research-article

Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

Authors:

Dominik Kowald,

Subhash Chandra Pujari,

Elisabeth LexAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 1401 - 1410

https://doi.org/10.1145/3038912.3052605

Published: 03 April 2017 Publication History

Abstract

Hashtags have become a powerful tool in social platforms such as Twitter to categorize and search for content, and to spread short messages across members of the social network. In this paper, we study temporal hashtag usage practices in Twitter with the aim of designing a cognitive-inspired hashtag recommendation algorithm we call BLLi,s. Our main idea is to incorporate the effect of time on (i) individual hashtag reuse (i.e., reusing own hashtags), and (ii) social hashtag reuse (i.e., reusing hashtags, which has been previously used by a followee) into a predictive model. For this, we turn to the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R, which accounts for the time-dependent decay of item exposure in human memory. We validate BLL_I,S using two crawled Twitter datasets in two evaluation scenarios. Firstly, only temporal usage patterns of past hashtag assignments are utilized and secondly, these patterns are combined with a content-based analysis of the current tweet. In both evaluation scenarios, we find not only that temporal effects play an important role for both individual and social hashtag reuse but also that our BLL_I,S approach provides significantly better prediction accuracy and ranking results than current state-of-the-art hashtag recommendation methods.

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Cited By

Schedl MMoscati MSguerra BHennequin RLex EAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)Psychology-informed Information Access Systems WorkshopProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635722(1216-1217)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635722
Kowald DReiter-Haas MKopeinik SSchedl MLex E(2024)Transparent Music Preference Modeling and Recommendation with a Model of Human Memory TheoryA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_4(113-136)Online publication date: 1-May-2024
https://doi.org/10.1007/978-3-031-55109-3_4
Sguerra BTran VHennequin R(2023)Ex2Vec: Characterizing Users and Items from the Mere Exposure EffectProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608856(971-977)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608856
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Index Terms

Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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https://dl.acm.org/doi/10.1145/3616855.3635722
Kowald DReiter-Haas MKopeinik SSchedl MLex E(2024)Transparent Music Preference Modeling and Recommendation with a Model of Human Memory TheoryA Human-Centered Perspective of Intelligent Personalized Environments and Systems10.1007/978-3-031-55109-3_4(113-136)Online publication date: 1-May-2024
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Sguerra BTran VHennequin R(2023)Ex2Vec: Characterizing Users and Items from the Mere Exposure EffectProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608856(971-977)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608856
Moscati MWallmann CReiter-Haas MKowald DLex ESchedl M(2023)Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608838(840-847)Online publication date: 14-Sep-2023
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