2017
DOI: 10.1007/978-3-319-55723-6_13
|View full text |Cite
|
Sign up to set email alerts
|

The Classification and Visualization of Twitter Trending Topics Considering Time Series Variation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2020
2020
2021
2021

Publication Types

Select...
1
1

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 3 publications
0
2
0
Order By: Relevance
“…Given this complexity, multimodal semiotic analysis has broad potential for application in theoretical and methodological studies and may be susceptible to mathematical modeling and analysis methods, as applied herein. The present analysis thus includes the specific elements of tweets, i.e., hashtag and keywords (Uzunoğlu; Türkel; Yaman-Akyar, 2017; Scott, 2018), which are examined by means of text mining techniques and statistical linguistic analysis (Baayen, 2008;Gries, 2013;Nakayama, 2017;Baier;Frost, 2018) with the addition of semantic clustering of hashtags (Javed;Lee, 2018). Considering the energy sector, Twitter allows the extensive use of facts and symbols that are communicated using carefully chosen keywords and hashtags, thereby creating trending conversations that are focused on CSR issues (Etter; Fieseler, 2010; Etter, 2013; Uzunoğlu; Türkel; Yaman-Akyar, 2017).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Given this complexity, multimodal semiotic analysis has broad potential for application in theoretical and methodological studies and may be susceptible to mathematical modeling and analysis methods, as applied herein. The present analysis thus includes the specific elements of tweets, i.e., hashtag and keywords (Uzunoğlu; Türkel; Yaman-Akyar, 2017; Scott, 2018), which are examined by means of text mining techniques and statistical linguistic analysis (Baayen, 2008;Gries, 2013;Nakayama, 2017;Baier;Frost, 2018) with the addition of semantic clustering of hashtags (Javed;Lee, 2018). Considering the energy sector, Twitter allows the extensive use of facts and symbols that are communicated using carefully chosen keywords and hashtags, thereby creating trending conversations that are focused on CSR issues (Etter; Fieseler, 2010; Etter, 2013; Uzunoğlu; Türkel; Yaman-Akyar, 2017).…”
Section: Methodsmentioning
confidence: 99%
“…The present study addresses this gap by departing from the traditional research focus on media news coverage of CSR information from companies (Lunenberg; Gosselt; De-Jong, 2016). Rather, it focuses on new research trends regarding messages transmitted to stakeholders via social media (Abitbol; Lee, 2017; Uzunoğlu; Türkel; Yaman-Akyar, 2017), from an interactive and discourse perspective (Nakayama, 2017) To what degree does this discourse applied in their PR strategies represent social and environmental engagement of companies in the energy sector via their CSR communications on Twitter?…”
Section: Literature Reviewmentioning
confidence: 99%