Exploring characteristics of online news comments and commenters with machine learning approaches

被引：11

作者：

Lee, Sang Yup ^{[1
]}

Ryu, Min Ho ^{[2
]}

机构：

[1] Yonsei Univ, Dept Commun, 102 Billingsley Hall,50 Yonsei Ro, Seoul 03722, South Korea

[2] Dong A Univ, Dept Management Informat Syst, 255 Gudeok Ro, Busan 49236, South Korea

来源：

TELEMATICS AND INFORMATICS | 2019年 / 43卷

关键词：

Online news comments; Demographics of news commenters; Online news participation; Big data; Machine learning; USER-GENERATED CONTENT; JOURNALISM; PARTICIPATION; MEDIA; INTERACTIVITY; DISCUSSIONS; PERCEPTIONS; NEWSPAPERS; 3RD-PERSON;

D O I：

10.1016/j.tele.2019.101249

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

Despite the pervasiveness and significant influence of online news comments, few studies have examined people's commenting behaviors regarding online news articles. This study examined the distribution of age and gender of online news commenters according to news topics. Using a computational approach, we collected publicly available data on online news comments and commenters from the most popular South Korean news aggregator during a three-month period spanning May to July 2016. The clusters of news were identified and categorized using machine learning techniques. The comments and commenters of 20,929 news articles were examined. We found that there were age and gender differences in commenting behaviors that varied based on news topics. Findings concerning large discrepancies in the ages and genders of commenters suggest that online commenting systems should be improved in a way that can guarantee more diverse opinions from readers.

引用

页数：10

共 50 条

[41] Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches
Francky Fouedjio
Jens Klump
[J]. Environmental Earth Sciences, 2019, 78
[42] Exploring prediction uncertainty of spatial data in geostatistical and machine learning approaches
Fouedjio, Francky
Klump, Jens
[J]. ENVIRONMENTAL EARTH SCIENCES, 2019, 78 (01)
[43] A Survey of Machine Learning Approaches for Student Dropout Prediction in Online Courses
Prenkaj, Bardh
Velardi, Paola
Stilo, Giovanni
Distante, Damiano
Faralli, Stefano
[J]. ACM COMPUTING SURVEYS, 2020, 53 (03)
[44] Exploring and Comparing Machine Learning Approaches for Predicting Mood Over Time
van Breda, Ward
Pastor, Johnno
Hoogendoorn, Mark
Ruwaard, Jeroen
Asselbergs, Joost
Riper, Heleen
[J]. INNOVATION IN MEDICINE AND HEALTHCARE 2016, 2016, 60 : 37 - 47
[45] Predicting of anaphylaxis in big data EMR by exploring machine learning approaches
Segura-Bedmar, Isabel
Colon-Ruiz, Cristobal
Angel Tejedor-Alonso, Miguel
Moro-Moro, Mar
[J]. JOURNAL OF BIOMEDICAL INFORMATICS, 2018, 87 : 50 - 59
[46] Fake News Detection: Traditional vs. Contemporary Machine Learning Approaches
Binay, Aditya
Binay, Anisha
Register, Jordan
[J]. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT, 2024, 23 (05)
[47] Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches
Shao, Zhuce
Gao, Huanshen
Wang, Benlong
Zhang, Shenqi
[J]. FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY, 2024, 14
[48] Comparison between Machine Learning and Deep Learning Approaches for the Detection of Toxic Comments on Social Networks
Bonetti, Andrea
Martinez-Sober, Marcelino
Torres, Julio C.
Vega, Jose M.
Pellerin, Sebastien
Vila-Frances, Joan
[J]. APPLIED SCIENCES-BASEL, 2023, 13 (10):
[49] The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments
Kolhatkar, Varada
Wu, Hanhan
Cavasso, Luca
Francis, Emilie
Shukla, Kavan
Taboada, Maite
[J]. CORPUS PRAGMATICS, 2020, 4 (02) : 155 - 190
[50] The SFU Opinion and Comments Corpus: A Corpus for the Analysis of Online News Comments
Varada Kolhatkar
Hanhan Wu
Luca Cavasso
Emilie Francis
Kavan Shukla
Maite Taboada
[J]. Corpus Pragmatics, 2020, 4 : 155 - 190

← 1 2 3 4 5 →