athens Social Data and Multimedia Analytics
www.puzzlemarketer.com/digital-social-brands-in-60-seconds/ (Apr, 2012)

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)

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/

Social Networks as Graphs
10
social web as a graph
nodes=twi er users
edges=retweetson #jan25 hashtag
announcement of Mubarak’sresigna on
h p://gephi.org/2011/the-egyp an-revolu on-on-twi er/

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)

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…”

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”

Events - Festivals
#11
http://www.eventmanagerblog.com/uploads/2012/12/event-technology-infographic.jpg

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

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

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

Social Sensor Project
Use Cases

SocialSensor Project Objective
SocialSensor quickly surfaces trusted and relevant material
from social media – with context.
DySCODySCO
behaviou
r
location
timecontent
usage
social context
Massive social media
and unstructured web
Social media mining
Aggregation & indexing
News - Infotainment
Personalised access
Ad-hoc P2P networks

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

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

Use Cases
Casual News
application
Casual News Readers
Professional
News application
Journalists, Editors, etc.
NEWS
EventLiveDashboard
Festival organizers
INFOTAINMENT
Social Media Walls
Festival attendants

“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

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)

Verification was simpler in the past...
Source: Frank Grätz
#22

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)

Research Approaches
Large-Scale Visual Search
Clustering – Community Detection
Social Media Verification

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

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

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.
image
local
descriptor
extraction
descriptor
aggregation
dimensionality
reductionset of local
descriptors
fixed size
vector
encoding &
indexing
low dimensional
vector
SIFT / SURF BOW / VLAD PCA
PQ + ADC/IVFADC

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)

VLAD+SIFT vs. VLAD+SURF
Accuracy 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

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

• 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

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

Methodology
#33

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
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

Results(2)
#35
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
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

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

Demos - Applications
MM News Demo
Clusttour
ThesFest

Multimedia Demo

Multimedia Demo Architecture
#39
StreamManager
Twitter Facebook Flickr YouTube RSS Instagram
160.xx.xx.207
MongoDBWrapper
160.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 Mining
160.xx.xx.191
Visual Clust. Geo Clust. Statistics
Web server
160.xx.xx.116
API (3)API (4)
API (1) API (2)

tags: sagrada familia,
cathedral, barcelona
taken: 12 May 2009
lat: 41.4036, lon: 2.1743
PHOTOS & METADATA
SPATIAL CLUSTERING + TEMPORAL ANALYSIS
COMMUNITY DETECTION
CLASSIFICATION TO LANDMARKS/EVENTS
VISUAL
TAG
HYBRID
[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)

City profile creation (Clusttour)
Community detection on
image similarity graphs
Nodes: photos
Edges: visual and tag
similarity

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

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 Monitoring
Main 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

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

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

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

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)

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/

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)

Thank you for your attention!
ikom@iti.gr
http://mklab.iti.gr

Social Data and Multimedia Analytics for News and Events Applications

More Related Content

What's hot (20)

Similar to Social Data and Multimedia Analytics for News and Events Applications (20)

More from Yiannis Kompatsiaris (16)

Recently uploaded (20)

Social Data and Multimedia Analytics for News and Events Applications

Editor's Notes