Topy: Real-time story tracking via social tags

Gevorg Poghosyan; M. Atif Qureshi; Georgiana Ifrim

doi:10.1007/978-3-319-46131-1_10

Topy: Real-time story tracking via social tags

Gevorg Poghosyan
, M. Atif Qureshi
, Georgiana Ifrim

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

Abstract

The Topy system automates real-time story tracking by utilizing crowd-sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., #rip, #blacklivesmatter, #icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., #palmyra, #isis, #refugeecrisis).

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
Editors	Björn Bringmann, Elisa Fromont, Nikolaj Tatti, Volker Tresp, Pauli Miettinen, Bettina Berendt, Gemma Garriga
Publisher	Springer Verlag
Pages	45-49
Number of pages	5
ISBN (Print)	9783319461304
DOIs	https://doi.org/10.1007/978-3-319-46131-1_10
Publication status	Published - 2016
Externally published	Yes
Event	15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy Duration: 19 Sep 2016 → 23 Sep 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9853 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
Country/Territory	Italy
City	Riva del Garda
Period	19/09/16 → 23/09/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

Keywords

News
Social media
Social tags
Story tracking

Access to Document

10.1007/978-3-319-46131-1_10

Cite this

Poghosyan, G., Atif Qureshi, M., & Ifrim, G. (2016). Topy: Real-time story tracking via social tags. In B. Bringmann, E. Fromont, N. Tatti, V. Tresp, P. Miettinen, B. Berendt, & G. Garriga (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings (pp. 45-49). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9853 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46131-1_10

Poghosyan, Gevorg ; Atif Qureshi, M. ; Ifrim, Georgiana. / Topy : Real-time story tracking via social tags. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. editor / Björn Bringmann ; Elisa Fromont ; Nikolaj Tatti ; Volker Tresp ; Pauli Miettinen ; Bettina Berendt ; Gemma Garriga. Springer Verlag, 2016. pp. 45-49 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5054aceb113d4755b376c0bd6c0f330d,

title = "Topy: Real-time story tracking via social tags",

abstract = "The Topy system automates real-time story tracking by utilizing crowd-sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., \#rip, \#blacklivesmatter, \#icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., \#palmyra, \#isis, \#refugeecrisis).",

keywords = "News, Social media, Social tags, Story tracking",

author = "Gevorg Poghosyan and \{Atif Qureshi\}, M. and Georgiana Ifrim",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 ; Conference date: 19-09-2016 Through 23-09-2016",

year = "2016",

doi = "10.1007/978-3-319-46131-1\_10",

language = "English",

isbn = "9783319461304",

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

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pages = "45--49",

editor = "Bj{\"o}rn Bringmann and Elisa Fromont and Nikolaj Tatti and Volker Tresp and Pauli Miettinen and Bettina Berendt and Gemma Garriga",

booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings",

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}

Poghosyan, G, Atif Qureshi, M & Ifrim, G 2016, Topy: Real-time story tracking via social tags. in B Bringmann, E Fromont, N Tatti, V Tresp, P Miettinen, B Berendt & G Garriga (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9853 LNCS, Springer Verlag, pp. 45-49, 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016, Riva del Garda, Italy, 19/09/16. https://doi.org/10.1007/978-3-319-46131-1_10

Topy: Real-time story tracking via social tags. / Poghosyan, Gevorg; Atif Qureshi, M.; Ifrim, Georgiana.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. ed. / Björn Bringmann; Elisa Fromont; Nikolaj Tatti; Volker Tresp; Pauli Miettinen; Bettina Berendt; Gemma Garriga. Springer Verlag, 2016. p. 45-49 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9853 LNCS).

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

TY - GEN

T1 - Topy

T2 - 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

AU - Poghosyan, Gevorg

AU - Atif Qureshi, M.

AU - Ifrim, Georgiana

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - The Topy system automates real-time story tracking by utilizing crowd-sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., #rip, #blacklivesmatter, #icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., #palmyra, #isis, #refugeecrisis).

AB - The Topy system automates real-time story tracking by utilizing crowd-sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., #rip, #blacklivesmatter, #icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., #palmyra, #isis, #refugeecrisis).

KW - News

KW - Social media

KW - Social tags

KW - Story tracking

UR - https://www.scopus.com/pages/publications/84988640444

U2 - 10.1007/978-3-319-46131-1_10

DO - 10.1007/978-3-319-46131-1_10

M3 - Conference contribution

AN - SCOPUS:84988640444

SN - 9783319461304

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

SP - 45

EP - 49

BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings

A2 - Bringmann, Björn

A2 - Fromont, Elisa

A2 - Tatti, Nikolaj

A2 - Tresp, Volker

A2 - Miettinen, Pauli

A2 - Berendt, Bettina

A2 - Garriga, Gemma

PB - Springer Verlag

Y2 - 19 September 2016 through 23 September 2016

ER -

Poghosyan G, Atif Qureshi M, Ifrim G. Topy: Real-time story tracking via social tags. In Bringmann B, Fromont E, Tatti N, Tresp V, Miettinen P, Berendt B, Garriga G, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. Springer Verlag. 2016. p. 45-49. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46131-1_10

Topy: Real-time story tracking via social tags

Abstract

Publication series

Conference

UN SDGs

Keywords

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