research-article

Spatio-temporal meme prediction: learning what hashtags will be popular where

Authors:

Krishna Y. Kamath,

James CaverleeAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1341 - 1350

https://doi.org/10.1145/2505515.2505579

Published: 27 October 2013 Publication History

Abstract

In this paper, we tackle the problem of predicting what online memes will be popular in what locations. Specifically, we develop data-driven approaches building on the global footprint of 755 million geo-tagged hashtags spread via Twitter. Our proposed methods model the geo-spatial propagation of online information spread to identify which hashtags will become popular in specific locations. Concretely, we develop a novel reinforcement learning approach that incrementally updates the best geo-spatial model. In experiments, we find that the proposed method outperforms alternative linear regression based methods.

References

[1]

Universal transverse mercator coordinate system, November 2012.

[2]

L. Backstrom, J. Kleinberg, R. Kumar, and J. Novak. Spatial variation in search engine queries. WWW '08.

Digital Library

[3]

L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. WWW '10.

Digital Library

[4]

A. Brodersen, S. Scellato, and M. Wattenhofer. Youtube around the world: geographic popularity of videos. WWW '12.

Digital Library

[5]

Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach to geo-locating twitter users. CIKM '10.

Digital Library

[6]

E. Cunha, G. Magno, G. Comarela, V. Almeida, M. A. Gonçalves, and F. Benevenuto. Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. LSM '11.

Digital Library

[7]

N. Dalvi, R. Kumar, and B. Pang. Object matching in tweets with spatial models. WSDM '12.

Digital Library

[8]

D. Davidov, O. Tsur, and A. Rappoport. Enhanced sentiment learning using twitter hashtags and smileys. COLING '10.

Digital Library

[9]

J. Ding, L. Gravano, and N. Shivakumar. Computing geographical scopes of web resources. VLDB '00.

Digital Library

[10]

Foursquare. About foursquare, April 2012.

[11]

M. Gomez Rodriguez, J. Leskovec, and A. Krause. Inferring networks of diffusion and influence. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10.

Digital Library

[12]

B. A. Huberman, D. M. Romero, and F. Wu. Social Networks that Matter: Twitter Under the Microscope. Social Science Research Network Working Paper Series, Dec. 2008.

[13]

K. Y. Kamath, J. Caverlee, Z. Cheng, and D. Z. Sui. Spatial influence vs. community influence: modeling the global spread of social media. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pages 962--971, New York, NY, USA, 2012. ACM.

Digital Library

[14]

D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. KDD '03.

Digital Library

[15]

D. Kempe, J. Kleinberg, and É. Tardos. Influential nodes in a diffusion model for social networks. In IN ICALP, 2005.

Digital Library

[16]

G. Kossinets, J. Kleinberg, and D. Watts. The structure of information pathways in a social communication network. KDD '08.

Digital Library

[17]

H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? WWW '10.

Digital Library

[18]

K. Lerman and R. Ghosh. Information contagion: an empirical study of the spread of news on digg and twitter social networks. CoRR, 2010.

[19]

J. Lin, R. Snow, and W. Morgan. Smoothing techniques for adaptive online language models: topic tracking in tweet streams. KDD '11.

Digital Library

[20]

A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. An empirical study of geographic user activity patterns in foursquare. ICWSM'11.

[21]

D. M. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. WWW '11.

Digital Library

[22]

S. Scellato, A. Noulas, R. Lambiotte, and C. Mascolo. Socio-spatial properties of online location-based social networks. ICWSM'11.

[23]

W. Tobler. A computer movie simulating urban growth in the detroit region. Economic Geography, 46(2), 1970.

[24]

O. Tsur and A. Rappoport. What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities. WSDM '12.

Digital Library

[25]

J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. ICDM'2010.

Digital Library

Cited By

Yin JJia HZhou BTang TYing LYe SPeng TWu Y(2024)Blowing Seeds Across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media PostsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345618131:1(185-195)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456181
Wu YMcAreavey KLiu WMcConville R(2024)A Comparative Analysis of Information Cascade Prediction Using Dynamic Heterogeneous and Homogeneous GraphsComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_14(168-179)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_14
Saha AGanguly N(2021)Modeling Inter-process Dynamics in Competitive Temporal Point ProcessesJournal of the Indian Institute of Science10.1007/s41745-021-00224-6101:3(455-484)Online publication date: 9-Jul-2021
https://doi.org/10.1007/s41745-021-00224-6
Show More Cited By

Index Terms

Spatio-temporal meme prediction: learning what hashtags will be popular where
1. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures

Recommendations

Spatio-temporal dynamics of online memes: a study of geo-tagged tweets
WWW '13: Proceedings of the 22nd international conference on World Wide Web

We conduct a study of the spatio-temporal dynamics of Twitter hashtags through a sample of 2 billion geo-tagged tweets. In our analysis, we (i) examine the impact of location, time, and distance on the adoption of hashtags, which is important for ...
A Spatio-Temporal Category Representation for Brand Popularity Prediction
ICMR '17: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

Social media has become an important tool in marketing for companies to communicate with their consumers. Firms post content and consumers express their appreciation for the brand by following them on social media and/or by liking the firm generated ...
Dynamic mapping of dense geo-tweets and web pages based on spatio-temporal analysis
SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

Twitter evidently stirred a popular trend of personal update sharing. Twitter users can be kept up to date with current information from Twitter; however, users cannot obtain the most recent information, while they browse web pages since these are not ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

Acceptance Rates

CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
315
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)2

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yin JJia HZhou BTang TYing LYe SPeng TWu Y(2024)Blowing Seeds Across Gardens: Visualizing Implicit Propagation of Cross-Platform Social Media PostsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345618131:1(185-195)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456181
Wu YMcAreavey KLiu WMcConville R(2024)A Comparative Analysis of Information Cascade Prediction Using Dynamic Heterogeneous and Homogeneous GraphsComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_14(168-179)Online publication date: 29-Feb-2024
https://doi.org/10.1007/978-3-031-53503-1_14
Saha AGanguly N(2021)Modeling Inter-process Dynamics in Competitive Temporal Point ProcessesJournal of the Indian Institute of Science10.1007/s41745-021-00224-6101:3(455-484)Online publication date: 9-Jul-2021
https://doi.org/10.1007/s41745-021-00224-6
Sun GTang TPeng TLiang RWu Y(2017)SocialWaveACM Transactions on Intelligent Systems and Technology10.1145/31067759:2(1-23)Online publication date: 23-Oct-2017
https://dl.acm.org/doi/10.1145/3106775
Niu WCaverlee JLu HKamath KSubrahmanian VRokne JKumar RCaverlee JTong H(2016)Community-based geospatial tag estimationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192475(279-286)Online publication date: 18-Aug-2016
https://dl.acm.org/doi/10.5555/3192424.3192475
Mishra SRizoiu MXie LMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Feature Driven and Point Process Approaches for Popularity PredictionProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983812(1069-1078)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983812
Ge HCaverlee JZhang NSquicciarini AMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete InformationProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983782(1493-1502)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983782
Maity SSaraf RMukherjee AGergle DMorris MBjørn PKonstan J(2016)#Bieber + #Blast = #BieberBlastProceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing10.1145/2818048.2820019(50-63)Online publication date: 27-Feb-2016
https://dl.acm.org/doi/10.1145/2818048.2820019
Niu WCaverlee JLu HKamath K(2016)Community-based geospatial tag estimation2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2016.7752246(279-286)Online publication date: Aug-2016
https://doi.org/10.1109/ASONAM.2016.7752246
Yan YTan ZGao XTang SChen G(2016)STH-BassProceedings, Part II, of the 21st International Conference on Database Systems for Advanced Applications - Volume 964310.1007/978-3-319-32049-6_2(18-32)Online publication date: 16-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-32049-6_2
Show More Cited By

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents