This document discusses using social media data to analyze political and economic trends: - It analyzed over 230,000 tweets from Belgium to estimate public support for different political parties, finding a strong correlation between party mentions and official polling data. - A similar analysis of German Twitter data correctly predicted the results of the 2009 national election. - Mentions of Belgian political leaders on Twitter also closely matched popularity polls for those individuals. - The document concludes social media can provide insights into trends where traditional data is limited or unavailable.

1. THE VOICE OF CITIZENS ON SOCIAL NETWORKS
Di
100,000 MESSAGES
(17/02/2014-08/03/2014)
2014
ALL RIGHTS TEVIZZ 2014. THIS PRESENTATION IS PROPRIETARY AND STRICTLY CONFIDENTIAL
230,000 TWEETS
FROM 15/02/2014 TO 20/04/2014
didier.joos@tevizz.com

2. HOW COULD YOU ESTIMATE
THE RELATIVE GDPs OF 100
COUNTRIES IN WITH NO
OFFICIAL ECONOMICS DATA?

3. BURGERNOMICS
Source : IMF (2011)
BIG MAC PRICE V GDP)
GDP BIGMAC PRICE
34,000 Restaurant

4. BURGERNOMICS TO TWITTERNOMICS
Source : IMF (2011)
BIG MAC PRICE V GDP (AND EXCHANGE RATES) MENTIONS ON TWITTER V INTENTIONS OF VOTE
GDP ELECTION RESULTS

5. % Intention of votes for parties **
*Data collected by TEVIZZ between 15/02/2014 and 20/04/2014 (320,703 mentions, 42,354 users)
**Poll by Dedicated in 3 regions based on 2767 internet interviews 08/04/2014 and 14/04.2014. Total % are computed by TEVIZZ based on
intentions on vote by regions (Flanders, Wallonia, Brussels) from Dedicated poll weighted by number of citizens by region.
PS
NV-A
Open VLD
PDVA-PTB
CD&V
MR
SP.A
CDH
Ecolo
% Mentions of parties on Twitter*
ALL RIGHTS TEVIZZ 2014. THIS PRESENTATION IS PROPRIETARY AND STRICTLY CONFIDENTIAL
VB
GROEN
R² = 0,8183
0%
5%
10%
15%
20%
25%
0% 5% 10% 15% 20% 25%
FDF
Parties and Twitter mentions

6. IT WORKS IN COUNTRIES
WITH LOW PENETRATION OF TWITTER
Results of German election 2009 and Twitter’s mentions by parties
SOURCE: Andranik Tumasjan Philipp G. Sandner Isabell M. Welpe “Election Forecasts With
Twitter: How 140 Characters Reflect the Political Landscape“ 2012

7. % Favourable opinion about candidates (IPSOS) **
*Data collected by TEVIZZ between 15/02/2014 and 20/04/2014 (320,703 mentions, 42,354 users)
**Poll by IPSOS in 3 regions with sample of 2767 (6 to 10 February 2014) . Total % are computed by TEVIZZ based on intentions on vote by
regions (Flanders, Wallonia, Brussels) from IPSOS poll weighted by number of citizens by region.
Verhofstadt
Di Rupo
De Wever
Peeters
De Blok
% Mentions of parties on Twitter*
ALL RIGHTS TEVIZZ 2014. THIS PRESENTATION IS PROPRIETARY AND STRICTLY CONFIDENTIAL
Maingain
Candidates and Twitter mentions

8. R² = 0,8357
0
5000
10000
15000
20000
25000
0% 10% 20% 30% 40% 50% 60%
De Wever
Lutgen
Milquet
% Mentions of parties on Twitter*
ALL RIGHTS TEVIZZ 2014. THIS PRESENTATION IS PROPRIETARY AND STRICTLY CONFIDENTIAL
Vande Lanotte
Maingain
Toback
De Block
*Data collected by TEVIZZ between 15/02/2014 and 20/04/2014 (320,703 mentions, 42,354 users)
**Poll by IPSOS in 3 regions with sample of 2767 (6 to 10 February 2014) . Total % are computed by TEVIZZ based on intentions on vote by
regions (Flanders, Wallonia, Brussels) from IPSOS poll weighted by number of citizens by region.
Excluding candidates not
usingTwitter
#Candidates and Twitter mentions
% Favourable opinion about candidates (IPSOS) **

9. « There is no such a thing as bad
publicity (except for your
obituary) » (B. Behan)
(No need to neutralize bias :
positive/neutral/negative opinion, ultras ,
demographics, …)

10. TODAY’S POPULARITY CIRCLE
POPULARITY
BUZZ
MEDIA
HEADLINES
SEARCH &
FIND
SHARE

11. Google search and Twitter’s mentions of « Gangnam Style »
Source : Ayman Farahat & Bongwon Suh ( Adobe Systems) “Do we search on what we tweet or tweet on
what we search?”, 2012