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Analysis and forecasting of trending topics in online media streams

Authors:

Andreas DengelAuthors Info & Claims

MM '13: Proceedings of the 21st ACM international conference on Multimedia

Pages 907 - 916

https://doi.org/10.1145/2502081.2502117

Published: 21 October 2013 Publication History

Abstract

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior.

We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.

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Cited By

Takedomi YNakayama YSuda TUno THashimoto TToyoda MYoshinaga NKitsuregawa MKobayashi R(2024)Evolution of COVID-19 Vaccination-related Topics on TwitterTwitterにおける新型コロナワクチンに関する話題の変化Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.39-5_C-N9339:5(C-N93_1-10)Online publication date: 1-Sep-2024
https://doi.org/10.1527/tjsai.39-5_C-N93
Saleh A(2024)Attend2trend: Attention-Based LSTM Model for Detecting and Forecasting of Trending TopicsAdvanced Data Mining and Applications10.1007/978-981-96-0847-8_10(142-154)Online publication date: 14-Dec-2024
https://doi.org/10.1007/978-981-96-0847-8_10
Yang MSeklouli AZhang HRen LYu XOuzrout Y(2023)Popularity Prediction of Micro-Blog Hot Topics Based on Time-Series Data2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)10.1109/SKIMA59232.2023.10387341(199-204)Online publication date: 8-Dec-2023
https://doi.org/10.1109/SKIMA59232.2023.10387341
Show More Cited By

Index Terms

Analysis and forecasting of trending topics in online media streams
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '13: Proceedings of the 21st ACM international conference on Multimedia

October 2013

1166 pages

ISBN:9781450324045

DOI:10.1145/2502081

General Chairs:
Alejandro (Alex) Jaimes
Yahoo!, Spain
,
Nicu Sebe
University of Trento, Italy
,
Nozha Boujemaa
INRIA, France
,
Program Chairs:
Daniel Gatica-Perez
IDIAP & EPFL, Switzerland
,
David A. Shamma
Yahoo!, USA
,
Marcel Worring
University of Amsterdam, The Netherlands
,
Roger Zimmermann
National University of Singapore, Singapore

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 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].

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 21 October 2013

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MM '13: ACM Multimedia Conference

October 21 - 25, 2013

Barcelona, Spain

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MM '13 Paper Acceptance Rate 47 of 235 submissions, 20%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Takedomi YNakayama YSuda TUno THashimoto TToyoda MYoshinaga NKitsuregawa MKobayashi R(2024)Evolution of COVID-19 Vaccination-related Topics on TwitterTwitterにおける新型コロナワクチンに関する話題の変化Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.39-5_C-N9339:5(C-N93_1-10)Online publication date: 1-Sep-2024
https://doi.org/10.1527/tjsai.39-5_C-N93
Saleh A(2024)Attend2trend: Attention-Based LSTM Model for Detecting and Forecasting of Trending TopicsAdvanced Data Mining and Applications10.1007/978-981-96-0847-8_10(142-154)Online publication date: 14-Dec-2024
https://doi.org/10.1007/978-981-96-0847-8_10
Yang MSeklouli AZhang HRen LYu XOuzrout Y(2023)Popularity Prediction of Micro-Blog Hot Topics Based on Time-Series Data2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)10.1109/SKIMA59232.2023.10387341(199-204)Online publication date: 8-Dec-2023
https://doi.org/10.1109/SKIMA59232.2023.10387341
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