The linguistic features driving information diffusion on Twitter: The case of #RevolutionNow in Nigeria

dc.contributor.committeeChairKee, Kerk F.
dc.contributor.committeeMemberZhang, Weiwu
dc.contributor.committeeMemberGlen, Cummins R.
dc.creatorOkunloye, Oluwabusayo Seyi
dc.creator.orcid0000-0002-8490-8205
dc.date.accessioned2021-01-26T19:59:15Z
dc.date.available2021-01-26T19:59:15Z
dc.date.created2020-12
dc.date.issued2020-12
dc.date.submittedDecember 2020
dc.date.updated2021-01-26T19:59:15Z
dc.description.abstractPrior research investigating political activism on social media concluded that that the number of followers, importance of a matter, and frames of a movement are factors that drive information diffusion. However, little research attention has been given to the linguistic features of highly diffused messages, although social media by nature facilitates information diffusion via messages. Therefore, this study utilizes Linguistic Word Count (LIWC) along with SPSS to explore the linguistic nature of words that drive diffusion in the case of #RevolutionNow in Nigeria between July 31, 2019 and August 29, 2019. This study also addresses the limitations of prior research that have measured diffusion on Twitter and/or social media simply in terms of shares and retweets. In this study, diffusion is measured in terms of retweets, likes, and replies that a tweet attracts. This study will yield practical strategies for future political movements on social media.
dc.description.abstractEmbargo status: Restricted to TTU only. TTU community may view by logging in with their eRaider (top right). Others may request access by clicking on the PDF link to the left.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2346/86780
dc.language.isoeng
dc.rights.availabilityRestricted to TTU only. For access, please log in at the top of this page using your eRaider credentials.
dc.subjectTwitter
dc.subjectSocial media
dc.subjectPolitical activism
dc.subjectHashtags
dc.subjectProtests
dc.subjectDiffusion of innovations
dc.subjectInformation diffusion
dc.subjectLinguistic Inquiry and Word Count (LIWC)
dc.titleThe linguistic features driving information diffusion on Twitter: The case of #RevolutionNow in Nigeria
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMass Communications
thesis.degree.disciplineMass Communications
thesis.degree.grantorTexas Tech University
thesis.degree.levelMasters
thesis.degree.nameMaster of Arts

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
OKUNLOYE-THESIS-2020.pdf
Size:
501.97 KB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.85 KB
Format:
Plain Text
Description: