Identification and Recommendation of Authorities on Different Topics Based on Twitter

Roque Daniel Contreras Chacon; Jack Fernando Bravo Torres; Jennifer Andrea Yepez Alulema; Diego Andres Cuji Dutan; Paul Esteban Vintimilla Tapia

Identification and Recommendation of Authorities on Different Topics Based on Twitter

Roque Daniel Contreras Chacon, Jack Fernando Bravo Torres, Jennifer Andrea Yepez Alulema, Diego Andres Cuji Dutan, Paul Esteban Vintimilla Tapia

Research output: Contribution to journal › Article

Abstract

The social platform Twitter is one of the most famous world microblogs. Monthly, it hosts hundreds of millions of visitors who publisha large amount of data that is consumed by others. Some of these publications help to others to generate knowledge. Users who generate content in a particular area, helping people to increase their knowledge, are classified as authorities in that field. This content could be exploited by students to improve their academic performance. In this paper, we present a system to identify and recommend authorities in a particular area based on machine learning, supervised and unsupervised techniques. To verify accuracy and our model, we developed several tests within a particular topic. The results show that the system can identify authorities with an average accuracy of [78.82 ±2.51] %, representing a high degree of confidence.

Translated title of the contribution	Identificación y Recomendación de Autoridades en Diferentes Temas Basado en Twitter
Original language	English (US)
Pages (from-to)	1-10
Number of pages	10
Journal	International Journal of Pure and Applied Mathematics
Volume	114
Issue number	114
State	Published - 1 Jan 2017

Keywords

Big data
Recommender systems
Social networks
Twitter

CACES Knowledge Areas

116A Computer Science

Cite this

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title = "Identification and Recommendation of Authorities on Different Topics Based on Twitter",

abstract = "The social platform Twitter is one of the most famous world microblogs. Monthly, it hosts hundreds of millions of visitors who publisha large amount of data that is consumed by others. Some of these publications help to others to generate knowledge. Users who generate content in a particular area, helping people to increase their knowledge, are classified as authorities in that field. This content could be exploited by students to improve their academic performance. In this paper, we present a system to identify and recommend authorities in a particular area based on machine learning, supervised and unsupervised techniques. To verify accuracy and our model, we developed several tests within a particular topic. The results show that the system can identify authorities with an average accuracy of [78.82 ±2.51] %, representing a high degree of confidence.",

keywords = "Big data, Recommender systems, Social networks, Twitter, Big data, Recommender systems, Social networks, Twitter",

author = "{Contreras Chacon}, {Roque Daniel} and {Bravo Torres}, {Jack Fernando} and {Yepez Alulema}, {Jennifer Andrea} and {Cuji Dutan}, {Diego Andres} and {Vintimilla Tapia}, {Paul Esteban}",

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T1 - Identification and Recommendation of Authorities on Different Topics Based on Twitter

AU - Contreras Chacon, Roque Daniel

AU - Bravo Torres, Jack Fernando

AU - Yepez Alulema, Jennifer Andrea

AU - Cuji Dutan, Diego Andres

AU - Vintimilla Tapia, Paul Esteban

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The social platform Twitter is one of the most famous world microblogs. Monthly, it hosts hundreds of millions of visitors who publisha large amount of data that is consumed by others. Some of these publications help to others to generate knowledge. Users who generate content in a particular area, helping people to increase their knowledge, are classified as authorities in that field. This content could be exploited by students to improve their academic performance. In this paper, we present a system to identify and recommend authorities in a particular area based on machine learning, supervised and unsupervised techniques. To verify accuracy and our model, we developed several tests within a particular topic. The results show that the system can identify authorities with an average accuracy of [78.82 ±2.51] %, representing a high degree of confidence.

AB - The social platform Twitter is one of the most famous world microblogs. Monthly, it hosts hundreds of millions of visitors who publisha large amount of data that is consumed by others. Some of these publications help to others to generate knowledge. Users who generate content in a particular area, helping people to increase their knowledge, are classified as authorities in that field. This content could be exploited by students to improve their academic performance. In this paper, we present a system to identify and recommend authorities in a particular area based on machine learning, supervised and unsupervised techniques. To verify accuracy and our model, we developed several tests within a particular topic. The results show that the system can identify authorities with an average accuracy of [78.82 ±2.51] %, representing a high degree of confidence.

KW - Big data

KW - Recommender systems

KW - Social networks

KW - Twitter

KW - Big data

KW - Recommender systems

KW - Social networks

KW - Twitter

UR - https://acadpubl.eu/jsi/2017-114-7-ICPCIT-2017/articles/11/12.pdf

M3 - Article

SN - 1311-8080

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EP - 10

JO - International Journal of Pure and Applied Mathematics

JF - International Journal of Pure and Applied Mathematics

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ER -

Identification and Recommendation of Authorities on Different Topics Based on Twitter

Abstract

Keywords

CACES Knowledge Areas

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