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Pairing Tweets with the Right Location
1. By
Esha and Osmar Zaïane
Alberta Machine Intelligence Institute,
University of Alberta, Edmonton, AB, Canada
Pairing Tweets with the
Right Location
2. • Introduction
• Related Work
• DigiCities
• Methodology
• Key Findings
• Limitations
• Conclusion
• Future Work
Presentation Overview
3. • The use of Twitter has become ubiquitous and it is used for various
reasons (e.g.,)
▪ Products and services promotion, information dissemination, event updates
• Tweets are becoming digital footprints of users’ expressions in real
world
• Information in tweets posted by users may have local relevance (e.g.,)
▪ It can be utilized to understand trends, and emerging emotions & sentiments
in a geographical location
• It is critical to identify the location-relevant tweets to effectively use
information posted by users in context of a geographical location
Introduction
4. • Geolocation detection is challenging to solve in the context of
Twitter (Cheng et al., 2010; Lee et al., 2014)
▪ Only a limited number of tweets are geotagged or have correct
geolocation information in a tweet's metadata records (e.g., Graham et
al., 2014; Lee et al., 2014; Watanabe et al., 2011)
▪ Data Sparsity i.e., limited tweets contain a specific city name (e.g.
Chang et al., 2012; Inkpen et al., 2015)
▪ Users may include varying granular levels of location information when
referring to a specific location (e.g., Huang et al,. 2019)
Introduction
5. • John (a hypothetical Twitter user) resides in St.
Paul, Minnesota, USA
• His profile states Minneapolis as the location (St.
Paul and Minneapolis are the twin cities)
• Currently, John is traveling to Toronto in Canada
• He is sitting in a restaurant and watching a hockey
game on TV played in Calgary, Canada
• He tweets about it – Just watched another win by
#CalgaryFlames an amazing game played
@TheSaddledome #YYC
• Calgary is the event-
related location
• Two geolocations
captured in the metadata
record are:
• Minneapolis - Twitter
profile
• Toronto – Location of
tweet posted
• But neither is relevant to
the posted tweet
Scenario
6. • The scenario re-iterates the argument that the location information
in metadata records may not be relevant to a tweet's content.
• However, in a large number of tweets, content will have relevant
contextual location-related information
▪ Such information can be exploited to identify location referenced by
users in their tweets
• We propose a novel approach, labeled as DigiCities
▪ It adds geographical context to tweets by harnessing information
included in the content of tweets.
Introduction
7. • Researchers have used different features and techniques to identify
or improve location detection for tweets (e.g.,):
▪ Exploiting variations in languages and terms used by users in tweets to
identify locations (e.g., Cheng et al., 2010; Hong et al., 2012)
▪ Utilizing location mentioned in tweet content, location included in users
profile, and location as captured at the time of posting tweet to identify
city-level appropriate location (Shen et al., 2018)
Related Work
8. ▪ Exploiting tweet's content with a number of metadata elements (e.g.,
user-description & user-location) supported by neural network-based
framework to predict locations for tweets (Thomas & Henning, 2017)
▪ Using location-relevant terms from content of tweets to identify
locations supported by Convolutional Neural Network (CNN) based
framework (Kumar & Singh, 2019)
▪ Using unsupervised method that used users past tweets and Google
Trends to estimate users location (Zola et al., 2020)
Related Work
9. ▪ Harnessing information in metadata record available in user’s Twitter
profile including user’s profile location, time zone, and language to
detect country-level location (Almadany et al., 2020)
▪ Utilizing information recorded coordinates and tweet content and the
applying geographical knowledge using specific set of rule to detect
event location (Ying et al., 2018)
▪ Researchers like Paradesi (2011) and Inkpen et al. (2015) aimed to
address geo/geo ambiguity and geo/non-geo ambiguity (e.g.,):
o Memphis - location in Egypt and the US (geo/geo)
o Berlin as the name of a person and also a location name in Germany ( geo/non-
geo)
Related Work
11. • The information on the Internet reflects our physical world
(Kindberg et al., 2002)
• “virtual worlds... serve as digital equivalents to...physical world”
(Warf and Sui, 2010 , p.202)
• Drawing on their viewpoints, a real world geographical location
can be represented by multiple facets in the virtual/digital world
• Our proposed novel approach, DigiCities, is based on a
linkage between the digital world and the physical world
What is DigiCities
12. DigiCities and the POP Framework
• DigiCities is the digital avatar of real world cities
▪ A city can be represented in a digital world by multiple facets including:
o People (e.g., City Mayor)
o Organizations (e.g., Local Museum, library)
o Places (e.g., local airport)
We call it the POP Framework!
• This framework helps in creating
Digital Profile of a location A City in
Digital Space
People
Organizations
Places
The POP Framework
13. DigiCities: Mapping Real and Digital World
• Facets in the POP Framework are digitally reflected in tweets
by:
▪ Handles (or user-ids) (starting with @)
▪ Hashtags (starting with #)
• Both handles and hashtags are semantically representing an
entity such as a geographical location
• Tweet Example
▪ I was on #5thavenue and guess who I saw? @Billdeblasio coming
out of the #nypl
14. DigiCities and the POP Framework
• I was on #5thavenue and guess who I saw? @Billdeblasio
coming out of the #nypl
Place
(Iconic New York Avenue)
People
(Mayor of New York)
Organization
(New York Public Library)
• Such representation can help in feature convergence and feature strengthening
(Saif et. al., 2012) i.e.,
▪ Handles and hashtags are referring to different facets (POP) associated with a
geographical location
▪ Thereby, converging to one semantic concept i.e., a location (or a city → New York
in the above example)
16. Edmonton
Calgary
Red Deer
Medicine Hat
FtMcMurry
Lethbridge
Banff
StAlbert
• Eight (8) cities from the Province of Alberta,
a mix of different-sized urban population
center:
▪ The provincial capital (Edmonton)
▪ The largest city in Alberta (Calgary)
▪ A popular tourist destination (Banff) with
transient population
▪ The twin-city of a larger population center (St.
Albert)
▪ An industrial center (Fort McMurray)
▪ Other key but relatively small cities (Red Deer,
Lethbridge, Medicine Hat)
DigiCities: Shortlisted Cities
Image Source: https://www.yellowmaps.com/map/alberta-printable-map-618.htm
17. Process of
Developing
Digital Profile
of Cities
Create Digital Profile of Cities
• The first step in
implementing our approach
is to create digital profiles
of cities as they are
represented by the elements
of the POP framework on
Twitter by handles (‘@’)
and hashtags (‘#’)
18. Digital Profile of Cities
▪ Lethbridge: 98
▪ Medicine Hat: 46
▪ Red Deer: 112
▪ St. Albert: 72
▪ Banff : 114
▪ Calgary: 214
▪ Edmonton : 198
▪ Fort McMurray: 100
• Total number of handles, hashtags and their variants in each
city’s digital profile include:
20. • DigiCities was implemented using two approaches
▪ Append Strategy and Replace Strategy
Original
Tweet
Just landed at #LGA and went straight to @Broadwaycom
so see #Aladdin. This is why I love the #biggapple.
Append
Strategy
Just landed at #LGA newyork and went straight to @Broadwaycom
newyork so see #Aladdin. This is why I love the #biggapple newyork
Replace
Strategy
Just landed at newyork and went straight to newyork so see
#Aladdin. This is why I love the newyork.
Implementation – Append and Replace
21. • A total of 4,500 tweets were manually selected
▪ 500 tweets per city (8 cities x 500 = 4,000 tweets for 8 Cities)
▪ Additional 500 tweets for ‘others’ category
• Basic preprocessing involved preliminary cleaning such as removal of
html tags and special characters (This dataset was labelled as the
Baseline Data)
• Stopwords were not removed and stemming was not applied on the
dataset
• Three algorithms were used: k-Nearest Neighbour (kNN), Naïve Bayes
NB) and Sequential Minimal Optimization (SMO)
Dataset, Data Preparation and Algorithms
22. • A total of 27 classification experiments were performed
Classification Experimentation Details
24. • Classification accuracy scores improved
significantly for all the three algorithms over
the baseline data using both append and
replace strategies
• Comparing with the baseline (data) accuracy
scores of each algorithm, for example, with the
use of append strategy:
▪ kNN had the highest improvement (by ~22%)
▪ NB had the next best improvement (by ~15%)
▪ SMO has an improvement in the accuracy score
(by ~6%) but was relatively less than kNN and NB
47.6%
56.1%
69.6%
69.9%
81.0%
85.1%
87.8% 93.8% 93.9%
40%
50%
60%
70%
80%
90%
100%
Baseline
(B)
Replace
(R)
Append
(A)
kNN NB SMO Algorithms
Data Variants →
Impact of DigiCities (No Preprocessing)
25. WS: Without Stopwords (i.e., Stopwords Removed)
• kNN and NB Algorithms
▪ Removal of stopwords alone as well as the
implementation of DigiCities improved the
accuracy scores significantly
▪ The append strategy worked relatively better
than the replace strategy
• SMO Algorithm
▪ Removal of stopwords alone did not play
critical role in improving classification
accuracy scores
▪ Use of DigiCities made impact on the
accuracy scores with or without stopwords
58.8%
74.6%
83.0%
77.3%
88.4%
89.9%
89.1%
94.1% 94.2%
40%
50%
60%
70%
80%
90%
100%
Base_WS Rep_WS App_WS
kNN NB SMO Algorithms
Data Variants →
B_WS R_WS A_WS
47.6%
56.1%
69.6%
69.9%
81.0%
85.1%
87.8% 93.8% 93.9%
40%
60%
80%
100%
Baseline (B) Replace (R) Append (A)
kNN NB SMO Algorithms
DigiCities and Stopwords
26. SA: Stemming Applied
• NB, kNN & SMO
▪ After stemming, the impact on the
accuracy scores was only marginal
▪ The results show that accuracy scores
improved after stemming with the
implementation of DigiCities
o The improvement can only be attributed to
our approach
DigiCities and Stemming
48.3%
57.8%
70.0%
69.6%
80.3%
85.2%
87.4%
94.0% 93.9%
40%
50%
60%
70%
80%
90%
100%
B_SA R_SA A_SA
kNN NB SMO Algorithms
Data Variants →
47.6%
56.1%
69.6%
69.9%
81.0%
85.1%
87.8% 93.8% 93.9%
40%
60%
80%
100%
Baseline (B) Replace (R) Append (A)
kNN NB SMO Algorithms
27. • Both append and replace strategies helped in
improving the accuracy scores for all the three
algorithms
• NB and kNN Algorithms
▪ Append strategy gave relatively better results as
compared to the replace strategy
▪ Statistically, the accuracy scores achieved with the
use of append and replace strategies were
significantly different
• SMO Algorithm
▪ There was no statistical difference in the accuracy
scores achieved with the use of append and replace
strategies
DigiCities – Append vs Replace
58.8%
74.6%
83.0%
77.3%
88.4%
89.9%
89.1% 94.1% 94.2%
40%
60%
80%
100%
Base_WS Rep_WS App_WS
kNN NB SMO Algorithms
B_WS R_WS A_WS
47.6%
56.1%
69.6%
69.9%
81.0%
85.1%
87.8% 93.8% 93.9%
40%
60%
80%
100%
Baseline (B) Replace (R) Append (A)
kNN NB SMO Algorithms
48.3% 57.8%
70.0%
69.6%
80.3%
85.2%
87.4%
94.0% 93.9%
40%
60%
80%
100%
B_SA R_SA A_SA
kNN NB SMO Algorithms
29. • Our approach, DigiCities, helped in improving the classification
accuracy scores
▪ For all the three algorithms, kNN, NB and SMO
▪ By using either append or replace strategy
• For SMO algorithm, both removal of stopwords and stemming did
not play a critical role with the use of DigiCities approach
• Removal of stopwords with DigiCities will positively impact
classification accuracy for both kNN and NB algorithms
Summary – Key Findings
30. • Stemming of tweet data may not play a critical role, particularly
when used with DigiCities
• Both append and replace strategies helped in improving the
classification accuracy of all the three algorithms
• The append strategy is better as compared to the replace strategy
to implement DigiCities when using kNN and NB algorithms but
with SMO either strategy would work
Summary – Key Findings
32. • Geographical biasness - Only eight cities from one province
• No prior research to model digital profiles of cities
• Lack of city-level knowledge may impact development of digital
profiles of cities
• Tweet dataset used in this research
▪ Small Dataset
▪ Tweet selection bias
▪ No inter-coder reliability
Limitations
33. • We proposed a novel approach, DigiCities, which uses the POP
Framework to map the real world locations in the digital world
• The facet of the POP framework includes:
▪ People, Organizations, and Places
• DigiCities helped in improving the right location – tweet pairing
by harnessing city relevant information from the content of
tweet, particularly by using hashtags and handles
Conclusion
34. • Areas of future work include:
▪ Automating the process of building digital profiles of cities
▪ Increasing the diversity of cities and the dataset size
▪ Scope to further enhance the POP Framework by adding new facets
such as local language and seasonal terms.
▪ Test our approach by varying of hyper-parameters and using other
classification algorithms
▪ Implement this approach in combination with other approaches (e.g.,
Inkpen et al., 2015) to make improvements in location detection and
disambiguation
Future Work
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