The document discusses an iterative process for extracting emerging knowledge from social networks, focusing on the evolution and geographic spread of this knowledge. It presents methods for retrieving knowledge related to various domains, demonstrating good precision despite multiple iterations. Future work aims to enhance the domain model by analyzing additional social media features.

1. Iterative
Knowledge Extraction
from Social Networks
Marco Brambilla, Stefano Ceri, Florian Daniel,
Marco Di Giovanni, Andrea Mauri, Giorgia Ramponi
The Web Conference, WWW 2018, Lyon, France
MSM'2018

2. Humans aim at
formalizing knowledge

3. Famous Emerging
…
Famous: entities on WikiPedia
or in other knowledge bases

4. Famous Emerging
…
Emerging: entities not present in
any knowledge bases

5. Extracting Emerging Knowledge from Social Media
Marco Brambilla, et Al. 2017. Extracting Emerging Knowledge from Social Media.
In Proceedings of the 26th International Conference on World Wide Web (WWW '17).
DOI: https://doi.org/10.1145/3038912.3052697

6. Extracting Emerging Knowledge from Social Media
@Kasparov63
@LevAronian
@MagnusCarlsen
@FabianoCaruana

7. Extracting Emerging Knowledge from Social Media
ChessPlayer
Tournament

8. Extracting Emerging Knowledge from Social Media

9. Extracting Emerging Knowledge from Social Media

10. Extracting Emerging Knowledge from Social Media
Feature vector: [𝑡1, 𝑡2, 𝑡3,..,𝑖1, 𝑖2, 𝑖3, . . ] where:
• 𝑡𝑖 are DBpedia types
• 𝑖𝑖 are DBpedia instances of Expert Types

11. Extracting Emerging Knowledge from Social Media

12. Extracting Emerging Knowledge from Social Media

13. Extracting Emerging Knowledge from Social Media

14. Extracting Emerging Knowledge from Social Media

15. Iterative Extraction Process

16. 1. How does reconstructed domain knowledge evolve with an iterative
process?
2. How does the reconstructed domain knowledge spread
geographically?
3. Can the method be used to inspect the past, present, and future of
knowledge?
4. Can the method be used to find emerging knowledge?

17. 1. How does reconstructed domain knowledge evolve with an iterative
process?
Extremely domain dependent

18. 1. How does reconstructed domain knowledge evolve with an iterative
process?
Extremely domain dependent
Precision remains rather stable

19. 1. How does reconstructed domain knowledge evolve with an iterative
process?
Extremely domain dependent
Precision sometimes also increases

20. 2. How does the reconstructed domain knowledge spread geographically?
USA ChessPlayers

21. 2. How does the reconstructed domain knowledge spread geographically?
USA ChessPlayers

22. 2. How does the reconstructed domain knowledge spread geographically?
USA ChessPlayers

23. 2. How does the reconstructed domain knowledge spread geographically?
Iteratively found knowledge spans large geographical areas very fast

24. 3. Can the method be used to inspect the past, present, and future of
knowledge?
2016.01 2016.03 2016.06 2016.09 2016.12
27
candidates
Fashion designer experiment

25. 3. Can the method be used to inspect the past, present, and future of
knowledge?
2016.01 2016.03 2016.06 2016.09 2016.12
27
candidates
34
new candidates
Fashion designer experiment

26. 3. Can the method be used to inspect the past, present, and future of
knowledge?
2016.01 2016.03 2016.06 2016.09 2016.12
27
candidates
34
new candidates
18
new candidates
Fashion designer experiment

27. 3. Can the method be used to inspect the past, present, and future of
knowledge?
2016.01 2016.03 2016.06 2016.09 2016.12
27
candidates
34
new candidates
18
new candidates
16
new candidates
Fashion designer experiment

28. 4. Can the method be used to find emerging knowledge?
Fashion designers Finance influencers
Fiction Writers Chess Players
Emergent
Famous

29. Some updates..
Syntactic features: verbs, nouns and proper nouns

30. Some updates..
Feature vector of user u: [𝑛1, 𝑛2, 𝑛3, . . ] where:
• 𝑛𝑖 are nouns/verbs/proper nouns frequencies in user tweets
Syntactic features: verbs, nouns and proper nouns

31. Some updates..
Feature vector of user u: [𝑛1, 𝑛2, 𝑛3, . . ] where:
• 𝑛𝑖 are nouns/verbs/proper nouns frequencies in user tweets
Syntactic features: verbs, nouns and proper nouns
We consider 𝑛𝑖 as the probabilities that u uses the i-th word in his
tweets.

32. Some updates..
Feature vector of user u: [𝑛1, 𝑛2, 𝑛3, . . ] where:
• 𝑛𝑖 are nouns/verbs/proper nouns frequencies in user tweets
We consider 𝑛𝑖 as the probabilities that u uses the i-th word in his
tweets.
Syntactic features: verbs, nouns and proper nouns
DISTRIBUTION OF WORDS Entropy as metric

33. Evaluation with 10 seeds, 10 good candidates and 600 random accounts
Finance domain

34. Conclusion
• We show the geographic and temporal spreading of entities extracted
by the method.
• The method grants a good precision even after some iterations in
many domains.
• Future work includes the semi-automatic building of a richer domain
model, by studying other twitter features (such as verbs and nouns
which appear in tweet texts).

35. THANKS!
QUESTIONS?
Contacts: marco.brambilla@polimi.it
http://datascience.deib.polimi.it
Twitter: datascience_mi and marcobrambi
Iterative Knowledge Extraction from Social Networks
Marco Brambilla, Stefano Ceri, Florian Daniel, Marco Di Giovanni, Andrea Mauri, Giorgia Ramponi