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Unsupervised Event Tracking by Integrating Twitter and Instagram

Published: 18 April 2017 Publication History

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 suffer 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.

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cover image ACM Conferences
SocialSens'17: Proceedings of the 2nd International Workshop on Social Sensing
April 2017
97 pages
ISBN:9781450349772
DOI:10.1145/3055601
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: 18 April 2017

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CPS Week '17
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CPS Week '17: Cyber Physical Systems Week 2017
April 18 - 21, 2017
PA, Pittsburgh, USA

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  • (2023)Towards comparable event detection approaches development in social mediaProcedia Computer Science10.1016/j.procs.2022.11.015212:C(312-321)Online publication date: 20-Jan-2023
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