The microblogging service, Twitter, has gained wide popularity with over 300M active users and over 500M tweets per day. The unique characteristic of Twitter, only allowing short length messages to be communicated, has brought about interesting changes to how information is expressed and communicated by the users, i.e., the semantics of information when expressed on Twitter differ from when expressed on other medium. For instance, the word 'metal' when observed on Twitter carries a different semantic meaning, most likely referring to heavy metal music, as opposed to when used in other contexts where its predominant sense is the metal material. In this talk, I will discuss how the meaning and senses of words can be captured and modeled on Twitter to enable better and more efficient search, retrieval and recommendation of content.

1. Modeling Semantics of
Content on Twitter
What did you mean when you said yoyo?
Ebrahim Bagheri
Laboratory for Systems, Software and Semantics (LS3)
@ebrahim_bagheri

6. A study in child health journal Acta Paediatrica
compared the physical fitness of 315 ten-year-olds
in 2008 with 1998.

7. Over the same period the arm strength of children fell by 26 per
cent, while their grip strength declined by 7 per cent.
The number of sit-ups fell by 27 per cent over the decade.

8. 5% of children were unable to hold their own weight when hanging from a bar in 1998.
Ten years later, that figure had doubled to 10%.

10. Social Impact and Influence
1/3 of respondents had no one in “the real world” with whom
to discuss their epilepsy.
2/3 of respondents reported a connection to at least one other
person with epilepsy after joining PatientsLikeMe.
59% reported that the site has given them a better
understanding of their seizures.
23% of respondents reported fewer visits to the ER.
28% reported that they felt more control over their condition
after joining PatientsLikeMe.
49% of respondents reported that
recording their seizures helps
them manage their condition.
50% Reported that
PatientsLikeMe helped
them understand their side
effects.

12. Sample Dataset Behavior

13. Free Riding Behavior?

14. Semantic Analysis of Tweets
“OMG! Cate Blanchett won the Best Actress Academy Award for Woody
Allen's film #excited #oscars”
https://en.wikipedia.org/wiki/Woody_Allen
https://en.wikipedia.org/wiki/Cate_Blanchett
14
https://en.wikipedia.org/wiki/Academy_Awards

15. Ambiguity?

16. Ambiguity?

19. Disambiguation
• The objective:
– for each ‘spot’ (i.e., mention spotted in the text),
select the entity/entities that properly reflect(s)
the semantics of the spot
– the selection is done from, often numerous,
candidate entities.
19
“They performed Kashmir, written by Page and Plant. Page
played unusual chords on his Gibson.”
Wikipedia:Kashmir – a valley between Pakistan, India and Ladakh
Wikipedia:Kashmir_(band) – a Danish rock band
Wikipedia:Kashmir_(song) – 1975 song by rock band Led Zeppelin
Wikipedia:Kashmir,_Iran – a village in Iran
…

20. Sense Dominance
Can we determine the likelihood of a sense?

23. Concept Distribution

24. Noise Detection

25. More Noise
Constant power spectral density

26. Unsupervised Topic Detection

27. Concept Lifecycle - Behavior

28. Clustering the Correlation Graph

29. Temporal and Topic Sensitive
Communities

30. User Contributions to Topics

31. CommunityCollectiveBehavior

32. Users in C5 contribute to three topics:
• Santa Claus (t9),
• Tornado during New Year celebrations (t30)
• Thanksgiving (t37)
Users in C6 contribute to t30 in the same time intervals but they do
not contribute to t9 and t30;

33. News Recommendation

34. Implicit Interests

35. Collaborative Semantic
Topic Relatedness

38. Part of Speech Distribution

39. Noticeable Semantic Shift

40. Semantic Relatedness Distribution

41. So what does “yoyo” mean on Twitter?