Today, a tremendous source of spatio-temporal data is user generated, so-called volunteered geographic information (VGI). Among the many VGI sources, microblogged services, such as Twitter, are extensively used to disseminate information on a near real-time basis. Interest in analysis of microblogged data has been motivated to date by many applications ranging from trend detection, early disaster warning, to urban management and marketing. One important analysis perspective in understanding microblogged data is based on the notion of drift, considering a gradual change of real world phenomena observed across space, time, content, or a combination thereof. The scientific contribution provided by this paper is the presentation of a systematic framework that utilises on the one hand a Kernel Density Estimation (KDE) to detect hotspot clusters of Tweeter activities, which are episodically sequential in nature. These clusters help to derive spatial trajectories. On the other hand we introduce the concept of drift that characterises these trajectories by looking into changes of sentiment and topics to derive meaningful information. We apply our approach to a Twitter dataset comprising 26,000 tweets. We demonstrate how phenomena of interest can be detected by our approach. As an example, we use our approach to detect the locations of Lady Gaga’s concert tour in 2013. A set of visualisations allows to analyse the identified trajectories in space, enhanced by optional overlays for sentiment or other parameters of interest.

1. Moving on Twitter: Using Episodic Hotspot and DriftAnalysis to Detect and Characterise Spatial Trajectories
Hansi Senaratne | Arne Broering| Tobias Schreck|Dominic Lehle
LBSN ‘ 2014
SIGSPATIAL, Dallas

2. Introduction
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Geography
Explicit
Implicit
Explicitly volunteered
Implicitly volunteered
Source: Cragliaet al. 2012 “A typology of VGI”
I’m going to see the Lady Gaga concert in New York
Heavy traffic in Dallas route 65, had to take a left at junction 4

3. Introduction
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(x1,y1,t1)
(x2,y2,t2)
(x3,y3,t3)
(x4,y4,t4)
(x5,y5,t5)
(x6,y6,t6)
(x7,y7,t7)
Figure: A trajectory segment
Episodic Sequential Hotspot
analysis
(series of separate events
that follow a logical order)
+
Drift analysis
(gradual change of
phenomena observed over
various modalities)

4. A Framework for Detecting MeaningfulTrajectories in Microblogged Data

5. Database & Application Structure
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6. Application
•Lady Gaga North American “Born This Way Ball” tour 2013
•> 41.2 Mio followers
•Tour time frame: 11.1-16.3
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Source: billboard© entertainment website

7. Framework: filtering the dataset
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GPS or Wi-Fi
Lady Gaga concert
‘Lady Gaga’
26,000 Tweets
11.1.-16.3. 2013
tf-idf
frequently used keywords (ranked*)
Selection of a keyword
* 1.Artpop 2.ladyGaga 3.Concert 4.Starlight 5.Nowplaying 6.Brazil 7.KEPO 8.Center 9.Rihanna 10.show

8. Framework: sampling the dataset
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Each Tweet associated to the closest larger city with population > 100,000
Remove noise

9. Framework: (episodic)Hotspot Cluster Analysis (I)
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Kernel Density Estimation for each day
Higher activity of tweeters are clustered around particular locations
Surface based on the distribution and density of Tweet geotags
Hotspots corresponding to the keyword

10. Framework: (episodic)Hotspot Cluster Analysis (II)
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11. Framework: parameterisationof the trajectory
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Credibility indicators by Castillo et al. 2011
Status count, follower count, list count, friend count
Trustworthy tweets, smoothing a trajectory

12. Framework: trajectory approximation (I)
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Avg. time at each hotspot cluster
Avg. time at each hotspot cluster is connected sequentially

13. Framework: trajectory approximation (II)
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Before averaging the time
After averaging the time

14. Framework: characterisingtrajectories through drifts
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Each Tweet is classified for positive, negative, neutral polarity based on Sander’s*dataset
Identify relevant keywords at each hotspot
Average the sentiments of each Tweet
Based on Wordle
* Saifet al. 2013 “Evaluation datasets for twitter sentiment analysis a survey and a new dataset”
Train a classifier using LingPipeJava toolkit

15. System Overview
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16. Results: actual route over the approximated route
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17. Results: sentiment drift reveals a cancellation of the tour
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18. Evaluation of Performance
•Specs:
•Google Chrome on a Dell XPS Laptop with
•Intel Core i7 Q 720 @ 1.6 GHz (2.8 GHz max)
•8 GB RAM and a ATI Mobility Radeon HD 4670.
Processing calculations and rendering for 1000 tweets ~ 4-6 seconds
•Real-time calculation of tf-idfwordcloud, lack of threads on HTML5 and Javascriptslows down the system when looking at >20,000 tweets
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19. Future Work
•Evaluation
•Improve the trajectory generationedge bundling techniques
•Application to automated filtering and ranking of trajectories
•Enable visual exploration at different spatial/temporal resolutions
•Text similarity drift analysis
•Different structures of trajectories
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20. Thank You!
Hansi Senaratnehttp://www.vis.uni- konstanz.de/mitglieder/senaratne/
@viharaa
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