Mapping auroral activity with Twitter
Twitter is a popular, publicly accessible, social media service that has proven useful in mapping large‐scale events in real time. In this study, for the first time, the use of Twitter as a measure of auroral activity is investigated. Peaks in the number of aurora‐related tweets are found to frequen...
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| Veröffentlicht in: | Geophysical research letters 2015-05, Vol.42 (10), p.3668-3676 |
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| container_title | Geophysical research letters |
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| creator | Case, N. A. MacDonald, E. A. Heavner, M. Tapia, A. H. Lalone, N. |
| description | Twitter is a popular, publicly accessible, social media service that has proven useful in mapping large‐scale events in real time. In this study, for the first time, the use of Twitter as a measure of auroral activity is investigated. Peaks in the number of aurora‐related tweets are found to frequently coincide with geomagnetic disturbances (detection rate of 91%). Additionally, the number of daily aurora‐related tweets is found to strongly correlate with several auroral strength proxies (ravg≈0.7). An examination is made of the bias for location and time of day within Twitter data, and a first‐order correction of these effects is presented. Overall, the results suggest that Twitter can provide both specific details about an individual aurora and accurate real‐time indication of when, and even from where, an aurora is visible.
Key Points
Aurora can be mapped in real time using Twitter
Individual tweets can be combined to provide statistically useful data
Number of tweets can be normalized to provide accurate overall levels |
| doi_str_mv | 10.1002/2015GL063709 |
| format | Article |
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Key Points
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Individual tweets can be combined to provide statistically useful data
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Key Points
Aurora can be mapped in real time using Twitter
Individual tweets can be combined to provide statistically useful data
Number of tweets can be normalized to provide accurate overall levels</description><subject>Accessibility</subject><subject>aurora</subject><subject>Auroral activity</subject><subject>Auroras</subject><subject>Bias</subject><subject>citizen science</subject><subject>Correlation</subject><subject>Data</subject><subject>Detection</subject><subject>Digital media</subject><subject>Disturbances</subject><subject>Geomagnetic disturbances</subject><subject>Geomagnetism</subject><subject>Indication</subject><subject>Mapping</subject><subject>Real time</subject><subject>Social media</subject><subject>Social networks</subject><subject>Strength</subject><subject>Time measurement</subject><subject>Time of use</subject><subject>Twitter</subject><issn>0094-8276</issn><issn>1944-8007</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqN0U9LwzAYBvAgCs7pzQ9QEMGD1TdJmz9HGVqFOnFMBC8ha1Pt7NaatM59eyMVEQ_DS94cfk_Iw4vQIYYzDEDOCeA4SYFRDnILDbCMolAA8G00AJD-TjjbRXvOzQGAAsUDdHyrm6ZcPge6s7XVVaCztnwv23WwKtuXYOrP1th9tFPoypmD7zlED1eX09F1mN4lN6OLNMxikCQsDNFACpNxUegZz7EkUksca0KZoSzjRM-IkbmYcZOTSHLMopjmpNAsjzIt6BCd9O82tn7rjGvVonSZqSq9NHXnFPbFeCQiX_EfNIY4EkJ6evSHzuvOLn0RhSUGIbj_30bFhPQQS-rVaa8yWztnTaEaWy60XSsM6msJ6vcSPCc9X5WVWW-0KpmkMZGU-FDYh0rXmo-fkLavinHKY_U4TtT9ZPw0lTBRmH4Cl8mTmQ</recordid><startdate>20150528</startdate><enddate>20150528</enddate><creator>Case, N. 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H. ; Lalone, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5092-fe2a02fec78fab7d1929a915a236e36c72ab2e9d8b7ed249716453d2fa6d4ca83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Accessibility</topic><topic>aurora</topic><topic>Auroral activity</topic><topic>Auroras</topic><topic>Bias</topic><topic>citizen science</topic><topic>Correlation</topic><topic>Data</topic><topic>Detection</topic><topic>Digital media</topic><topic>Disturbances</topic><topic>Geomagnetic disturbances</topic><topic>Geomagnetism</topic><topic>Indication</topic><topic>Mapping</topic><topic>Real time</topic><topic>Social media</topic><topic>Social networks</topic><topic>Strength</topic><topic>Time measurement</topic><topic>Time of use</topic><topic>Twitter</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Case, N. 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Additionally, the number of daily aurora‐related tweets is found to strongly correlate with several auroral strength proxies (ravg≈0.7). An examination is made of the bias for location and time of day within Twitter data, and a first‐order correction of these effects is presented. Overall, the results suggest that Twitter can provide both specific details about an individual aurora and accurate real‐time indication of when, and even from where, an aurora is visible.
Key Points
Aurora can be mapped in real time using Twitter
Individual tweets can be combined to provide statistically useful data
Number of tweets can be normalized to provide accurate overall levels</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2015GL063709</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-0692-1778</orcidid><oa>free_for_read</oa></addata></record> |
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| issn | 0094-8276 1944-8007 |
| language | eng |
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| source | Wiley Free Content; Wiley-Blackwell AGU Digital Library; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Accessibility aurora Auroral activity Auroras Bias citizen science Correlation Data Detection Digital media Disturbances Geomagnetic disturbances Geomagnetism Indication Mapping Real time Social media Social networks Strength Time measurement Time of use |
| title | Mapping auroral activity with Twitter |
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