Twitter Emotions in Austria

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Twitter Emotions

David Garcia, Jana Lasser, Hannah Metzler and Max Pellert from the Computational Social Science Group at the Complexity Science Hub
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Methods & Results

Vertical lines indicate the following events in Austria:

  • Dotted: Terrorist attack in Hanau on 2020-02-25
  • Dotdashed: First Covid-19 case on 2020-02-25
  • Dashed: First death from Covid-19 on 2020-03-12
  • Twodash: The first press conference on Covid-19, announcing bans of large public events and university closures as first measures, on 2020-03-10
  • Longdash: Strict social distancing measures announced on 2020-03-15, starting on the day after
  • Dotdashed: Press releases about considerations of public agencies to stir anxiety of the population at the begin of the pandemic
  • Dotted: Black lives matter: Demonstrations against police brutality on 2020-05-31
  • Dotdashed: Terrorist attack in Vienna on 2020-11-02

Results

  • Our analysis of tweets from users in Austria show increases of expressions of anxiety and sadness, but also positive emotions, and prosocial and social terms. Anger is the only emotion that has decreased.
  • The extent of the change is different for different emotions and behaviors:
    • The increase is strongest for tweets expressing anxiety, fear and worries. The highest peak corresponds to an increase of 50% compared to the average in 2019.
    • The decrease for anger is also quite strong, with the lowest level representing about a 35% decrease compared to 2019.
    • Sadness has also considerably increased with peaks of above 20%, and an increasing trend during the last week.
    • Prosocial terms (willingness to help, cooperate, volunteer, empathy, ā€¦) have shown increases of more than 15%.
    • Changes are smallest for social terms and positive emotions, which both show an increase of 5% compared to 2019.
  • For most emotions and behaviors, similarly strong changes have been observed before since the beginning of 2019. Prosocial and social terms, however, are clearly higher than they have been since the beginning of 2019.
  • What is exceptional for all emotions is the time for which these high levels remain stable: All of them have not returned to the average level for about a month in a row since they first increased.
  • The changes began at different moments in time for different emotions and behaviors:
    • Anxiety, prosocial and social terms started increasing shortly after the first death occured.
    • Changes in anger, sadness and positive emotions coincided with the onset of the social distancing measures.

In summary, our results suggest that people are expressing more anxiety since the outbreak of Covid-19, but also increasingly talk about cooperation, supporting each other and express empathy for people who are in some way concerned by Covid-19. The decrease in anger may suggest that people were relatively satisfied with the first measures. At the same time, however, they also became increasingly sad, possibly anticipating the rather lonely times ahead. The smaller increase in positive emotions may indicate that they also try to focus on positive aspects of the crisis.

Methods

  • We analysed tweets from users located in Austria by Crimson Hexagon. Only original tweets (no retweets) in German and from users with a number of followers between 100 and 50.000 (to remove spam and media) were included.
  • The figures below show the difference in the proportion of tweets expressing each type of emotion or behavior in comparison to the average proportion of such tweets in 2019 (i.e.Ā the baseline). Taking into account the baseline level makes emotion levels comparable across countries, and shows how much emotions change from their usual level expressed in percent. A value of zero corresponds to the average level in 2019, and everything above or below to an increase or decrease in comparison to this level.
  • For anxiety, anger, sadness, positive emotions and social words, tweet text is matched for terms from the LIWC lexicon (version from 2007). LIWC is a standard methodology in psychology for text analysis that includes validated lexica in German.
  • Similarly, tweet text was matched to a translated list of prosocial terms used in previous research, including for example words related to helping, empathy, cooperating, sharing, volunteering, and donating. We excluded words referring to (1) health (e.g.Ā Heilverfahren, Behandlung, Behandlungen), (2) service (Dienst*, mostly comments about the public services and availability of other services during the Corona-crisis), (3) sharing words (teilen, mitteilen). We did this to avoid confounds with non-prosocial tweets about Covid-19 related health- and other services, and about sharing of links/tweets/videos, etc.
  • Marked dates during the Covid-19 outbreak are taken from Wikipedia.

derstandard.at

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Postings at derstandard.at livetickers

These plots were created in the same way as the Twitter Plots, except that we average the frequency of terms per posting instead of the number of postings containing the terms to account for varying lengths of postings.
By moving the mouse over a data point, additional information (including the number of observations ā€œNā€) is displayed. Clicking on a line in the legend, removes the line from the plot. Double-clicking on a line in the legend, isolates that line.
Lines are smoothed using a 7 day moving average.
Contributors: David Garcia, Jana Lasser, Hannah Metzler and Max Pellert from the Computational Social Science Group at the Complexity Science Hub
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