Content diffusion prediction in social networks

Balali, Ali; Rajabi, Aboozar; Ghassemi, Sepehr; Asadpour, Masoud; Faili, Hesham

doi:10.1109/ikt.2013.6620114

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…In the comparable way, paper by Balali A. and Rajabi A. et al [6] has made examination on the publication time and content of online news offices, to identify effective variables of diffusing content out. It additionally utilized the Random Forest Classifier to characterize articles in three classifications, i.e.…”

Section: Related Workmentioning

confidence: 99%

Fusion of PACE Regression and Decision Tree for Comment Volume Prediction

Kaur¹,

Verma²

2016

IJDTA

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Section: Related Workmentioning

confidence: 99%

Fusion of PACE Regression and Decision Tree for Comment Volume Prediction

Kaur¹,

Verma²

2016

IJDTA

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“…Outcomes show better results for binary classification and evaluated that the Textual and semantic features are strong performers among others. In the similar way, paper [7] has made analysis on the content and publish time of online news agencies, to detect effective factors of diffusing contents in public. It has also used the Random Forest Classifier to classify articles in three categories i.e.…”

Section: Related Workmentioning

confidence: 99%

Comment Volume Prediction using Regression

Kaur¹,

Verma²

2016

IJCA

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The latest decade lead to a unconstrained advancement of the importance of online networking. Due to the gigantic measures of records appearing in web organizing, there is a colossal necessity for the programmed examination of such records. Online networking customer's comments expect a basic part in building or changing the one's acknowledgments concerning some specific indicate or making it standard. This paper demonstrates a preliminary work to exhibit the sufficiency of machine learning prescient calculations on the remarks of most well known long range informal communication site, Facebook. We showed the customer remark patters, over the posts on Facebook Pages and expected that what number of remarks a post is depended upon to get in next H hrs. To automate the technique, we developed an item display containing the crawler, information processor and data disclosure module. For prediction, we used the Linear Regression model (Simple Linear model, Linear relapse model and Pace relapse model) and Non-Linear Regression model(Decision tree, MLP) on different data set varieties and evaluated them under the appraisal estimations Hits@10, AUC@10, Processing Time and Mean Absolute Error.

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“…Faktor kebolehan mudah alih (Kwon, Park & Kim, 2014;Pee, Maksum & Norizan, 2014), faktor pendedahan (Helou & Ab. Rahim, 2014), faktor kandungan (Frey et al 2013;Balali et al 2013) dan faktor sosial (Glynn, Huge & Hoffman, 2012) dikenalpasti sebagai faktor yang mempengaruhi penggunaan rangkaian sosial diperingkat pendidikan tertier dan pascasiswazah.…”

Section: 10 Pendahuluanunclassified

Model Regressi Penggunaan Rangkaian Sosial Pendidikan

Shamsudin

Aris

Rosli

2016

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Tujuan kajian ini adalah untuk mengkaji kekerapan penggunaan rangkaian sosial dalam kalangan pelajar sarjana teknologi pendidikan di Universiti Teknologi Malaysia serta mengkaji faktor-faktor penggunaannya. Kajian ini menggunakan rekabentuk kajian tinjauan, soal selidik digunakan sebagai instrumen kajian dan 70 orang sampel kajian dilibatkan menggunakan kaedah persampelan rawak mudah. Faktor penggunaan dibahagikan kepada empat faktor iaitu faktor teknologi, faktor pendedahan, faktor kandungan serta faktor pengaruh sosial. Kebolehpercayaan instrumen kajian adalah 0.88. Ujian regresi pelbagai kaedah prosedur penyelesaian ke belakang digunakan bagi membangunkan model regressi penggunaan rangkaian sosial pendidikan. Hasil dapatan kajian mencadangkan pembangunan fizikal tidak perlu difokuskan, namun pembangunan infrastruktur bagi tujuan menyokong pembelajaran ubiquitous adalah disarankan.

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Content diffusion prediction in social networks

Cited by 7 publications

References 14 publications

Fusion of PACE Regression and Decision Tree for Comment Volume Prediction

Fusion of PACE Regression and Decision Tree for Comment Volume Prediction

Comment Volume Prediction using Regression

Model Regressi Penggunaan Rangkaian Sosial Pendidikan

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