CoChat
Sign Up
Cited by 5Topic ModelingAdvanced Clustering Algorithms ResearchComputational and Text Analysis Methods

The DOI matches its Crossref record.

SocialStories: Segmenting Stories within Trending Twitter Topics

Jaidka, Kokil, Ramachandran, Kaushik, Gupta, Prakhar, Rustagi, Sajal·2016·Proceedings of the 3rd IKDD Conference on Data Science, 2016
DOI 10.1145/2888451.2888453
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1145/2888451.2888453

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 16

  1. 10.1109/HICSS.2014.282 · DOI
  2. 10.5555/772260 · DOI
  3. 10.1145/2247596.2247636 · DOI
  4. 10.5555/944919.944937 · DOI
  5. 10.1145/1814245.1814249 · DOI
  6. C. de Mazancourt and U. Dieckmann . TradeâĂŘoff geometries and frequencyâĂŘdependent selection. The American Naturalist , 164 ( 6 ): 765 -- 778 , 2004 . C. de Mazancourt and U. Dieckmann. TradeâĂŘoff geometries and frequencyâĂŘdependent selection. The American Naturalist, 164(6):765--778, 2004.
  7. 10.1145/2063576.2063686 · DOI
  8. H. Koga and T. Taniguchi . Developing a user recommendation engine on twitter using estimated latent topics . In Human-Computer Interaction. Design and Development Approaches , pages 461 -- 470 . Springer , 2011 . H. Koga and T. Taniguchi. Developing a user recommendation engine on twitter using estimated latent topics. In Human-Computer Interaction. Design and Development Approaches, pages 461--470. Springer, 2011. · DOI
  9. C. D. Manning , P. Raghavan , and H. Schütze . Introduction to information retrieval , volume 1 . Cambridge university press Cambridge , 2008 . C. D. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval, volume 1. Cambridge university press Cambridge, 2008. · DOI · Paper page
  10. 10.1145/1807167.1807306 · DOI
  11. 10.1145/2484028.2484166 · DOI
  12. 10.1145/1367497.1367510 · DOI
  13. H. Sayyadi , M. Hurst , and A. Maykov . Event detection and tracking in social streams . In ICWSM , 2009 . H. Sayyadi, M. Hurst, and A. Maykov. Event detection and tracking in social streams. In ICWSM, 2009.
  14. C. Shirky . It is not information overload. it is filter failure . 2008 . C. Shirky. It is not information overload. it is filter failure. 2008.
  15. J. Weng and B.-S. Lee . Event detection in twitter. ICWSM , 11 : 401 -- 408 , 2011 . J. Weng and B.-S. Lee. Event detection in twitter. ICWSM, 11:401--408, 2011.
  16. 10.1007/978-3-642-20161-5_34 · DOI · Paper page

Cited by

Sorted by citations

See all 5

Metadata

Authors
Jaidka, K., Ramachandran, K., Gupta, P., & Rustagi, S.
Affiliations
  • Big Data Experience Lab, Adobe Systems Pvt Ltd, Bengaluru, India
  • Adobe Systems Pvt Ltd, Bengaluru India
ORCID
Venue
Proceedings of the 3rd IKDD Conference on Data Science, 2016
Publisher
ACM
Published
2016-03-13
Pages
1-7
DOI
10.1145/2888451.2888453
OpenAlex
W2302305679
Cited by
5
References
0
Citations by year
2023: 1 · 2021: 1 · 2018: 2 · 2016: 1
FWCI
0.52
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
Joint International Conference on Data Science & Management of Data (CODS-COMAD) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
incremental clustering, Twitter, trending topics, marketing conference

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: SocialStories: Segmenting Stories within Trending Twitter Topics. https://app.cochat.ai/papers/10.1145/2888451.2888453
The paper
Jaidka, K., Ramachandran, K., Gupta, P., & Rustagi, S. (2016). SocialStories: Segmenting Stories within Trending Twitter Topics. Proceedings of the 3rd IKDD Conference on Data Science, 2016. https://doi.org/10.1145/2888451.2888453