This document summarizes a study that analyzed Swedish alternative media and regular media to detect xenophobic narratives. It found that alternative media used narratives depicting elite, minority and people groups more frequently than regular media. Alternative media also exhibited higher levels of negative emotions and third person plural pronouns compared to regular media. The study demonstrated that computerized text analysis methods like LIWC can help identify biased, stereotypical or extreme narratives in media sources.

1. Automatic detection of xenophobic narratives:
A case study on Swedish alternative media
1Amendra Shrestha
1 Lisa Kaati 2 Katie Cohen 2Sinna Lindquist
1Uppsala University
2Swedish Defence Research Agency (FOI)
September 28, 2016

2. Outline Introduction Method Result Summary
1 Introduction
2 Method
Narratives and Sterotypes
LIWC
3 Result
4 Summary
- 1 -

3. Outline Introduction Method Result Summary
Alternative Immigration Critic Media
• Free and independent news sites
• Clear focus on politics & society issues, crime & wars and
conflicts
• More critical and negative in tone
• Negative sentiment
- 2 -

4. Outline Introduction Method Result Summary
Example of alternative media
- 3 -

5. Outline Introduction Method Result Summary
Goal
• Investigate to what extend immigration critic alternative
media reproduce far-right narratives
• Compare immigration critic alternative media with regular
media
- 4 -

6. Outline Introduction Method Result Summary
Narratives and Sterotypes
Expo Foundation
- 5 -
• Swedish anti-racist magazine started in 1995
• Investigative journalism focused on nationalist, racist,
anti-democratic, anti-semitic, and far-right movements and
organisations
• No connections with specific organisations or political parties

7. Outline Introduction Method Result Summary
Narratives and Sterotypes
Narratives and Stereotypes
Table: The mixed stereotype content model
Competence
Low High
High
Paternalistic stereotype
low status, not competitive
(eg., housewives, elderly people,
disabled people)
Admiration
high status, not competitive
(eg., ingroup, close allies)
Warmth
Low
Contemptuous stereotype
low status, competitive
(eg., welfare recipients, poor
people)
Envious stereotype
high status, competitive
(eg., Asians, Jews, rich people,
feminists)
- 6 -
• Explains how prejudices are based on relations of competition and
status
• Competence : do not compete with the ingroup for same resources
• Warmth : high in status

8. Outline Introduction Method Result Summary
Narratives and Sterotypes
- 7 -
• The Elite: national state-haters, liberal politicians
• The Minority: welfare-tourist, immigrants
• The People: real people, Swedish workers, elderly, children

9. Outline Introduction Method Result Summary
LIWC
LIWC
- 8 -
• Linguistic Inquiry and Word Count

10. Outline Introduction Method Result Summary
LIWC
- 9 -
Psychologist User

11. Outline Introduction Method Result Summary
LIWC
- 10 -

12. Outline Introduction Method Result Summary
LIWC
- 11 -

13. Outline Introduction Method Result Summary
LIWC
- 12 -

14. Outline Introduction Method Result Summary
LIWC
LIWC Dimensions
- 13 -

15. Outline Introduction Method Result Summary
LIWC
LIWC can be used to
• Make large amounts of text easily available for quick analysis
• Observe personality traits and psychological states
• Observe social status and relations between individuals
• Assess the cognitive complexity of argumentation
• Can be used on group- or individual level
- 14 -

16. Outline Introduction Method Result Summary
LIWC
We use LIWC to
• emotional tone
• pronoun use
- 15 -

17. Outline Introduction Method Result Summary
LIWC
Expression of emotions
• Natural conversation contains almost twice as many positive
than negative emotion words
• Previous work on al-Qaida texts showed a higher relative
degree of negative emotion words, mostly anger
- 16 -

18. Outline Introduction Method Result Summary
LIWC
3rd person plural
• Frequent use of third person plural (they, them etc) in a
group suggests that the group is defining itself to a large
degree by the existence of an oppositional group
• A strong indicator of negative identification with an outgroup
is a precursor of terrorism
- 17 -

19. Outline Introduction Method Result Summary
LIWC
Example
Table: The different categories we focus on our analysis and some
example words
Categories Example
Language variables
3rd person plural they, their, them
Negative Emotions hate, worthless, enemy, hurt
Narratives
Elite race mixers, anti-swedes
Minority luxury immigrants, occupants
People nation, people of reality, Swedes
- 18 -

20. Outline Introduction Method Result Summary
Dataset
• Alternative Media
• Nordfront (619 articles)
• Avpixlat (4391 articles)
• Exponerat (6239 articles)
• Fria tider (4856 articles)
• Nyheter idag (1400 articles)
• Samtiden (2632 articles)
• Regular Media
• DN (1747 articles)
• Aftonbladet (613 articles)
- 19 -

21. Outline Introduction Method Result Summary
Result : Narratives
Nordfront
Avpixlat
Exponerat
Friatider
Nyheteridag
Samtiden
DN
Aftonbladet
0%
0.02%
0.04%
0.06%
News
Value
The Elite The People The Minority
Figure: The use of narratives
- 20 -

22. Outline Introduction Method Result Summary
Result : Negative Emotions
Nordfront
Avpixlat
Exponerat
Friatider
Nyheteridag
Samtiden
DN
Aftonbladet
1%
1.1%
1.2%
1.3%
1.4%
News
Value
Figure: The use of negative emotions
- 21 -

23. Outline Introduction Method Result Summary
Result : 3rd Person Plural
Nordfront
Avpixlat
Exponerat
Friatider
Nyheteridag
Samtiden
DN
Aftonbladet
1%
1.1%
1.2%
1.3%
News
Value
Figure: The use of third person plural
- 22 -

24. Outline Introduction Method Result Summary
Significant test
Table: Significance test for alternative media compared with
non-alternative medias
Category p-value Significant
The Elite 7.809190e-13 Yes
The People 4.388677e-03 Yes
The Minority 8.649609e-26 Yes
Negative emotion 2.683428e-03 Yes
Third person plural 7.961904e-03 Yes
- 23 -

25. Outline Introduction Method Result Summary
Significant test
Table: Significance test for Aftonbladet compared with alternative medias
Avpixlat Exponerat Fria tider Nordfront Nyheter idag Samtiden
Eliten * * - * - -
The People - - - - - -
The Minority * * * * - *
Negative emotion - - * - - -
Third person plural - - - - - -
- 24 -
* = significant difference with p < 0.05

26. Outline Introduction Method Result Summary
Significant test
Table: Significance test for DN compared with alternative medias
Avpixlat Exponerat Fria tider Nordfront Nyheter idag Samtiden
Eliten * * * * - *
The People * * - - - *
The Minority * * - * - *
Negative emotion * * * - - -
Third person plural * * * - * *
- 25 -
* = significant difference with p < 0.05

27. Outline Introduction Method Result Summary
Future Work
• Use of machine learning to detect narratives
• Able to capture irony or metaphors
• Dynamic dictionaries and independent
- 26 -

28. Outline Introduction Method Result Summary
Summary
• Alternative media use narrative words
• Alternative media has high frequency of negative emotion and
third person plural
• Possible to use computerised text analysis method to find
bias, stereotypes or extremism
- 27 -

29. Outline Introduction Method Result Summary
- 28 -
Thank You