short-paper

Quantitative Information Extraction From Social Data

Authors:

Omar Alonso,

Thibault SellamAuthors Info & Claims

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Pages 1005 - 1008

https://doi.org/10.1145/3209978.3210133

Published: 27 June 2018 Publication History

Get Access

Abstract

Social data is a rich data source for identifying trends and topics of interest based on user activity. Social data also provides opportunities to collect numerical data about events like elections, sport games, disasters or economic news. We propose the problem of identifying relevant quantitative information from social data as annotations for a topic. We investigate how to extract quantitative information and perform a number of experiments and analysis with Twitter data.

References

[1]

J. Allan, R. Gupta, and V. Khandelwal. Temporal summaries of new topics. In Proc. SIGIR, pages 10--18. ACM, 2001.

Digital Library

Google Scholar

[2]

P. M. Andersen, P. J. Hayes, A. K. Huettner, L. M. Schmandt, I. B. Nirenburg, and S. P. Weinstein. Automatic extraction of facts from press releases to generate news stories. In Proc. ANLP, pages 170--177. ACL, 1992.

Digital Library

Google Scholar

[3]

H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. Gate: an architecture for development of robust hlt applications. In Proc. ACL, pages 168--175, 2002.

Digital Library

Google Scholar

[4]

M. Imran, C. Castillo, F. Diaz, and S. Vieweg. Processing social media messages in mass emergency: A survey. In CoRR. arXiv preprint: 1407.7071, 2014.

Google Scholar

[5]

H. Luhn. The automatic creation of literature abstracts. In IBM Journal of research and development, pages 159--165, 1958.

Digital Library

Google Scholar

[6]

D. Pighin, M. Cornolti, E. Alfonseca, and K. Filippova. Modelling events through memory-based, open-ie patterns for abstractive summarization. In Proc. ACL, volume 1, pages 892--901, 2014.

Crossref

Google Scholar

[7]

A. Ritter, O. Etzioni, and S. Clark. Open domain event extraction from twitter. In KDD, page 1104. ACM, 2012.

Digital Library

Google Scholar

[8]

T. Sellam and O. Alonso. Raimond: Quantitative data extraction from twitter to describe events. In Proc. ICWE - Engineering the Web in the Big Data Era, pages 251--268. 2015.

Digital Library

Google Scholar

Cited By

View all

Wang ZLi TLi Z(2023)Unsupervised Numerical Information Extraction via Exploiting Syntactic StructuresElectronics10.3390/electronics1209197712:9(1977)Online publication date: 24-Apr-2023
https://doi.org/10.3390/electronics12091977
Zhang P(2022)A Numerical Fact Extraction Method for Chinese Text2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)10.1109/SmartCloud55982.2022.00021(97-103)Online publication date: Oct-2022
https://doi.org/10.1109/SmartCloud55982.2022.00021
Ho VPal KWeikum GLi GLi ZIdreos SSrivastava D(2021)QuTE: Answering Quantity Queries from Web TablesProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452763(2740-2744)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452763
Show More Cited By

Index Terms

Quantitative Information Extraction From Social Data
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media
2. Information systems
  1. Information retrieval

Recommendations

An extraction and unification methodology for social networks data: an application to public security
iiWAS '17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services

Social media are invaded our daily life where millions of users are subscribed. Those online sites provide great tool for users to communicate with others from all over the world, share information, and express their opinion. Unfortunately, this ease of ...
Microscopic Description and Prediction of Information Diffusion in Social Media: Quantifying the Impact of Topical Interests
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

A number of recent studies of information diffusion in social media, both empirical and theoretical, have been inspired by viral propagation models derived from epidemiology. These studies model propagation of memes, i.e., pieces of information, between ...
Social Media Marketing in Luxury Retail

This study examines the potentials of social media marketing for luxury retailers. Social media marketing tactics of three luxury retail brands Barneys New York, Net-a-Porter.com, and Saks Fifth Avenue were examined across three major social media sites ...

Comments

Information & Contributors

Information

Published In

SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

June 2018

1509 pages

ISBN:9781450356572

DOI:10.1145/3209978

General Chairs:
Kevyn Collins-Thompson
University of Michigan, United States
,
Qiaozhu Mei
University of Michigan, United States
,
Program Chairs:
Brian Davison
Lehigh University, United States
,
Yiqun Liu
Tsinghua University, China
,
Emine Yilmaz
University College London, United Kingdom

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

SIGIR '18

Sponsor:

SIGIR

SIGIR '18: The 41st International ACM SIGIR conference on research and development in Information Retrieval

July 8 - 12, 2018

MI, Ann Arbor, USA

Acceptance Rates

SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
371
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wang ZLi TLi Z(2023)Unsupervised Numerical Information Extraction via Exploiting Syntactic StructuresElectronics10.3390/electronics1209197712:9(1977)Online publication date: 24-Apr-2023
https://doi.org/10.3390/electronics12091977
Zhang P(2022)A Numerical Fact Extraction Method for Chinese Text2022 IEEE 7th International Conference on Smart Cloud (SmartCloud)10.1109/SmartCloud55982.2022.00021(97-103)Online publication date: Oct-2022
https://doi.org/10.1109/SmartCloud55982.2022.00021
Ho VPal KWeikum GLi GLi ZIdreos SSrivastava D(2021)QuTE: Answering Quantity Queries from Web TablesProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452763(2740-2744)Online publication date: 9-Jun-2021
https://dl.acm.org/doi/10.1145/3448016.3452763
Ho VPal KRazniewski SBerberich KWeikum G(2021)Extracting Contextualized Quantity Facts from Web TablesProceedings of the Web Conference 202110.1145/3442381.3450072(4033-4042)Online publication date: 19-Apr-2021
https://doi.org/10.1145/3442381.3450072
Li TFang LLou JLi ZZhang DLewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)AnaSearch: Extract, Retrieve and Visualize Structured Results from Unstructured Text for Analytical QueriesProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441694(906-909)Online publication date: 8-Mar-2021
https://dl.acm.org/doi/10.1145/3437963.3441694
Lin WXu HLi JWu ZHu ZChang VWang J(2021)Deep-profiling: a deep neural network model for scholarly Web user profilingCluster Computing10.1007/s10586-021-03315-226:3(1753-1766)Online publication date: 9-Jun-2021
https://doi.org/10.1007/s10586-021-03315-2
Ho VPal KKleer NBerberich KWeikum GCaverlee JHu XLalmas MWang W(2020)Entities with QuantitiesProceedings of the 13th International Conference on Web Search and Data Mining10.1145/3336191.3371860(833-836)Online publication date: 20-Jan-2020
https://dl.acm.org/doi/10.1145/3336191.3371860
Ibrahim YRiedewald MWeikum GZeinalipour-Yazti D(2019)Bridging Quantities in Tables and Text2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00094(1010-1021)Online publication date: Apr-2019
https://doi.org/10.1109/ICDE.2019.00094
Ho VIbrahim YPal KBerberich KWeikum G(2019)Qsearch: Answering Quantity Queries from TextThe Semantic Web – ISWC 201910.1007/978-3-030-30793-6_14(237-257)Online publication date: 17-Oct-2019
https://doi.org/10.1007/978-3-030-30793-6_14

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

An extraction and unification methodology for social networks data: an application to public security

Microscopic Description and Prediction of Information Diffusion in Social Media: Quantifying the Impact of Topical Interests

Social Media Marketing in Luxury Retail