Article:
Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter

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dc.creatorMurthy, Dhiraj
dc.creatorGross, Alexander
dc.creatorMcGarry, Marisa
dc.date.accessioned2018-09-25T15:13:20Z
dc.date.available2018-09-25T15:13:20Z
dc.date.issued2016
dc.description.abstractSocial media such as Twitter and Instagram are fast, free, and multicast. These attributes make them particularly useful for crisis communication. However, the speed and volume also make them challenging to study. Historically, journalists controlled what/how images represented crises. Large volumes of social media can change the politics of representing disasters. However, methodologically, it is challenging to study visual social media data. Specifically, the process is usually labour-intensive, using human coding of images to discern themes and subjects. For this reason, Studies investigating social media during crises tend to examine text. In addition, application programming interfaces (APIs) for visual social media services such as Instagram and Snapchat are restrictive or even non-existent. Our work uses images posted by Instagram users on Twitter during Hurricane Sandy as a case study. This particular case is unique as it is perhaps the first US disaster where Instagram played a key role in how victims experienced Sandy. It is also the last major US disaster to take place before Instagram images were removed from Twitter feeds. Our sample consists of 11,964 Instagram images embedded into tweets during a twoweek timeline surrounding Hurricane Sandy. We found that the production and consumption of selfies, food/drink, pets, and humorous macro images highlight possible changes in the politics of representing disasters – a potential turn from top-down understandings of disasters to bottom-up, citizen informed views. Ultimately, we argue that image data produced during crises has potential value in helping us understand the social experience of disasters, but studying these types of data presents theoretical and methodological challenges.en
dc.identifier.doi10.14361/dcs-2016-0208
dc.identifier.doi10.25969/mediarep/1027
dc.identifier.urihttp://digicults.org/files/2018/01/Dhiraj-Murthy-Alexander-Gross-Marisa-McGarry_Visual-social-media.pdf
dc.identifier.urihttps://mediarep.org/handle/doc/3169
dc.languageeng
dc.publishertranscript
dc.publisher.placeBielefeld
dc.relation.isPartOfissn:2364-2114
dc.relation.ispartofseriesDigital Culture & Society
dc.rightsCreative Commons Attribution Non Commercial No Derivatives 4.0 Generic
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectInstagramde
dc.subjectTwitterde
dc.subjectSocial Mediade
dc.subjectFoto-Postingde
dc.subjectKrisenkommunikationde
dc.subjectHumorde
dc.subjectMassendatende
dc.subjectsocial mediaen
dc.subjectimage postingen
dc.subjectcrisis communicationen
dc.subjecthumouren
dc.subjectBig Dataen
dc.subject.ddcddc:304
dc.titleVisual Social Media and Big Data. Interpreting Instagram Images Posted on Twitterde
dc.typearticle
dc.type.statuspublishedVersion
dcterms.bibliographicCitationMurthy, Dhiraj; Gross, Alexander; McGarry, Marisa (2016): Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter. In: Digital Culture & Society 2 (2), S. 113–133. DOI: http://dx.doi.org/10.25969/mediarep/1027.
dspace.entity.typeArticleen
local.coverpage2020-12-17T07:29:02
local.identifier.firstpublishedhttps://doi.org/10.14361/dcs-2016-0208
local.source.epage133
local.source.issue2
local.source.spage113
local.source.volume2
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local.subject.gndhttps://d-nb.info/gnd/1064778283
local.subject.gndhttps://d-nb.info/gnd/7660487-1
local.subject.gndhttps://d-nb.info/gnd/4026170-0
local.subject.gndhttps://d-nb.info/gnd/4802620-7
local.subject.wikidatahttps://www.wikidata.org/wiki/Q202833
local.subject.wikidatahttps://www.wikidata.org/wiki/Q209330
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local.subject.wikidatahttps://www.wikidata.org/wiki/Q378874
local.subject.wikidatahttps://www.wikidata.org/wiki/Q35874
local.subject.wikidatahttps://www.wikidata.org/wiki/Q858810

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2(2) 2016: Politics of Big Data