Abstract
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 language | English (US) |
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Title of host publication | Proceedings - 2017 2nd International Workshop on Social Sensing, SocialSens 2017 (part of CPS Week) |
Publisher | Association for Computing Machinery, Inc |
Pages | 81-86 |
Number of pages | 6 |
ISBN (Electronic) | 9781450349772 |
DOIs | |
State | Published - Apr 18 2017 |
Event | 2nd International Workshop on Social Sensing, SocialSens 2017 - Pittsburgh, United States Duration: Apr 21 2017 → … |
Other
Other | 2nd International Workshop on Social Sensing, SocialSens 2017 |
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Country | United States |
City | Pittsburgh |
Period | 4/21/17 → … |
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications