social media crawling and mining seminar (motivation part)
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Lecture @ International Hellenic UniversityThessaloniki, 8 May 2014
Social Media Crawling and MiningMotivation – Use CasesSymeon (Akis) Papadopoulos, Manos Schinas, Katerina Iliakopoulou, Yiannis KompatsiarisInformation Technologies Institute (ITI)Centre for Research & Technologies Hellas (CERTH)
MSDM 2014, Athens Social Data and Multimedia Analytics #2
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 #5
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
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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.
8
“…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)
#9
“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
#10http://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
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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
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“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
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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
#21
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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
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/
Thank you for your attention!ikom@iti.gr
http://mklab.iti.gr
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