Unsupervised event tracking by integrating twitter and instagram

Shiguang Wang, Prasanna Giridhar, Lance Kaplan, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper proposes an unsupervised framework for tracking real world events from their traces on Twitter and Instagram. Empirical data suggests that event detection from Instagram streams errs on the false-negative side due to the relative sparsity of Instagram data (compared to Twitter data), whereas event detection from Twitter can sutter from false-positives, at least if not paired with careful analysis of tweet content. To tackle both problems simultaneously, we design a unified unsupervised algorithm that fuses events detected originally on Instagram (called I-events) and events detected originally on Twitter (called T-events), that occur in adjacent periods, in an attempt to combine the benefits of both sources while eliminating their individual disadvantages. We evaluate the proposed framework with real data crawled from Twitter and Instagram. The results indicate that our algorithm significantly improves tracking accuracy compared to baselines.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 2nd International Workshop on Social Sensing, SocialSens 2017 (part of CPS Week)
PublisherAssociation for Computing Machinery, Inc
Number of pages6
ISBN (Electronic)9781450349772
StatePublished - Apr 18 2017
Event2nd International Workshop on Social Sensing, SocialSens 2017 - Pittsburgh, United States
Duration: Apr 21 2017 → …


Other2nd International Workshop on Social Sensing, SocialSens 2017
CountryUnited States
Period4/21/17 → …

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Unsupervised event tracking by integrating twitter and instagram'. Together they form a unique fingerprint.

Cite this