This document summarizes a study that analyzed social media content from Poland during the pre-election period in 2011 to predict the results of the country's general election. The study found that the amount and sentiment of content about different political parties on blogs and forums correctly identified the top two parties that went on to gain the most votes. Quantitative analysis showed the most discussion was on blogs and forums, while qualitative analysis found content focused more on essential policy areas than just media topics. The results supported using social media content analysis to forecast election outcomes. Further research is needed to better understand influencers and advance sentiment analysis techniques.

1. General Online Research Conference
GOR 13, 4-6 March 2013
Baden-Wuerttemberg Cooperative State University Mannheim, Germany
Wlodzimierz Gogolek, Institute of Journalism, University of Warsaw
Pawel Kuczma, Institute of Journalism, University of Warsaw
Forecasting General Election Results in Poland
2011 on the basis of Social Media content
Contact: p.kuczma@id.uw.edu.pl

2. Forecasting General Election Results in Poland
2011 on the basis of Social Media content
Paweł Kuczma, Włodzimierz Gogołek
Institute of Journalism
University of Warsaw
Graphic source: http://jonnewman12.files.wordpress.com/2010/05/sm-crystal-ball.png
GOR 13, 4-6 March 2013
Baden-Wuerttemberg Cooperative State University Mannheim

3. Data Refining* Process
Results
Data from Social
Media & News Portals
Qualitative
Analysis
Quantitative
Analysis
Włodzimierz Gogołek, Refining network information (Rafinacja informacji sieciowej), [in:], Aleksander Jastriebow, Maria
Raczyńska, Informatyka w dobie XXI wieku, Nauka, Technika, Edukacja a nowoczesne technologie informatyczne, Radom 2011,
Politechnika Radomska, s. 229 - 238.

4. The purpose of this study:
The purpose of this study was to define factors allowing prediction of the outcome of
the general election in Poland in October 2011 basing on big data resources from
Social Media websites in pre-election period in, what we call, data refining process.
The research question was:
Is it possible to predict the action (cast a vote for a political party in general election)
on the basis of quantitative (number of content related to the subject of the
research) and qualitative (the contexts in which they appear and their emotional
values) content analysis on Social Media?
Study Details - Methodology

5. Study Details - Methodology
Type of content analysed:
 Social Media websites (such as social networking sites, forums, blogs and microblogs)
 News portals (websites with content written by professionals) for comparative analysis
Time of the study: Analysed content was published in pre-election period in 2011
(between March 1st and Octobrer 31st)
The following indicators of the content were examined :
 Quantitative assessment:
- Amount of the content about candidates,
- Trends / dynamics of changes in the amount of the content,;
 Qualitative assessment:
- Contexts analysis (finding topic content),
- Sentiment analysis (distinction between positive and negative content).

6. Quantitative assessment

7. Share of voice by different types of Social Media
websites
Most important sources were Blogs and Forums – 97% of content
Blogs
57.72%
Forums
39.92%
Facebook
0.31%
Twitter
2.05%

8. Election results
Source: State Electoral Comission
Party Vote Share
PO 39,18%
PiS 29,89%
Ruch Palikota 10,02%
SLD 8,24%
PSL 8,36%
PJN 2,19%

9. Parties’ visibility online, especially in Social Media

10. Two the most popular parties in Social Media
gained biggest users attention in this channel
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Ruch Palikota PiS PJN PO PSL SLD
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Ruch Palikota PiS PJN PO PSL SLD
Blogs Forums

11. Two the most popular parties in Social Media
gained biggest users’ attention in both
channels along with SLD
Ruch Palikota,
2%
PiS, 22%
PJN, 6%
PO, 37%
PSL, 11%
SLD, 22%
Ruch Palikota,
4%
PiS, 23%
PJN, 9%
PO, 32%
PSL, 12%
SLD, 22%
Blogs Forums

12. Qualitative assessment

13. Social Media context od content
Edukacja, 907
Finanse, 1843 Gospodarka, 1195
Infrastruktura, 1710
Katastrofa, 2323
Kościół, 634
Kultura, 764
Media, , 4231
Nauka i Szkolnictwo
Wyższe, 662
Obrona, 137
Prawo, 5664
Reformy, 368
Sprawy Zagraniczne, 1804
Środowiska, 0 UE, 797
Władza, 0 Zdrowie,
810

14. Media contexts vs. Essential contexts
Media context
35%
Essential
context
65%
Media contexts – which appear strongly in media during the campaign period
Essential contexts – connected directly with powers of the government

15. Sentiment analysis in Social Media (forums)
Data gathered: 1.03-31.10.2011
0
50
100
150
200
250
300
350
SLD
PSL
PO
PJN
PiS
Palikot
0
50
100
150
200
250
300
350
400
450
SLD
PSL
PO
PJN
PiS
Palikot
Positive Negative

16. Data gathered: 1.03-31.10.2011
0
50
100
150
200
250
300
350
400
SLD PSL PO PJN PiS Palikot
0
50
100
150
200
250
300
350
400
450
SLD PSL PO PJN PiS Palikot
Positive Negative
Sentiment analysis in Social Media (blogs)

17. Negative content
Data gathered: 1.03-31.10.2011
-140
-120
-100
-80
-60
-40
-20
0
20
40
60
marzec kwiecień maj czerwiec lipiec sierpień wrzesień
PO PIS

18. Research Results
• This study confirms that content from Social Media is valuable source of
information reflecting political preferences of internet users, which finds
its expression in voting for candidates from certain political party during
the election.
• Thanks to that analysis there was possible to predict names of political
parties which dominated the parliament after the election and predicted
that a new party would get to the parliament for the first time. Results
show that the research hypothesis was generally proven.
• This research confirmed a method supporting the diagnosis of the
condition and dynamics of changes of parties. Therefore it can be used to
influence democratic processes with the use of Social Media.

19. Next Steps
Next step are:
• Importance of content generated by Social Media users leads its way to
even deeper analysis an research including advancement in sentiment
analysis
• Deeper social network analysis (who are the users, how they behave
online, what are factors that influence them the most)
• Extention of the method for different fields (stock exchange, complex
social processes)

20. Thank you
Danke Schön
Paweł Kuczma, Włodzimierz Gogołek
p.kuczma@id.uw.edu.pl