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A hashtag recommendation system for twitter data streams

Overview of attention for article published in Computational Social Networks, May 2016
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Mentioned by

twitter
1 tweeter

Citations

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16 Dimensions

Readers on

mendeley
48 Mendeley
Title
A hashtag recommendation system for twitter data streams
Published in
Computational Social Networks, May 2016
DOI 10.1186/s40649-016-0028-9
Pubmed ID
Authors

Eriko Otsuka, Scott A. Wallace, David Chiu

Abstract

Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests on-demand. For hashtag ranking, we propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness which is one of the key challenges in analyzing microblog data. Our system is built on top of Hadoop, a leading platform for distributed computing, to provide scalable performance using Map-Reduce. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations are more stable and reliable than ranking tags based on tweet content similarity. Our results show that HF-IHU can achieve over 30 % hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet. Furthermore, our method out-performs kNN, k-popularity, and Naïve Bayes by 69, 54, and 17 %, respectively, on recall of the top 200 hashtags.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 19%
Student > Ph. D. Student 8 17%
Student > Master 8 17%
Student > Doctoral Student 2 4%
Researcher 2 4%
Other 7 15%
Unknown 12 25%
Readers by discipline Count As %
Computer Science 22 46%
Medicine and Dentistry 3 6%
Business, Management and Accounting 2 4%
Engineering 2 4%
Social Sciences 2 4%
Other 3 6%
Unknown 14 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2021.
All research outputs
#14,366,602
of 21,364,317 outputs
Outputs from Computational Social Networks
#25
of 40 outputs
Outputs of similar age
#165,420
of 282,192 outputs
Outputs of similar age from Computational Social Networks
#1
of 1 outputs
Altmetric has tracked 21,364,317 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 40 research outputs from this source. They receive a mean Attention Score of 3.7. This one scored the same or higher as 15 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 282,192 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them