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TOTEM: Personal Tweets Summarization on Mobile Devices

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SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1305 - 1308

https://doi.org/10.1145/3077136.3084138

Published: 07 August 2017 Publication History

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Abstract

Tweets summarization aims to find a group of representative tweets for a specific topic. In recent times, there have been several research efforts toward devising a variety of techniques to summarize tweets in Twitter. However, these techniques are either not personal (i.e., consider only tweets in the timeline of a specific user) or are too expensive to be realized on a mobile device. Given that 80% of active Twitter users access the site on mobile devices, in this demonstration we present a lightweight, personalized, on-demand, topic modeling-based tweets summarization engine called TOTEM, designed for such devices. Specifically, TOTEM summarizes most recent tweets on a user's timeline and enables her to visualize and navigate representative topics and associated tweets in a user-friendly tap-and-swipe manner.

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Cited By

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Lin COuyang ZWang XLi HHuang Z(2021)Preserve Integrity in Realtime Event SummarizationACM Transactions on Knowledge Discovery from Data10.1145/344234415:3(1-29)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.1145/3442344
Gao XZheng ZChu QTang SChen GDeng Q(2020)Popularity Prediction for Single Tweet based on Heterogeneous Bass ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2952856(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2019.2952856
Chin JBhowmick SJatowt A(2019)On‐demand recent personal tweets summarization on mobile devicesJournal of the Association for Information Science and Technology10.1002/asi.2413770:6(547-562)Online publication date: 22-Apr-2019
https://dl.acm.org/doi/10.1002/asi.24137
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Index Terms

TOTEM: Personal Tweets Summarization on Mobile Devices
1. Information systems
  1. Information systems applications
    1. Collaborative and social computing systems and tools

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Published In

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

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Published: 07 August 2017

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SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

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

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Cited By

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Lin COuyang ZWang XLi HHuang Z(2021)Preserve Integrity in Realtime Event SummarizationACM Transactions on Knowledge Discovery from Data10.1145/344234415:3(1-29)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.1145/3442344
Gao XZheng ZChu QTang SChen GDeng Q(2020)Popularity Prediction for Single Tweet based on Heterogeneous Bass ModelIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2952856(1-1)Online publication date: 2020
https://doi.org/10.1109/TKDE.2019.2952856
Chin JBhowmick SJatowt A(2019)On‐demand recent personal tweets summarization on mobile devicesJournal of the Association for Information Science and Technology10.1002/asi.2413770:6(547-562)Online publication date: 22-Apr-2019
https://dl.acm.org/doi/10.1002/asi.24137
Lin LLin CLai Y(2018)Realtime Event Summarization from Tweets with Inconsistency DetectionConceptual Modeling10.1007/978-3-030-00847-5_41(555-570)Online publication date: 26-Sep-2018
https://doi.org/10.1007/978-3-030-00847-5_41

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Topic and sentiment aware microblog summarization for twitter

Summarizing web forum threads based on a latent topic propagation process

Tweet Sentiment Analyzer: Sentiment Score Estimation Method for Assessing the Value of Opinions in Tweets

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