Gradients of Fear and Anger in the Social Media Response to Terrorism

Simple item page
dc.contributor.authorBaucum, Matthew
dc.contributor.authorJohn, Richard
dc.date.accessioned2019-01-03T00:02:31Z
dc.date.available2019-01-03T00:02:31Z
dc.date.issued2019-01-08
dc.description.abstractResearch suggests that public fear and anger in wake of a terror attack can each uniquely contribute to policy attitudes and risk-avoidance behaviors. Given the importance of these negative-valanced emotions, there is value in studying how terror events can incite fear and anger at various times and locations relative to an attack. We analyze 36,259 Twitter posts authored in response to the 2016 Orlando nightclub shooting and examined how fear- and anger-related language varied with time and distance from the attack. Fear-related words sharply decreased over time, though the trend was strongest at locations near the attack, while anger-related words slightly decreased over time and increased with distance from Orlando. Comparing these results to users’ pre-attack emotional language suggested that distant users remained both angry and fearful after the shooting, while users close to the attack remained angry but quickly reduced expressions of fear to pre-attack levels.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.276
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59667
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectDigital and Social Media
dc.subjectemotion, social media, terrorism, text mining
dc.titleGradients of Fear and Anger in the Social Media Response to Terrorism
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
0227.pdf
Size:
571.89 KB
Format:
Adobe Portable Document Format
Download

Collections

Data Analytics, Data Mining and Machine Learning for Social Media