Presentation at CBDS seminar Big Data Matters 27 sept 2017

2. Useful.Beatiful.Data: social media
− To produce official statistics you need DATA
 Its getting more and more difficult to collect data from
respondents
• Response burden
• Decreasing response rates
• Mode effects (CAPI/PAPI/CATI/CAWI)
− What are alternatives?
 Admin data sources (since the 80’s)
 BIG DATA (NOW), such as social media

3. The glass if half full

4. Potential of social media
− 3 million public messages produced every day in the
Netherlands
 mainly on Twitter and Facebook (~60%)
 Nearly ‘real-time’ available
− Content: Topics discussed
 50% ‘pointless babble’ (noisy) but there are messages relevant for official
statistics
 Selecting the relevant part is important (removing noise)
− Producers: Not much info (directly) available
 But much can be derived

5. Social media in the Netherlands
Map by Eric Fischer (via Fast Company)
Map by Eric Fischer

6. Examples of social media studies at CBS/CBDS
− Content
1. Sentiment in social media
 What is the development of the average sentiment in social media
over time?
2. Feelings of social tension
 Can social media be used to measure specific feelings in (the online)
society?
3. Propensity to move (‘Wish to move’)
 Can we identify messages of people that wish to move to another
house?
− Population
4. Characterizing users
 Derive characteristics /discern subpopulations

7. 1. Social media sentiment

8. 1. Social media sentiment (2)
− Facebook and Twitter messages both contribute
− Daily data is highly volatile
− Monthly aggregates correlate well with consumer confidence (> 0.9)
− Including sentiment series improves the accuracy of consumer
confidence series (survey data)
− Product:
 Averaged monthly or smoothed weekly online Dutch sentiment could be a
potential new indicator
 Can also be produced for large Dutch cities

9. 2. Social tension indicator
Available at: http://research.cbs.nl/socialtension/en/
Percentageofmessagesindicatingsocialtension

10. 2. Social tension indicator (2)
− Currently based on Twitter messages alone
 Other platforms can be added
− Selected messages containing specific keywords
 These were originally derived from the safety monitor questionnaire
 Used the events detected as feedback
− Peaks indicate points in time at which increasing numbers of social
tension related messages are produced
 Usually don’t last long
 Sometime a shift in the base line is observed (i.e. MH17)
− Product: can be produced on a daily basis
 This is how ‘real-time’ statistics will look like

11. 3. ‘Wish to move’
− Current topic of research
 Social media contains messages that indicate a ‘wish’ of people
to move to another house (on all platforms)
 Select messages containing ‘verhuiz*’ or ‘verhuis*’
 Created a model to identify messages of people that wish to
move (accuracy 0.85 ±0.02)
 Relate social media findings to findings derived from
survey/admin data
 Study time-series to check on what frequency such an indicator
could best be produced

12. 4. Characterizing social media users
− Social media contains multiple populations
− Identifying Dutch users
• 3 approaches:
 From meta/para data available (language setting, location)
 From network structure (following), essential when hardly any
user info is present
 From texts, discern Dutch and Flemish ‘tweets’

13. 4. Characterizing social media users (2)
− Social media contains multiple populations
− Discerning between accounts of people and companies
 2 step approach
Human
(private users)
Non-human
(corporate users)
Private persons
(77%)
Self-employed
(9%)
‘Non-profit groups’
(11%)
Companies
(3%)

14. 4. Characterizing social media users (3)
− Social media contains multiple populations
− Identifying background characteristics
 Challenging topic:
• Gender (M/F) could be identified with 96% accuracy
- Combining user short bio, first names, tweet content and pictures
- 50% male, 33% female, 17% ‘others’
• Other characteristics are possible (future research)

15. Conclusion
− Social media is an interesting data source for official statistics
− To enable this, two steps are essential:
– Noise reduction
− By aggregating lots of data
− By removing ‘off-topic’ messages
− Correct differences between ‘on-line’ and ‘real-world’ populations
– By removing non-target population users
– By applying a model (work in progress)