demonstration

TwitterMonitor: trend detection over the twitter stream

Authors:

Michael Mathioudakis,

Nick KoudasAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 1155 - 1158

https://doi.org/10.1145/1807167.1807306

Published: 06 June 2010 Publication History

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Abstract

We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend.

We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario.

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Cited By

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Yamamoto SKando NSatoh T(2025)Life aspect inference of tweets based on probability distributionWeb Intelligence10.3233/WEB-17035215:1(55-65)Online publication date: 3-Feb-2025
https://doi.org/10.3233/WEB-170352
Au A(2025)How Elites Invigorate Emotionality and Extremity in Digital NetworksSocial Science Computer Review10.1177/0894439324124742743:1(48-66)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1177/08944393241247427
Han ZShi LLiu LJiang LFang JLin FZhang JPanneerselvam JAntonopoulos N(2024)A Survey on Event Tracking in Social Media Data StreamsBig Data Mining and Analytics10.26599/BDMA.2023.90200217:1(217-243)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020021
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TwitterMonitor: trend detection over the twitter stream

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Published In

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

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]

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Published: 06 June 2010

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SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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Au A(2025)How Elites Invigorate Emotionality and Extremity in Digital NetworksSocial Science Computer Review10.1177/0894439324124742743:1(48-66)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1177/08944393241247427
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https://doi.org/10.26599/BDMA.2023.9020021
Lingham N SParameswari MP KPriya TRajesh GJagannathan J(2024)Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493471(1-6)Online publication date: 22-Feb-2024
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Basharat SAfzal SBamhdi AKhurshid SChachoo M(2023)Predicting and Mitigating the Effect of Skewness on Credibility Assessment of Social Media Content Using Machine Learning: A Twitter Case StudyInternational Journal of Computer Theory and Engineering10.7763/IJCTE.2023.V15.133815:3(101-110)Online publication date: 2023
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Monitoring Trends on Facebook

Detection and Analysis of Trend Topics for Global Scientific Literature Using Feature Selection Based on Gini-Index

SigniTrend: scalable detection of emerging topics in textual streams by hashed significance thresholds

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