Development of a system that automatically generates (kind of) storylines out of social media aggregated around hashtags, following links being shared.

1. Extracting Resources that Help
Tell Events' Stories
Raphaël Troncy
raphael.troncy@eurecom.fr
Mor Naaman
mor.naaman@cornell.edu
Carlo Andrea Conte
carloandreaconte@icloud.com
@mrgreenh!

2. InputsIntroduction
People share a huge amount of diverse content about real-world events
Links to these resources are often associated with #hashtags

3. OutputsIntroduction
Assuming that Real-world events can
be associated to particular #hashtags at particular points in time
Resources can be filtered and aggregated into storylines

4. Our ApproachIntroduction
Near Realtime!
~ 15-30 mins.
Extracting links from an event's tweets
Resolving shortened urls
Rank (and filter when necessary) links
Collect data by dereferencing those links
ExperimentalResults

5. Conclusions
Experiments
System Architecture
Outline
- Seen.co
- System description
- Ranking methods
- Events Dataset
- Interface
- Storylines
- Ranking performances
- Sources analysis
Results -
Future Work -

6. Conclusions
Experiments
System Architecture

7. Seen.coSystem Architecture
- Starting point for our system
- Rich database of #hashtag-defined events
- For every event, raw Twitter data is available

8. Seen.coSystem Architecture
Automatically organizes content shared on
Twitter and Instagram about an event.!
The resulting narrations highlight the most
important moments and let the user quickly see
what happened.

9. Seen.coSystem Architecture
Our system aims at gathering together a more
diverse set of media, by focusing on the
referenced resources rather than on the tweets
themselves.

10. RequirementsSystem Architecture
- Extracting links from an event's tweets
- Resolving shortened urls
- Rank links
- Collect metadata by dereferencing those links
Near Realtime!
~ 15-30 mins.
Severe Bottlenecks

11. RequirementsSystem Architecture
Process Parallelization
- Extracting links from an event's tweets
- Resolving shortened urls
- Rank links
- Collect metadata by dereferencing those links
Near Realtime!
~ 15-30 mins.
Severe Bottlenecks

12. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save

13. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save

14. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save

15. Links AppearancesSystem Architecture
Links
Appearances
Link A Tweet 1
Link B Tweet 2
Link A Tweet 3
Link C Tweet 4

16. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links
Resolutor
Page Scrapers
Server 1
save
lookup
save

17. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save

18. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save

19. Event LinksSystem Architecture
Event Links
Score
Link A 2
Link B 1
Link C 1

20. ArchitectureSystem Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save

21. Queues and WorkersSystem Architecture
Links Resolutor
Queue
Links Metadata
Queue
Queues
Page ScrapersLinks Resolutor
"Workers" pop jobs from the queues
Setup flexibility, easily scalable

22. Link Score Processors (LSP)System Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save
Decider
LSP

23. Link Score Processors (LSP)System Architecture
Responsible of ranking extracted links
Decider
LSP

24. Link Score Processors (LSP)System Architecture
Links
Dispatcher
Server 2
Content db
WebDB
Links
Mapping
Collection
Pages
Metadata
Collection
Event
Links
Collection
Links
Appearances
Collection
Links Resolutor
Queue
Decider Links Metadata
Queue
Links Mappings
save
lookup
Links
Appearances
save
query
Pages Metadata
Event Links
Links Resolutor Page Scrapers
Server 1
save
lookup
save
Decider
LSP
LSP
LSP

25. Link Score Processors (LSP)System Architecture
Decider
LSP
LSP
LSP
Multiple LSPs can be defined to
adopt different score functions
- Velocity based LSP
- Volume based LSP

26. Velocity BasedRanking Methods
Velocity Based ProcessorVolume
Time
Score
LSP Window

27. Volume BasedRanking Methods
Volume Based ProcessorCumulativeVolume
Time
Score
LSP Window

28. Conclusions
Experiments
System Architecture

29. #KanyeEvents Dataset
Kanye West at Barclays Center
- Music event
- 5 hours
- 794 links (low volume)

30. #TDisruptEvents Dataset
Tech Crunch Disrupt
- Conference
- 3 days
- 1201 links
(medium volume)

31. #SFBatkidEvents Dataset
San Francisco's Batkid
- Breaking News
- 9 hours
- 8842 links (high volume)

32. Building a StorylineExperiments
Front-end Interface
- The story follows a chronological timeline
- Links are illustrated with metadata extracted from
those resources
How to visualize the extracted links in order to tell a story?

33. Front-end InterfaceExperiments

34. Front-end InterfaceExperiments
- Filtering on score
- Top score and number of links

35. Front-end InterfaceExperiments
- Filtering on score
- Top score and number of links
- Source domains composition

36. Front-end InterfaceExperiments
- Filtering on score
- Top score and number of links
- Source domains composition
- Possibility to mark links as false positives

37. Front-end InterfaceExperiments
- Filtering on score
- Top score and number of links
- Source domains composition
- Possibility to mark links as false positives

38. Example LinksExperiments
Instagram
#Kanye Volume Based
Threshold: 2
Instagram
Instagram

39. Example LinksExperiments
App 1 (scandal)
App 2
Apologies
#TCDisrupt Velocity Based
Threshold: 5

40. Example LinksExperiments
#SFBatkid Volume Based
Threshold: 32
News
News
News

41. ProblemsExperiments
- Spam (e.g. tweets advertising unrelated pages,
Twitter bots) easily reaches high volumes
- Content is sometimes removed after publication
(broken links)
- Query parameters in urls cause some duplicated links

42. NumberofLinks
1
10
100
1000
Score Range
1 10 19 28 35
Results True-positives
LSP PerformancesExperiments
Tech Crunch Disrupt
- In this particular case, volume
based results have higher
precision
- Links are better classified
along the scores range
NumberofLinks
1
10
100
1000
Score Range
1 27 53 79 102
Results True-positives
Volume Based
Velocity Based
5

43. LSP PerformancesExperimentsNumberofLinks
1
10
100
1000
10000
Threshold
1 301 601 887
Results True-positives
Volume Based
SFBatkid
Velocity Based
President Obama's Vine
- In the velocity based results,
the top relevant links usually
occupy a relatively big slice
of the topmost part of the
scores range
NumberofLinks
1
10
100
1000
10000
Threshold
1 301 601 901 1201 1501 1801 2101
Results True-positives
32

44. Sources CompositionExperiments
TCDisrupt (no filtering)
Sources Composition
- Different classes of events
have different sources
composition
- This division changes with
increasing threshold
- It is possible to define
"fingerprints" for different
classes of event
Given a specific "fingerprint",
outliers can help define official
sources

45. Sources CompositionExperiments
Music event: mostly visual and
instant sources
Breaking news: bigger slices
represent typical news sources
and mass media

46. Conclusions
Experiments
System Architecture

47. Conclusions
- Development of a system that automatically generates
(kind of) storylines out of social media aggregated around
hashtags, following links being shared
- Extracting, ranking, filtering links
- Select the content which is the most shared
- Generate the results while the event is happening (with
a 15 - 30 minutes delay)
- Implementation of a front-end interface for exploring the
stories generated:
- Fills up a template, following a chronological timeline
- Different genres of events generate different outputs
- Musical events are illustrated with photos and videos,
while breaking news events are described with articles
from newspapers and blogs

48. Conclusions
- More experiments are needed to confirm the pattern we
have described with those three events
- Can we automatically classify an event based on its
signature? Can we train a classifier where the features
would be tweets, velocity and the source domains
composition?
- Implementation of an advanced scoring function taking
into account additional features of the user (lists in which
the user is appearing, etc.) and of the tweets (number of
favorites, number of RT, etc.)
Future work

49. Thank you!
for listeningConclusions
Experiments
System Architecture
Link to slideshare!