Structuring and Exploring User Behavioral Patterns in Social Media Traces
| dc.contributor.author | Herder, Eelco | |
| dc.contributor.author | Roßner, Daniel | |
| dc.contributor.author | Atzenbeck, Claus | |
| dc.contributor.editor | Hansen, Christian | |
| dc.contributor.editor | Nürnberger, Andreas | |
| dc.contributor.editor | Preim, Bernhard | |
| dc.date.accessioned | 2020-08-18T15:19:49Z | |
| dc.date.available | 2020-08-18T15:19:49Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | User behavior and the resulting behavioral data forms the basis of personalized feeds, recommendations and advertisements in social networks such as Facebook. These platforms are now required to provide users with their personal data. However, these dumps with chronological data in different files do not provide users insight in overarching themes and connections in their online behavior. In this paper, we discuss the development and preliminary evaluation of an exploratory interface for visual data exploration. First insights include that the less obvious, more associative and obscure connections are more interesting and relevant to the user than very close semantic or temporal connections. | en |
| dc.identifier.doi | 10.18420/muc2020-ws111-343 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/33511 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Mensch und Computer 2020 - Workshopband | |
| dc.relation.ispartofseries | Mensch und Computer | |
| dc.subject | spatial hypertext | |
| dc.subject | social networks | |
| dc.subject | user profiles | |
| dc.subject | GDPR | |
| dc.subject | data exploration | |
| dc.subject | transparency | |
| dc.title | Structuring and Exploring User Behavioral Patterns in Social Media Traces | en |
| dc.type | Text/Workshop Paper | |
| gi.citation.publisherPlace | Bonn | |
| gi.conference.date | 6.-9. September 2020 | |
| gi.conference.location | Magdeburg | |
| gi.conference.sessiontitle | MCI-WS02: UCAI 2020: Workshop on User-Centered Artificial Intelligence | |
| gi.document.quality | digidoc |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- muc2020-ws-343.pdf
- Größe:
- 559.91 KB
- Format:
- Adobe Portable Document Format