Final presentation for King's College London Master's Dissertation

1. Examining the Global
Conversation on Twitter
Bias and mapping geographies in Cyberspace
Abdelrahman Hassan
Supervisor: Dr. Nishanth Sastry

2. Table of contents
The problem
Motivation
Aims and Objectives
Background
Contributions
Results and Accuracy
Conclusion

3. The problem
Undiagnosed conversational terrain on
cyberspace
Twitter as a platform
Location sparsity

4. Key terms
 Home location: Base location of the tweet. i.e.“China”
 Destinations: Locations explicitly mentioned in the tweet. Ie.( Lebanon, Paris)
 Conversational bias: Bias relevant to one conversation.
 Conversational profile of a country: Where a country lies in the conversation
zone model.
A user from china tweets: Pray for Paris, pray for Lebanon, pray for the world.

5. Motivation
A visual representation of conversational bias.
The need for adjacent or parallel geographies
online. Is cyberspace really independent?

6. Aims and Objectives
two key contributions:
comprehensive 3D visualization
Visual footprint and conversational bias.

7. Datasets
 PARIS 2015 dataset
 Rio2016 dataset
 World2016 dataset

8. COMPONENTOVERVIEW

9. Background

10. Contributions

11. 3D visualization
Countries/conversations represented on a 3D sphere.
Each country’s incoming interactions and outgoing
interactions can be seen
A macroscopic view of the global conversation.

12. 3D visualization
http://localhost:63342/armsglobe-
master/index.html?_ijt=hrbe98rhcqopm9s6fg6j9iij1k

13. Visualizing bias: Background
 Concentric zone model:
 Burgess in 1925
 Fruchterman Reingold
 Parallel representation,
 Circular topology.

14. Results and accuracy

15. a) Machine learning and visualization
 The dataset ( Paris2015, Rio2016, world2016)
 300,000 tweets for Paris2015,
 400,000 for Rio2016
 500,000 for World2016
 78% accuracy at country-level granularity
 Mean distance error: 13%.

16. Top 35 countries (93% of tweet
population)

17. b) Evaluating and visualizing bias

18. Paris2015

19. PARIS2015 BIAS
 POPULATION BIAS  RELEVANCE BIAS UNFILTERED FOOTPRINT

20. Rio2016

21. RIO2016-BIAS
 PARTICIPATION BIAS  POPULATION BIAS UNFILTERED FOOTPRINT

22. Conclusions
 Context matters but core bias still holds.
 Further investigation is needed.