Context over time: Modeling context evolution in social media

Md Hijbul Alam; Woo Jong Ryu; Sang Keun Lee

doi:10.1145/2665994.2665996

Context over time: Modeling context evolution in social media

Md Hijbul Alam
, Woo Jong Ryu
, Sang Keun Lee

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

5 Sitaatiot (Scopus)

Abstrakti

The rise of online social media has led to an explosion in user-generated content. However, user-generated content is difficult to analyze in isolation from its context. Accordingly, context detection and tracking its evolution is essential to understanding social media. This paper presents a statistical model that can detect interpretable topics along with their contexts. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags that frequently occur with a topic. The model combines a context with a related topic by jointly modeling words with hashtags and time. Experiments on real datasets demonstrate that the proposed model successfully discovers both meaningful topics and contexts, and tracks their evolution.

Alkuperäiskieli	Englanti
Otsikko	DUBMOD 2014 - Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, co-located with CIKM 2014
Kustantaja	ACM
Sivut	15-18
Sivumäärä	4
Vuosikerta	2014-November
Painos	November
ISBN (painettu)	978-1-4503-1303-2
DOI - pysyväislinkit	https://doi.org/10.1145/2665994.2665996
Tila	Julkaistu - 2014
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD 2014, Co-located with 23rd ACM Conference on Information and Knowledge Management, CIKM 2014 - Kesto: 1 tammik. 2014 → …

Conference

Conference	3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD 2014, Co-located with 23rd ACM Conference on Information and Knowledge Management, CIKM 2014
Ajanjakso	1/01/14 → …

Tutkimusalat

Context and topic evolution
Social media
Topic model

Julkaisufoorumi-taso

Jufo-taso 1

Pääsy asiakirjaan

10.1145/2665994.2665996

Siteeraa tätä

@inproceedings{90a17f5bd62942a39a508c69408e7eeb,

title = "Context over time: Modeling context evolution in social media",

abstract = "The rise of online social media has led to an explosion in user-generated content. However, user-generated content is difficult to analyze in isolation from its context. Accordingly, context detection and tracking its evolution is essential to understanding social media. This paper presents a statistical model that can detect interpretable topics along with their contexts. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags that frequently occur with a topic. The model combines a context with a related topic by jointly modeling words with hashtags and time. Experiments on real datasets demonstrate that the proposed model successfully discovers both meaningful topics and contexts, and tracks their evolution.",

keywords = "Context and topic evolution, Social media, Topic model, Context and topic evolution, Social media, Topic model",

author = "Alam, \{Md Hijbul\} and Ryu, \{Woo Jong\} and Lee, \{Sang Keun\}",

year = "2014",

doi = "10.1145/2665994.2665996",

language = "English",

isbn = "978-1-4503-1303-2",

volume = "2014-November",

pages = "15--18",

booktitle = "DUBMOD 2014 - Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, co-located with CIKM 2014",

publisher = "ACM",

address = "United States",

edition = "November",

note = "3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD 2014, Co-located with 23rd ACM Conference on Information and Knowledge Management, CIKM 2014 ; Conference date: 01-01-2014",

}

Alam, MH, Ryu, WJ & Lee, SK 2014, Context over time: Modeling context evolution in social media. julkaisussa DUBMOD 2014 - Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, co-located with CIKM 2014. November toim, Vuosikerta 2014-November, ACM, Sivut 15-18, 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD 2014, Co-located with 23rd ACM Conference on Information and Knowledge Management, CIKM 2014, 1/01/14. https://doi.org/10.1145/2665994.2665996

Context over time: Modeling context evolution in social media. / Alam, Md Hijbul; Ryu, Woo Jong; Lee, Sang Keun.
DUBMOD 2014 - Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, co-located with CIKM 2014. Vuosikerta 2014-November November. toim. ACM, 2014. s. 15-18.

Tutkimustuotos: Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

TY - GEN

T1 - Context over time: Modeling context evolution in social media

AU - Alam, Md Hijbul

AU - Ryu, Woo Jong

AU - Lee, Sang Keun

PY - 2014

Y1 - 2014

N2 - The rise of online social media has led to an explosion in user-generated content. However, user-generated content is difficult to analyze in isolation from its context. Accordingly, context detection and tracking its evolution is essential to understanding social media. This paper presents a statistical model that can detect interpretable topics along with their contexts. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags that frequently occur with a topic. The model combines a context with a related topic by jointly modeling words with hashtags and time. Experiments on real datasets demonstrate that the proposed model successfully discovers both meaningful topics and contexts, and tracks their evolution.

AB - The rise of online social media has led to an explosion in user-generated content. However, user-generated content is difficult to analyze in isolation from its context. Accordingly, context detection and tracking its evolution is essential to understanding social media. This paper presents a statistical model that can detect interpretable topics along with their contexts. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags that frequently occur with a topic. The model combines a context with a related topic by jointly modeling words with hashtags and time. Experiments on real datasets demonstrate that the proposed model successfully discovers both meaningful topics and contexts, and tracks their evolution.

KW - Context and topic evolution

KW - Social media

KW - Topic model

KW - Context and topic evolution

KW - Social media

KW - Topic model

U2 - 10.1145/2665994.2665996

DO - 10.1145/2665994.2665996

M3 - Conference contribution

AN - SCOPUS:84937715645

SN - 978-1-4503-1303-2

VL - 2014-November

SP - 15

EP - 18

BT - DUBMOD 2014 - Proceedings of the 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, co-located with CIKM 2014

PB - ACM

T2 - 3rd Workshop on Data-Driven User Behavioral Modeling and Mining from Social Media, DUBMOD 2014, Co-located with 23rd ACM Conference on Information and Knowledge Management, CIKM 2014

Y2 - 1 January 2014

ER -

Context over time: Modeling context evolution in social media

Abstrakti

Conference

Tutkimusalat

Julkaisufoorumi-taso

Pääsy asiakirjaan

Sormenjälki

Siteeraa tätä