Social Data and Multimedia Analytics for News and Events Applications

1. 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

2. 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

3. MSDM 2014, Athens Social Data and Multimedia Analytics#3 Introduction Motivation Example Applications Conceptual Architecture Challenges

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

5. 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)

6. 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/

7. MSDM 2014, Athens Social Data and Multimedia Analytics 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/

8. 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)

9. 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…”

10. 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”

11. MSDM 2014, Athens Social Data and Multimedia Analytics Events - Festivals #11 http://www.eventmanagerblog.com/uploads/2012/12/event-technology-infographic.jpg

12. 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

13. 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

14. 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

15. MSDM 2014, Athens Social Data and Multimedia Analytics#15 Social Sensor Project Use Cases

16. MSDM 2014, Athens Social Data and Multimedia Analytics 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

17. MSDM 2014, Athens Social Data and Multimedia Analytics#17 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

18. MSDM 2014, Athens Social Data and Multimedia Analytics#18 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

19. 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

20. 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

21. 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)

22. MSDM 2014, Athens Social Data and Multimedia Analytics Verification was simpler in the past... Source: Frank Grätz #22

23. 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)

24. MSDM 2014, Athens Social Data and Multimedia Analytics#24 Research Approaches Large-Scale Visual Search Clustering – Community Detection Social Media Verification

25. MSDM 2014, Athens Social Data and Multimedia Analytics#25 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

26. 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

27. 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. 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

28. 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)

29. MSDM 2014, Athens Social Data and Multimedia Analytics#29 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

30. 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

31. 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

32. 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

33. MSDM 2014, Athens Social Data and Multimedia Analytics Methodology #33

34. 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

35. 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

36. 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

37. MSDM 2014, Athens Social Data and Multimedia Analytics#37 Demos - Applications MM News Demo Clusttour ThesFest

38. MSDM 2014, Athens Social Data and Multimedia Analytics Multimedia Demo

39. MSDM 2014, Athens Social Data and Multimedia Analytics#39 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)

40. MSDM 2014, Athens Social Data and Multimedia Analytics 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)

41. MSDM 2014, Athens Social Data and Multimedia Analytics#41 City profile creation (Clusttour) Community detection on image similarity graphs Nodes: photos Edges: visual and tag similarity

42. MSDM 2014, Athens Social Data and Multimedia Analytics

43. 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

44. 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 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

45. 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

46. 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

47. 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

48. 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)

49. 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/

50. 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)

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