Table 1
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 |
[i] Comments: The intercoder reliability statistic used is Cohen's Kappa (κ). This score was calculated on 800 tweets coded by both authors in the PopRand sample (n = 5,000).
Figure 1
Network map, full English-language collection
Comments: 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)
Figure 3
Themes co-occurrence, PopRand sample
Comments: 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.)
Figure 5
Timeline of hourly tweeting, full English- and Swedish-language collections
Comments: 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).
Table A1
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 | – |
[i] Comments: The highest overlap is between NationalistRise and Migration, closely followed by Violence and Migration, as seen in the bold figures.