The document presents a PhD thesis defending research on inferring the geolocation of non-geotagged tweets at a fine-grained level to enhance applications like traffic incident detection. It argues that previous methods were limited by working only at coarse levels and proposes a novel approach using individual tweet analysis, majority voting, and learning to rank methods to improve accuracy. The findings demonstrate that these approaches significantly improve the geolocation accuracy of tweets, which is vital for effective emergency management and traffic monitoring.

1. Inferring the Geolocation of Tweets at a
Fine-Grained Level
Jorge David Gonzalez Paule
PhD Thesis Defence – 7th November 2018
Supervisors: Iadh Ounis, Craig Macdonald, Yashar Moshfeghi

2. Motivations
• Only 1% of the Twitter stream contains geographical information [Graham et al.,
2014].
• This sample of tweets is insufficient for several applications such as Disaster
and Emergency Management or Traffic Incident Detection (Chapter 6)
• Inferring the geolocation of non-geotagged tweets can increase the sample of
actionable geotagged data.
• Previous tweet geolocalisation approaches work at a coarse-grained level
(country or city level) [Eisenstein et al., 2010a; Han and Cook, 2013; Kinsella et al., 2011; Schulz et al.,
2013a]
• We aim to infer the geolocation of tweets at a fine-grained level (street or
neighbour level).
In this thesis, we aim to achieve an error distance of at most 1 km!
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3. Thesis Statement
“The geolocalisation of non-geotagged tweets at a fine-grained
level can be achieved by exploiting the characteristics of already
available individual finely-grained geotagged tweets.”
Chapter 3
Chapter 4
Chapter 5
Chapter 6
H4: By geolocalising non-geotagged tweets we can obtain a more representative sample of
geotagged data and, therefore, improve the effectiveness of the traffic incident detection task.
H3: By improving the ranking of geotagged tweets with respect to a given non-geotagged tweet, […]
we can obtain a higher number of other fine-grained predictions.
H2: The predictability of the geolocation of tweets at a fine-grained level is given by the correlation
between their content similarity and geographical distance to finely-grained geotagged tweets.
H1: By considering geotagged tweets individually […] we can improve the performance of fine-
grained geolocalisation.
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4. Thesis Structure
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Fine-Grained Geolocalisation of Tweets.
Application: Traffic Incident Detection
Ranking Approach
Ranking Approach +
Majority Voting
Learning to Rank +
Majority Voting
Sophisticated but
more accurate(1km)
Simple but efficient
• Evaluate effectiveness
• Assess the generalisation of our approaches on a new
dataset
Evolution of Techniques
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5. Data and Metrics
Datasets
• 3 different datasets of geotagged tweets
−Chicago (March 2016)
−New York (March 2016)
−Chicago (July 2016)
Metrics
• Average Error Distance (AED)
−Distance on Earth between the predicted and the real location
• Accuracy@1km
−The fraction of predicted locations that lie within a radius of 1 km from the real
location
• Coverage
−The fraction of tweets for which our approach finds a geolocation
Chapters 3,4 and 5
Chapter 6
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6. Motivation
• Existing approaches in the literature have limitations when they are adapted
to work at a fine-grained level [Kinsella et al. (2011), Paraskevopoulos et al. (2015)].
−To represent a location, these approaches aggregate the text of the tweets in this
location into a virtual document.
−We postulate that this aggregation approach can lead to a loss of important
evidence.
Chapter 3
H1:
By considering geotagged tweets individually we can preserve the
evidence lost when adapting previous approaches at a fine-
grained level, and thus we can improve the performance of fine-
grained geolocalisation.
Enabling Fine-Grained
Geolocalisation
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7. Chapter 3
Geographical Area: Grid of squares of a
pre-defined size (1km)
Ranking of Documents
Enabling Fine-Grained
Geolocalisation
Aggregated
Individual
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8. Chapter 3 Enabling Fine-Grained
Geolocalisation
Conclusions
• Individual outperforms Aggregated (accuracy@1km):
−from 50.67% to 55.20% in Chicago
−from 45.40% to 48.46% in New York
• Key Findings:
−IDF is the best performing retrieval model.
−Document Frequency (DF) is the most important feature when using
individual tweets.
−The Aggregated approach loses in accuracy when transforming DF to term
frequency.
Contributions
• A novel ranking approach for fine-grained geolocalisation (Individual),
compared against the baseline (Aggregated).
• An investigation into the performance issues of the existing SOTA approaches
(Aggregated).
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9. Majority Voting For Fine-
Grained Geolocalisation
Chapter 4
Motivation
• The approach of Chapter 3 obtains an AED of 4.693 km (Chicago)
−Not enough. We aim for an AED of 1 km.
• This approach considers only the similarity evidence for prediction (always
returns the Top-1 geotagged tweet)
• We postulate that in some cases the similarity of the tweets does not always
correlate with geographical distance, thus we cannot predict a location in
such cases.
• We aim to exploit the geographical evidence encoded within the Top-N
geotagged tweets.
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H2:
The predictability of the geolocation of tweets at a fine-grained level is
given by the correlation between their content similarity and geographical
distance to finely-grained geotagged tweets.

10. Contributions
• A novel approach that uses a weighted majority voting to exploit the
geographical evidence encoded within the Top-N geotagged tweets in the
ranking.
Chapter 4 Majority Voting For Fine-
Grained Geolocalisation
Conclusions
• AED is markedly reduced:
−4.694 km to 1.602 km in Chicago
−4.972 km to 1.448 km in New York
• Key Findings:
−Trade-off between AED and Coverage (weighted Majority Voting)
−As the value N of the Top-N increases we observe:
• Lower AED
• But also Lower Coverage
.
.
.
Ranking of Geotagged Tweets
(Chapter 3) Majority
Voting
Top-N
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11. Learning to GeolocaliseChapter 5
H3:
By improving the ranking of geotagged tweets with respect to a given
non-geotagged tweet, […] we can obtain a higher number of other fine-
grained predictions.
Motivation
• The approaches of Chapters 3 & 4 use traditional retrieval models
−Similarity is based only on document frequency information (IDF weighting).
• Considering only IDF can limit the quality of the Top-N geotagged tweets.
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12. Contributions
• A novel learning to rank-based approach that re-ranks geotagged tweets
−Targeting their geographical proximity to a given non-geotagged tweet.
• We propose a set of 28 features for learning.
Chapter 5 Learning to Geolocalise
Experiments
• Experiment with several learning to rank algorithms
−MART, Random Forest, RankNet, AdaRank, ListNet and LambdaMART
• Explore the best combination of features
−Document features, query features and query-dependent features.
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13. Chapter 5 Learning to Geolocalise
Conclusions
• LambdaMART is the best algorithm for fine-grained geolocalisation.
• A better ranking result does improve geoloalisation
−Increased accuracy while increasing coverage (best configuration @Top-13):
−AED: from 1.490 km to 1.441 km
−Coverage: from 31.88% to 46.01%
• Best combination of features:
−Extracted from query-tweet .
−Query-dependent: relation between query and doc tweets.
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14. Application: Traffic Incident
Detection
Chapter 6
H4:
By geolocalising non-geotagged tweets we can obtain a more
representative sample of geotagged data and, therefore, improve the
effectiveness of the traffic incident detection task.
Motivation
• We aim to assess the generalisation of our approaches.
• It is important to:
−Identify traffic incident-related content.
−Know the precise location of the tweets to locate incidents.
• There are detection rates problems due to small sample size (use only 1% of
geotagged tweets) (Gu et al., 2016; Mai and Hranac, 2013)
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15. Application: Traffic Incident
Detection
Chapter 6
Geolocalised
+
Geotagged
Geotagged Alone
Detection Rate
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16. Application: Traffic Incident
Detection
Chapter 6
Geotagged Alone
Geolocalised + Geotagged
(High Acc. Config)
Geolocalised + Geotagged
(High Coverage Config)
Accuracy@1km
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17. Contributions
• Demonstrated the usefulness of our approaches in a traffic incident detection
task.
• We showed improvements in the performance of the traffic incident
detection task.
Chapter 6
Application: Traffic Incident
Detection
Conclusions
• Expanding the sample with new geolocalised tweets increases the
performance of the traffic incident detection task.
• The geolocalised tweets are located close to the location of the real
incidents.
• Our fine-grained geolocalisation approaches do seem to generalise
− Consistency with behaviour observed in the previous chapters on other datasets
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18. Concluding Remarks
Across all the chapters in this thesis we have….
• Addressed the fine-grained tweet geolocalisation problem.
• Alleviated the problem of the existing state-of-the-art
approaches when working at a fine-grained level
• Proposed an suite of techniques that
−Effectively infer the geolocation of tweets at a fine-grained
level
−Provide different trade-offs between accuracy and coverage
• Showed the effectiveness and generalisation of our
approaches on a traffic incident detection task
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19. Publication Breakdown
• “On fine-grained geolocalisation of tweets”. ICTIR 2017.
• “On fine-grained geolocalisation of tweets and real-time traffic incident
detection”. Information Processing & Management. 2018.
• “Beyond geotagged tweets: exploring the geolocalisation of tweets for
transportation applications”. In Transportation Analytics in the Era of Big
Data, Springer. 2018.
• “Learning to Geolocalise Tweets at a Fine-Grained Level”. In CIKM 2018.
Also contributed to Geographical and Social Sciences
• "Sensing spatiotemporal patterns in urban areas: analytics and visualizations
using the integrated multimedia city data platform“. Built Environment 42.3.
2016.
• "Spatial analysis of usersgenerated ratings of yelp venues“. In Open
Geospatial Data, Software and Standards. 2017.
5 research publications
Ch. 6
Ch. 5
Ch. 3
Ch. 4
Ch. 4
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20. Thanks for listening!
Glad to answer your
questions
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