Research topics and trends of the hashtag recommendation domain

Amiri, Bahram; Karimianghadim, Ramin; Hossain, Liaquat

doi:10.1007/s11192-021-03874-6

Cited by 5 publications

(3 citation statements)

References 101 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This hashtag recommendation system is based on CNN and LSTM models and works with a mix of other types of data like images. Also, based on [ 28 ], popularity prediction in the hashtag recommendation field is not researched as much as others. Most of this research also conducted on textual information and Twitter social network [ 24 , 49 ].…”

Section: Related Workmentioning

confidence: 99%

“…Based on the review of survey [28], hashtag recommendations on social network systems are categorized into fve sections. Tese categories are methods based on similarity measures [6][7][8][9] and classifcation models based on oldfashioned machine learning methods [10,12] and models based on topic modeling [13][14][15][16], topical translation models [17][18][19][20][21], and Deep Learning models [4,[22][23][24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

“…This study [ 4 ] proposed the coattention LSTM model to generate tags, and this article presented a multimodal model for video tagging and also proposed the LSTM neural network for image captioning based on videos thumbnails [ 5 ]; however, that is not the subject of our study. Most of them are about Twitter social network and textual data [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Popular Tag Recommendation by Neural Network in Social Media

Jafari Sadr,

Mirtaheri,

Greco

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Although “a picture is worth a thousand words,” this may not be enough to get your post seen on social media. This study’s main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting-edge GPT-2 language model for the second portion. This work differs from others in that, and it initially offered a cutting-edge GPT-2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well-liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Popular Tag Recommendation by Neural Network in Social Media

Jafari Sadr,

Mirtaheri,

Greco

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Analysing Protest-Related Tweets: An Evaluation of Techniques by the Open Source Intelligence Team

Müter,

Veltkamp

2024

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Revealing the structure of combinative research design and performance in an operations management context

Curado,

Oliveira,

Teixeira

et al. 2024

Manag Rev Q

View full text Add to dashboard Cite

This study defines the conceptual structure of combinative research design in operations management by relying on several proxies of combinative research topics, methods, and author affiliations, covering a decade of research and analyzing 1026 bibliographic records in an applied social science research context. Through the use of bibliometrics, co-word analyses are conducted to compute keyword co-occurrences to determine distinct interdisciplinary structures. Furthermore, this study conducts a series of regression analyses to determine the associations between combinative research (based on combinative topics, methods and author affiliations) and several proxies of article performance, including usage metrics (citations and views) and effort and time expenditures (the number of days under review and the number of revisions), while controlling for article age and the journal outlet. The results indicate strong associations with multi-country author affiliations with reduced effort and time expenditures as well as increased usage and scholarly attention. Future research directions are provided that integrate with replication studies, meta-analyses, and literature reviews.

show abstract

Research topics and trends of the hashtag recommendation domain

Cited by 5 publications

References 101 publications

Popular Tag Recommendation by Neural Network in Social Media

Popular Tag Recommendation by Neural Network in Social Media

Analysing Protest-Related Tweets: An Evaluation of Techniques by the Open Source Intelligence Team

Revealing the structure of combinative research design and performance in an operations management context

Contact Info

Product

Resources

About