Show simple item record

dc.contributor.authorZhao, Y
dc.contributor.authorWang, C
dc.contributor.authorHan, H
dc.contributor.authorvan den Heuvel, WJ
dc.contributor.authorChi, CH
dc.contributor.authorLi, W
dc.date.accessioned2020-04-02T00:49:29Z
dc.date.available2020-04-02T00:49:29Z
dc.date.issued2019
dc.identifier.isbn9783030352301
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-030-35231-8_26
dc.identifier.urihttp://hdl.handle.net/10072/392907
dc.description.abstractDespite the extensive research efforts in information diffusion, most previous studies focus on the speed and coverage of the diffused information in the network. A better understanding on the semantics of information diffusion can provide critical information for the domain-specific/socio-economic phenomenon studies based on diffused topics. More specifically, it still lacks (a) a comprehensive understanding of the multiplexity in the diffused topics, especially with respect to the temporal relations and inter-dependence between topic semantics; (b) the similarities and differences in these dimensions under different diffusion degrees. In this paper, the semantics of a topic is described by sentiment, controversy, content richness, hotness, and trend momentum. The multiplexity in the diffusion mechanisms is also considered, namely, hashtag cascade, url cascade, and retweet. Our study is conducted upon 840, 362 topics from about 42 million tweets during 2010.01–2010.10. The results show that the topics are not randomly distributed in the Twitter space, but exhibiting a unique pattern at each diffusion degree, with a significant correlation among content richness, hotness, and trend momentum. Moreover, under each diffusion mechanism, we also find the remarkable similarity among topics, especially when considering the shifting and scaling in both the temporal and amplitude scales of these dimensions.
dc.description.peerreviewedYes
dc.publisherSpringer
dc.relation.ispartofconferencename15th International Conference on Advanced Data Mining and Applications (ADMA 2019)
dc.relation.ispartofconferencetitleAdvanced Data Mining and Applications
dc.relation.ispartofdatefrom2019-11-21
dc.relation.ispartofdateto2019-11-23
dc.relation.ispartoflocationDalian, China
dc.relation.ispartofpagefrom359
dc.relation.ispartofpageto369
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.ispartofvolume11888
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleUnfolding the Mixed and Intertwined: A Multilevel View of Topic Evolution on Twitter
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationZhao, Y; Wang, C; Han, H; van den Heuvel, WJ; Chi, CH; Li, W, Unfolding the Mixed and Intertwined: A Multilevel View of Topic Evolution on Twitter, Advanced Data Mining and Applications, 2019, 11888, pp. 359-369
dc.date.updated2020-04-02T00:47:01Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record