What tweets and retweets on twitter can tell for the restaurant industry: A big-data approach

dc.contributor.advisor Liang (Rebecca) Tang
dc.contributor.author Wang, Xi
dc.contributor.department Apparel, Events and Hospitality Management
dc.date 2020-06-26T19:55:15.000
dc.date.accessioned 2020-06-30T03:21:55Z
dc.date.available 2020-06-30T03:21:55Z
dc.date.copyright Fri May 01 00:00:00 UTC 2020
dc.date.embargo 2020-06-23
dc.date.issued 2020-01-01
dc.description.abstract <p>In the Internet age, the sheer volume of information can be generated and disseminated through online user-generated content (UGC). Within the context of Twitter, the retweeting function is one of the key mechanisms, which enables the information diffusion process among users in the social network. Stimulated by this concern, the purpose of the current study was to investigate the effects of textual content including the sentiments, emotions, and language style matching (LSM) of Twitter, a series of statistical analyses are conducted to the Twitter dataset with around one million pieces of customer tweet information. The results indicated that sentiments, emotions, and LSM have significant influences on customer retweeting behavior. Besides, significant differences were identified of both sentiments and emotions based on both six periods of the timeline analysis and the geographic distance at the city level, state level, and nationwide level. Discussions and implications interpreted the significance of the most valuable findings and suggested some important insights to both academia and industry.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/17934/
dc.identifier.articleid 8941
dc.identifier.contextkey 18242514
dc.identifier.doi https://doi.org/10.31274/etd-20200624-113
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/17934
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/32117
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/17934/Wang_iastate_0097E_18710.pdf|||Fri Jan 14 21:31:21 UTC 2022
dc.subject.keywords emotion
dc.subject.keywords language style matching
dc.subject.keywords restaurant
dc.subject.keywords retweeting
dc.subject.keywords Twitter
dc.title What tweets and retweets on twitter can tell for the restaurant industry: A big-data approach
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 5960a20b-38e3-465c-a204-b47fdce6f6f2
thesis.degree.discipline Hospitality Management
thesis.degree.level thesis
thesis.degree.name Doctor of Philosophy
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