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Using topic models for Twitter hashtag recommendation

Published: 13 May 2013 Publication History

Abstract

Since the introduction of microblogging services, there has been a continuous growth of short-text social networking on the Internet. With the generation of large amounts of microposts, there is a need for effective categorization and search of the data. Twitter, one of the largest microblogging sites, allows users to make use of hashtags to categorize their posts. However, the majority of tweets do not contain tags, which hinders the quality of the search results. In this paper, we propose a novel method for unsupervised and content-based hashtag recommendation for tweets. Our approach relies on Latent Dirichlet Allocation (LDA) to model the underlying topic assignment of language classified tweets. The advantage of our approach is the use of a topic distribution to recommend general hashtags.

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A. Mazzia and J. Juett, "Suggesting Hashtags on Twitter," tech. rep., Computer Science and Engineering, University of Michigan, 2009.
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  • (2024)Mutual Interest-Based Twitter Followee Recommendation Using Latent Dirichlet Allocation Topic ModellingJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01125-9Online publication date: 9-Aug-2024
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Published In

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WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web
May 2013
1636 pages
ISBN:9781450320382
DOI:10.1145/2487788

Sponsors

  • NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
  • CGIBR: Comite Gestor da Internet no Brazil

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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Author Tags

  1. hashtag prediction
  2. microposts
  3. short-text classification
  4. topic models
  5. twitter

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  • Research-article

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WWW '13
Sponsor:
  • NICBR
  • CGIBR
WWW '13: 22nd International World Wide Web Conference
May 13 - 17, 2013
Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2024)Topic modeling for short texts: comparative analysis of algorithmsSociology: methodology, methods, mathematical modeling (Sociology: 4M)10.19181/4m.2023.32.1.229:56(69-112)Online publication date: 2024
  • (2024)Analysis on the Research Hot Topics of Smart Classroom Based on LDA Model2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS)10.1109/DOCS63458.2024.10704292(665-670)Online publication date: 16-Aug-2024
  • (2024)Mutual Interest-Based Twitter Followee Recommendation Using Latent Dirichlet Allocation Topic ModellingJournal of The Institution of Engineers (India): Series B10.1007/s40031-024-01125-9Online publication date: 9-Aug-2024
  • (2024)WASM: A Dataset for Hashtag Recommendation for Arabic TweetsArabian Journal for Science and Engineering10.1007/s13369-023-08567-149:9(12131-12145)Online publication date: 3-Jan-2024
  • (2024)Improved Hashtag Recommendation Algorithm Determining Appropriate Hashtags for Words with Different MeaningsThe Review of Socionetwork Strategies10.1007/s12626-024-00173-3Online publication date: 17-Sep-2024
  • (2024)Community Perspectives on ChatGPT: Sentiment Analysis in Educational ForumTechTrends10.1007/s11528-024-01012-6Online publication date: 9-Nov-2024
  • (2024)Analyzing Sentiments and Topics on Twitter Towards Rising Cost of LivingInformation Management and Big Data10.1007/978-3-031-63616-5_13(167-183)Online publication date: 29-Jun-2024
  • (2023)Recommending Words Using a Bayesian NetworkElectronics10.3390/electronics1210221812:10(2218)Online publication date: 12-May-2023
  • (2023)Topic modeling methods for short texts: A surveyJournal of Intelligent & Fuzzy Systems10.3233/JIFS-22383445:2(1971-1990)Online publication date: 1-Aug-2023
  • (2023)An Efficient and Accurate GPU-based Deep Learning Model for Multimedia RecommendationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/352402220:2(1-18)Online publication date: 25-Sep-2023
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