The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
| dc.contributor.author | Lex, Elisabeth | |
| dc.contributor.author | Kowald, Dominik | |
| dc.contributor.editor | David, Klaus | |
| dc.contributor.editor | Geihs, Kurt | |
| dc.contributor.editor | Lange, Martin | |
| dc.contributor.editor | Stumme, Gerd | |
| dc.date.accessioned | 2019-08-27T12:55:25Z | |
| dc.date.available | 2019-08-27T12:55:25Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model how a person reuses her own, individual hashtags as well as hashtags from her social network. The BLL equation accounts for the time-dependent decay of item exposure in human memory. According to BLL, the usefulness of a piece of information (e.g., a hashtag) is defined by how frequently and how recently it was used in the past, following a time-dependent decay that is best modeled with a power-law distribution. We used the BLL equation in our previous work to recommend tags in social bookmarking systems [KL16]. Here [KPL17], we adopt the BLL equation to model temporal reuse patterns of individual (i.e., reusing own hashtags) and social hashtags (i.e., reusing hashtags, which has been previously used by a followee) and to build a cognitive-inspired hashtag recommendation algorithm. We demonstrate the efficacy of our approach in two empirical social networks crawled from Twitter, i.e., CompSci and Random (for details about the datasets, see [KPL17]). Our results show that our approach can outperform current state-of-the-art hashtag recommendation approaches. | en |
| dc.identifier.doi | 10.18420/inf2019_46 | |
| dc.identifier.isbn | 978-3-88579-688-6 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/24995 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-294 | |
| dc.subject | hashtag recommendation | |
| dc.subject | ACT-R | |
| dc.subject | temporal effects | |
| dc.subject | hashtag reuse | |
| dc.subject | user behavior modeling | |
| dc.title | The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 286 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 285 | |
| gi.conference.date | 23.-26. September 2019 | |
| gi.conference.location | Kassel | |
| gi.conference.sessiontitle | Data Science |
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