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#MakeSwedenGreatAgain: Media events as politics in the deterritorialised nationalism debate

Jessica Yarin Robinson
Jessica Yarin Robinson
 e 
Gunn Enli
Gunn Enli
   | 07 mag 2022
Nordic Journal of Media Studies's Cover Image
Nordic Journal of Media Studies
Volume 4 (2022): Numero 1 (June 2022)
Media Events in the Age of Global, Digital Media
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Pubblicato online: 07 mag 2022
Pagine: 56 - 80
DOI: https://doi.org/10.2478/njms-2022-0004
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© 2022 Jessica Yarin Robinson et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Network map, full English-language collectionComments: Users are coloured according to subnetwork ID. The users retweeted and @mentioned the most by other users have been labelled. Generated in Gephi using ForceAtlas 2 (nusers = 88,525; ntweets = 173,678). 24,957 tweets from the data collection could not be included because they do not @mention or retweet another user and thus have no network information.
Network map, full English-language collectionComments: Users are coloured according to subnetwork ID. The users retweeted and @mentioned the most by other users have been labelled. Generated in Gephi using ForceAtlas 2 (nusers = 88,525; ntweets = 173,678). 24,957 tweets from the data collection could not be included because they do not @mention or retweet another user and thus have no network information.

Figure 2

Results of content analyses, English-language samples (per cent)Comments: The PopRand sample is a random sample of the entire English-language collection. The non-RT sample is a random sample of original tweets in the English-language collection (nPopRand = 5,000; nnon-RT = 1,000). The graph helps show the themes that individuals tweeted about (non-RT sample) versus what themes were amplified through networks (PopRand sample)
Results of content analyses, English-language samples (per cent)Comments: The PopRand sample is a random sample of the entire English-language collection. The non-RT sample is a random sample of original tweets in the English-language collection (nPopRand = 5,000; nnon-RT = 1,000). The graph helps show the themes that individuals tweeted about (non-RT sample) versus what themes were amplified through networks (PopRand sample)

Figure 3

Themes co-occurrence, PopRand sampleComments: Generated in Gephi using ForceAtlas 2 (ntweets = 4,741; nthemes = 15; ncodings = 8,109). Tweets associated with the “Other” category have been excluded.
Themes co-occurrence, PopRand sampleComments: Generated in Gephi using ForceAtlas 2 (ntweets = 4,741; nthemes = 15; ncodings = 8,109). Tweets associated with the “Other” category have been excluded.

Figure 4

Themes co-occurrence, PopRand sample (according to subnetwork)Comments: Generated in Gephi using ForceAtlas 2 (ntweets = 4,741; nthemes = 15; ncodings = 8,109). Tweets are coloured according to the subnetwork ID of the user: pink for tweets from the far-right subnetwork; green for tweets from mainstream subnetwork; and blue for tweets from the British subnetwork users. Tweets in yellow are from other subnetworks. (See the full network map in Figure 1.)
Themes co-occurrence, PopRand sample (according to subnetwork)Comments: Generated in Gephi using ForceAtlas 2 (ntweets = 4,741; nthemes = 15; ncodings = 8,109). Tweets are coloured according to the subnetwork ID of the user: pink for tweets from the far-right subnetwork; green for tweets from mainstream subnetwork; and blue for tweets from the British subnetwork users. Tweets in yellow are from other subnetworks. (See the full network map in Figure 1.)

Figure 5

Timeline of hourly tweeting, full English- and Swedish-language collectionsComments: Temporal/volume comparison of Swedish-language tweets (top) and English-language tweets (bottom). Grey lines mark live televised debates and election day (nSwedish = 221,686; nEnglish = 198,635).
Timeline of hourly tweeting, full English- and Swedish-language collectionsComments: Temporal/volume comparison of Swedish-language tweets (top) and English-language tweets (bottom). Grey lines mark live televised debates and election day (nSwedish = 221,686; nEnglish = 198,635).

Coding scheme

No. Theme Description Intercoder reliability Occurrence
1 NationalistRise The tweet puts emphasis on the success or expected success of SD in the election. .820 1,256
2 Horserace The tweet puts emphasis on updates of who is winning and losing, including poll results, voter turnout, results of the election, and updates on government formation. .837 1,422
3 Violence The tweet puts emphasis on reports of violence, threats of violence, rape, terrorism, or other violent crime. .784 498
4 HistoricUpheaval The tweet puts emphasis on the historic nature of the election or the permanent mark it will leave on Sweden .678 373
5 Migration The tweet puts emphasis on immigration policy, (im)migrants, refugees, Islam (as implicit to migration in Sweden), or multiculturalism. .801 1,431
6 DebateDistortion The tweet puts emphasis on external factors: Russian or other foreign interference, fake news, or platforms manipulating content. .949 321
7 ElectoralFailure The tweet puts emphasis on internal factors: voter fraud, public corruption, unfair treatment of parties, unfair voting rules, and other institutional failures that would impact the results. .959 415
8 GlobalPolitics The tweet puts emphasis on a relationship between the Swedish election and politics in other places (e.g., Europe, the UK, the EU, the West, the world). .795 665
9 WelfareState The tweet puts emphasis on Sweden's welfare state, including taxes and welfare benefits. .887 61
10 UtopiaDystopia The tweet puts emphasis on Sweden as a model leftist, progressive, socialist, or social democratic country. This may be in a positive or negative light. .660 188
11 Counternarrative The tweet puts emphasis on the idea that the media or dominant narrative sensationalises, exaggerates, or ignores some aspect of the election. .818 609
12 Environment The tweet puts emphasis on climate change, wildfires, or other environmental issue. 1.00 32
13 Racism The tweet puts emphasis on racism in Swedish politics, including referring to a party as Nazi or having Nazi roots. Note that this does not refer to tweets that express racist views themselves. .764 336
14 Rooting The tweet puts emphasis on personal support for a political “team”, including encouraging voter turnout (before the election) or expressing celebration or disappointment about the result (after the election). .752 467
15 Financial The tweet puts emphasis on the election's impact or potential impact on global markets, investments, the SEK, etc. .830 35
16 Other Emphasis of tweet not captured by the above categories. This includes tweets that are not about the election at all, are apolitical jokes about politicians, are not in English, or are unintelligible. .700 259

Jaccard Index – measure of overlap between themes (PopRand sample)

Theme Total tweets Counternarrative DebateDistortion ElectoralFailure Environment Financial GlobalPolitics HistoricUpheaval Horserace Migration NationalistRise Other Racism Rooting UtopiaDystopia Violence WelfareState
Total tweets – 609 321 415 32 35 665 373 1,422 1,431 1,256 259 336 467 188 498 61
Counternarrative 609 – 0.004 0.005 0.000 0.002 0.111 0.002 0.099 0.066 0.059 0.000 0.051 0.013 0.005 0.020 0.009
Debate Distortion 321 0.004 – 0.041 0.000 0.000 0.038 0.001 0.000 0.005 0.005 0.000 0.011 0.003 0.004 0.006 0.000
ElectoralFailure 415 0.005 0.041 – 0.000 0.000 0.006 0.003 0.002 0.018 0.006 0.000 0.000 0.008 0.007 0.025 0.000
Environment 32 0.000 0.000 0.000 – 0.000 0.006 0.000 0.000 0.007 0.000 0.000 0.003 0.014 0.005 0.004 0.069
Financial 35 0.002 0.000 0.000 0.000 – 0.004 0.000 0.003 0.001 0.005 0.000 0.000 0.000 0.005 0.000 0.021
GlobalPolitics 665 0.111 0.038 0.006 0.006 0.004 – 0.023 0.027 0.084 0.091 0.000 0.051 0.068 0.017 0.007 0.013
HistoricUpheaval 373 0.002 0.001 0.003 0.000 0.000 0.023 – 0.044 0.107 0.191 0.000 0.086 0.065 0.214 0.093 0.021
Horserace 1,422 0.099 0.000 0.002 0.000 0.003 0.027 0.044 – 0.097 0.148 0.000 0.016 0.020 0.004 0.015 0.001
Migration 1,431 0.066 0.005 0.018 0.007 0.001 0.084 0.107 0.097 – 0.242 0.000 0.026 0.109 0.067 0.214 0.019
NationalistRise 1,256 0.059 0.005 0.006 0.000 0.005 0.091 0.191 0.148 0.242 – 0.000 0.118 0.039 0.113 0.074 0.020
Other 259 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 – 0.000 0.000 0.000 0.000 0.000
Racism 336 0.051 0.011 0.000 0.003 0.000 0.051 0.086 0.016 0.026 0.118 0.000 – 0.009 0.076 0.008 0.000
Rooting 467 0.013 0.003 0.008 0.014 0.000 0.068 0.065 0.020 0.109 0.039 0.000 0.009 – 0.008 0.094 0.004
UtopiaDystopia 188 0.005 0.004 0.007 0.005 0.005 0.017 0.214 0.004 0.067 0.113 0.000 0.076 0.008 – 0.112 0.020
Violence 498 0.020 0.006 0.025 0.004 0.000 0.007 0.093 0.015 0.214 0.074 0.000 0.008 0.094 0.112 – 0.015
WelfareState 61 0.009 0.000 0.000 0.069 0.021 0.013 0.021 0.001 0.019 0.020 0.000 0.000 0.004 0.020 0.015 –
eISSN:
2003-184X
Lingua:
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Social Sciences, Communication Science, Mass Communication, Public and Political Communication, Visual Communication
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