| dc.contributor.author | Ziegler, John | |
| dc.contributor.author | Gertz, Michael | |
| dc.contributor.editor | König-Ries, Birgitta | |
| dc.contributor.editor | Scherzinger, Stefanie | |
| dc.contributor.editor | Lehner, Wolfgang | |
| dc.contributor.editor | Vossen, Gottfried | |
| dc.date.accessioned | 2023-02-23T13:59:47Z | |
| dc.date.available | 2023-02-23T13:59:47Z | |
| dc.date.issued | 2023 | |
| dc.identifier.isbn | 978-3-88579-725-8 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/40321 | |
| dc.description.abstract | The focus of social media is characterized by stories about short-lived breaking news. Often, such mayflies make it hard to keep track of more profound topics that are prevalent over a longer period of time. To tackle this issue we present a method to detect such long-term trends based on temporal networks and community evolution. Connecting those methods with that of trend analysis allows to study the temporal development of trends" | en |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BTW 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-331 | |
| dc.subject | Social Media Analytics | |
| dc.subject | Temporal Networks | |
| dc.subject | Trend Analysis | |
| dc.subject | Twitter Data | |
| dc.title | No Mayfly: Detection and Analysis of Long-term Twitter Trends | en |
| dc.type | Text/Conference Paper | |
| dc.identifier.doi | 10.18420/BTW2023-17 | |
| gi.conference.location | Dresden, Germany | |
| gi.conference.date | 06.-10. März 2023 | |
| gi.citation.startPage | 353 | |
| gi.citation.endPage | 364 | |
| gi.citation.publisherPlace | Bonn | |
