On the evaluation of tweet timeline generation task

Walid Magdy, Tamer Elsayed, Maram Hasanain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Tweet Timeline Generation (TTG) task aims to generate a timeline of relevant but novel tweets that summarizes the development of a given topic. A typical TTG system first retrieves tweets then detects novel tweets among them to form a timeline. In this paper, we examine the dependency of TTG on retrieval quality, and its effect on having biased evaluation. Our study showed a considerable dependency, however, ranking systems is not highly affected if a common retrieval run is used.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages648-653
Number of pages6
Volume9626
ISBN (Print)9783319306704
DOIs
Publication statusPublished - 2016
Event38th European Conference on Information Retrieval Research, ECIR 2016 - Padua, Italy
Duration: 20 Mar 201623 Mar 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9626
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other38th European Conference on Information Retrieval Research, ECIR 2016
CountryItaly
CityPadua
Period20/3/1623/3/16

Fingerprint

Evaluation
Retrieval
Biased
Ranking

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Magdy, W., Elsayed, T., & Hasanain, M. (2016). On the evaluation of tweet timeline generation task. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9626, pp. 648-653). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9626). Springer Verlag. https://doi.org/10.1007/978-3-319-30671-1_48

On the evaluation of tweet timeline generation task. / Magdy, Walid; Elsayed, Tamer; Hasanain, Maram.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9626 Springer Verlag, 2016. p. 648-653 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9626).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Magdy, W, Elsayed, T & Hasanain, M 2016, On the evaluation of tweet timeline generation task. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9626, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9626, Springer Verlag, pp. 648-653, 38th European Conference on Information Retrieval Research, ECIR 2016, Padua, Italy, 20/3/16. https://doi.org/10.1007/978-3-319-30671-1_48
Magdy W, Elsayed T, Hasanain M. On the evaluation of tweet timeline generation task. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9626. Springer Verlag. 2016. p. 648-653. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-30671-1_48
Magdy, Walid ; Elsayed, Tamer ; Hasanain, Maram. / On the evaluation of tweet timeline generation task. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9626 Springer Verlag, 2016. pp. 648-653 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{1e1f21f5a314456ea6f46115df33f07b,
title = "On the evaluation of tweet timeline generation task",
abstract = "Tweet Timeline Generation (TTG) task aims to generate a timeline of relevant but novel tweets that summarizes the development of a given topic. A typical TTG system first retrieves tweets then detects novel tweets among them to form a timeline. In this paper, we examine the dependency of TTG on retrieval quality, and its effect on having biased evaluation. Our study showed a considerable dependency, however, ranking systems is not highly affected if a common retrieval run is used.",
author = "Walid Magdy and Tamer Elsayed and Maram Hasanain",
year = "2016",
doi = "10.1007/978-3-319-30671-1_48",
language = "English",
isbn = "9783319306704",
volume = "9626",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "648--653",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - On the evaluation of tweet timeline generation task

AU - Magdy, Walid

AU - Elsayed, Tamer

AU - Hasanain, Maram

PY - 2016

Y1 - 2016

N2 - Tweet Timeline Generation (TTG) task aims to generate a timeline of relevant but novel tweets that summarizes the development of a given topic. A typical TTG system first retrieves tweets then detects novel tweets among them to form a timeline. In this paper, we examine the dependency of TTG on retrieval quality, and its effect on having biased evaluation. Our study showed a considerable dependency, however, ranking systems is not highly affected if a common retrieval run is used.

AB - Tweet Timeline Generation (TTG) task aims to generate a timeline of relevant but novel tweets that summarizes the development of a given topic. A typical TTG system first retrieves tweets then detects novel tweets among them to form a timeline. In this paper, we examine the dependency of TTG on retrieval quality, and its effect on having biased evaluation. Our study showed a considerable dependency, however, ranking systems is not highly affected if a common retrieval run is used.

UR - http://www.scopus.com/inward/record.url?scp=84962508027&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962508027&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-30671-1_48

DO - 10.1007/978-3-319-30671-1_48

M3 - Conference contribution

AN - SCOPUS:84962508027

SN - 9783319306704

VL - 9626

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

SP - 648

EP - 653

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

PB - Springer Verlag

ER -