Characterization and Early Detection of Evergreen News Articles

Yiming Liao; Shuguang Wang; Eui Hong Han; Jongwuk Lee; Dongwon Lee

doi:10.1007/978-3-030-46133-1_33

Characterization and Early Detection of Evergreen News Articles

Yiming Liao
, Shuguang Wang
, Eui Hong Han
, Jongwuk Lee
, Dongwon Lee

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Although the majority of news articles are only viewed for days or weeks, there are a small fraction of news articles that are read across years, thus named as evergreen news articles. Because evergreen articles maintain a timeless quality and are consistently of interests to the public, understanding their characteristics better has huge implications for news outlets and platforms yet there are few studies that have explicitly investigated on evergreen articles. Addressing this gap, in this paper, we first propose a flexible parameterized definition of evergreen articles to capture their long-term high traffic patterns. Using a real dataset from the Washington Post, then, we unearth several distinctive characteristics of evergreen articles and build an early prediction model with encouraging results. Although less than 1% of news articles were identified as evergreen, our model achieves 0.961 in ROC AUC and 0.172 in PR AUC in 10-fold cross validation.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
Editors	Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Publisher	Springer
Pages	552-568
Number of pages	17
ISBN (Print)	9783030461324
DOIs	https://doi.org/10.1007/978-3-030-46133-1_33
State	Published - 2020
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany Duration: 16 Sep 2019 → 20 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11908 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/Territory	Germany
City	Wurzburg
Period	16/09/19 → 20/09/19

Keywords

Evergreen
Long-term popularity
News articles

Access to Document

10.1007/978-3-030-46133-1_33

Cite this

Liao, Y., Wang, S., Han, E. H., Lee, J., & Lee, D. (2020). Characterization and Early Detection of Evergreen News Articles. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings (pp. 552-568). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908 LNAI). Springer. https://doi.org/10.1007/978-3-030-46133-1_33

Liao, Yiming ; Wang, Shuguang ; Han, Eui Hong et al. / Characterization and Early Detection of Evergreen News Articles. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. Springer, 2020. pp. 552-568 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f1f05917fa774387af5cea607917e6e5,

title = "Characterization and Early Detection of Evergreen News Articles",

abstract = "Although the majority of news articles are only viewed for days or weeks, there are a small fraction of news articles that are read across years, thus named as evergreen news articles. Because evergreen articles maintain a timeless quality and are consistently of interests to the public, understanding their characteristics better has huge implications for news outlets and platforms yet there are few studies that have explicitly investigated on evergreen articles. Addressing this gap, in this paper, we first propose a flexible parameterized definition of evergreen articles to capture their long-term high traffic patterns. Using a real dataset from the Washington Post, then, we unearth several distinctive characteristics of evergreen articles and build an early prediction model with encouraging results. Although less than 1\% of news articles were identified as evergreen, our model achieves 0.961 in ROC AUC and 0.172 in PR AUC in 10-fold cross validation.",

keywords = "Evergreen, Long-term popularity, News articles",

author = "Yiming Liao and Shuguang Wang and Han, \{Eui Hong\} and Jongwuk Lee and Dongwon Lee",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 ; Conference date: 16-09-2019 Through 20-09-2019",

year = "2020",

doi = "10.1007/978-3-030-46133-1\_33",

language = "English",

isbn = "9783030461324",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "552--568",

editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",

booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings",

address = "United States",

}

Liao, Y, Wang, S, Han, EH, Lee, J & Lee, D 2020, Characterization and Early Detection of Evergreen News Articles. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11908 LNAI, Springer, pp. 552-568, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg, Germany, 16/09/19. https://doi.org/10.1007/978-3-030-46133-1_33

Characterization and Early Detection of Evergreen News Articles. / Liao, Yiming; Wang, Shuguang; Han, Eui Hong et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. Springer, 2020. p. 552-568 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Characterization and Early Detection of Evergreen News Articles

AU - Liao, Yiming

AU - Wang, Shuguang

AU - Han, Eui Hong

AU - Lee, Jongwuk

AU - Lee, Dongwon

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - Although the majority of news articles are only viewed for days or weeks, there are a small fraction of news articles that are read across years, thus named as evergreen news articles. Because evergreen articles maintain a timeless quality and are consistently of interests to the public, understanding their characteristics better has huge implications for news outlets and platforms yet there are few studies that have explicitly investigated on evergreen articles. Addressing this gap, in this paper, we first propose a flexible parameterized definition of evergreen articles to capture their long-term high traffic patterns. Using a real dataset from the Washington Post, then, we unearth several distinctive characteristics of evergreen articles and build an early prediction model with encouraging results. Although less than 1% of news articles were identified as evergreen, our model achieves 0.961 in ROC AUC and 0.172 in PR AUC in 10-fold cross validation.

AB - Although the majority of news articles are only viewed for days or weeks, there are a small fraction of news articles that are read across years, thus named as evergreen news articles. Because evergreen articles maintain a timeless quality and are consistently of interests to the public, understanding their characteristics better has huge implications for news outlets and platforms yet there are few studies that have explicitly investigated on evergreen articles. Addressing this gap, in this paper, we first propose a flexible parameterized definition of evergreen articles to capture their long-term high traffic patterns. Using a real dataset from the Washington Post, then, we unearth several distinctive characteristics of evergreen articles and build an early prediction model with encouraging results. Although less than 1% of news articles were identified as evergreen, our model achieves 0.961 in ROC AUC and 0.172 in PR AUC in 10-fold cross validation.

KW - Evergreen

KW - Long-term popularity

KW - News articles

UR - https://www.scopus.com/pages/publications/85084795907

U2 - 10.1007/978-3-030-46133-1_33

DO - 10.1007/978-3-030-46133-1_33

M3 - Conference contribution

AN - SCOPUS:85084795907

SN - 9783030461324

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 552

EP - 568

BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings

A2 - Brefeld, Ulf

A2 - Fromont, Elisa

A2 - Hotho, Andreas

A2 - Knobbe, Arno

A2 - Maathuis, Marloes

A2 - Robardet, Céline

PB - Springer

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019

Y2 - 16 September 2019 through 20 September 2019

ER -

Liao Y, Wang S, Han EH, Lee J, Lee D. Characterization and Early Detection of Evergreen News Articles. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Springer. 2020. p. 552-568. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-46133-1_33

Characterization and Early Detection of Evergreen News Articles

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this