Applying "negativity bias" to Twitter: negative news on Twitter, emotions, and political learning

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Applying "negativity bias" to Twitter: negative news on Twitter, emotions, and political learning
Authors: Chang Sup Park
Citation: Journal of Information Technology & Politics missing volume : missing pages. 2015
Database(s):
DOI: 10.1080/19331681.2015.1100225.
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Extract:

Applying "negativity bias" to Twitter: negative news on Twitter, emotions, and political learning reports on an online experiment with 420 South Korean voters. Three negative emotions were investigated: anger, fear and disgust.

[edit] Hypotheses

  1. "Highly negative news stories on Twitter will arouse stronger anger, fear, and disgust than weakly negative news stories on Twitter."
  2. "Highly negative news stories on Twitter will be better recalled than weakly negative news stories on Twitter"
  3. "Highly negative news stories on Twitter will trigger more active information-seekingbehavior than weakly negative news stories on Twitter."
  4. "How does age influence the relationship between exposure to negative news on Twitter and discrete emotions, recall, and information seeking?"

and several more.

[edit] Method

  • 40 news articles formatted as a Twitter post
  • 2-by-3 factorial design (news negativity by age group)
  • Subjects rated 10 chosen articles in a constructed newsfeed with respect to their level of anger, fear and disgust.

[edit] Results

  • News negativity has an effect on anger.
  • Some interaction results with age were also found and weaker effect on disgust.
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