research-article

SocialStories: Segmenting Stories within Trending Twitter Topics

Authors:

Kaushik Ramachandran,

Sajal RustagiAuthors Info & Claims

CODS '16: Proceedings of the 3rd IKDD Conference on Data Science, 2016

Article No.: 1, Pages 1 - 7

https://doi.org/10.1145/2888451.2888453

Published: 13 March 2016 Publication History

Abstract

This study present SocialStories - a system based on incremental clustering for streaming tweets, for identifying fine-grained stories within a broader trending topic on Twitter. The contributions include a novel tf-metric, called the inverse cluster frequency, and a decay weighting for entities. We present our experiments on 0.19 million tweets posted in June 2014, revolving around the mentions of a software brand before, during and after a marketing conference and a software release. The novelty of our work is the text-based similarity calculation metrics, including a new similarity metric, called the inverse cluster frequency, and time-specific metrics that allow for the decay of old entities with the passage of time and preserve the homogeneity and the freshness of stories. We report improved performance and higher recall of 80%, against the gold standard (posthoc journalistic reports), as compared to LDA-, and Wavelet-based systems. Our algorithm is able to cluster 80% of all tweets into story-based clusters, which are 86% pure. It also enables earlier detection of trending stories than manual reports, and is far more accurate in identifying fine-grained stories within sub-topics as compared to baseline systems.

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Cited By

Bodaghi ASchmitt KWatine PFung B(2024)A Literature Review on Detecting, Verifying, and Mitigating Online MisinformationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.328903111:4(5119-5145)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3289031
Dhanya NHarish U(2021)Detection of Rumors in Tweets Using Machine Learning TechniquesAdvances in Automation, Signal Processing, Instrumentation, and Control10.1007/978-981-15-8221-9_289(3095-3111)Online publication date: 5-Mar-2021
https://doi.org/10.1007/978-981-15-8221-9_289
Zubiaga AAker ABontcheva KLiakata MProcter R(2018)Detection and Resolution of Rumours in Social MediaACM Computing Surveys10.1145/316160351:2(1-36)Online publication date: 20-Feb-2018
https://dl.acm.org/doi/10.1145/3161603

Index Terms

SocialStories: Segmenting Stories within Trending Twitter Topics
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Other conferences

CODS '16: Proceedings of the 3rd IKDD Conference on Data Science, 2016

March 2016

122 pages

ISBN:9781450342179

DOI:10.1145/2888451

General Chairs:
Madhav Marathe
Virginia Polytechnic Institute and State, University, Blacksburg (VA), USA
,
Mukesh Mohania
IBM India Research Lab, New Delhi, INDIA
,
Program Chairs:
Mausam
Indian Institute of Technology, Delhi, INDIA
,
Prateek Jain
Microsoft Research Lab, Bangalore, INDIA

Copyright © 2016 ACM.

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Published: 13 March 2016

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CODS '16

CODS '16: IKDD Conference on Data Science, 2016

March 13 - 16, 2016

Pune, India

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Cited By

Bodaghi ASchmitt KWatine PFung B(2024)A Literature Review on Detecting, Verifying, and Mitigating Online MisinformationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.328903111:4(5119-5145)Online publication date: Aug-2024
https://doi.org/10.1109/TCSS.2023.3289031
Dhanya NHarish U(2021)Detection of Rumors in Tweets Using Machine Learning TechniquesAdvances in Automation, Signal Processing, Instrumentation, and Control10.1007/978-981-15-8221-9_289(3095-3111)Online publication date: 5-Mar-2021
https://doi.org/10.1007/978-981-15-8221-9_289
Zubiaga AAker ABontcheva KLiakata MProcter R(2018)Detection and Resolution of Rumours in Social MediaACM Computing Surveys10.1145/316160351:2(1-36)Online publication date: 20-Feb-2018
https://dl.acm.org/doi/10.1145/3161603

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