The widespread popularity and worldwide application of social networks have raised interest in the analysis of content created on the networks. One such analytical application and aspect of social networks, including Twitter, is identifying the location of various political and social events, natural disasters and so on. The present study focuses on the localization of traffic accidents. Outdated and inaccurate information in user profiles, the absence of location data in tweet texts, and the limited number of geotagged posts are among the challenges tackled by location estimation. Adopting the Dempster-Shafer Evidence Theory, the present study estimates the location of accidents using a combination of user profiles, tweet texts, and the attached locations in tweets. The results indicate improved performance regarding error distance and average error distance compared to previously developed methods. The proposed method in this study resulted in a reduced error distance of 26%.

1. Evidential fine-grained event localization using Twitter
By:
Zahra Khodabandeh Shahraki
Supervisors:
Dr. Afsaneh Fatemi Khorasgani
Dr. Hadi Tabatabaee Malazi

2. Overview
• Introduction
• Concepts
• Challenges
• Techniques
• Related Work
• Proposed Framework
• Evaluation
• Conclusion
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3. Introduction
3/26

4. Consepts
Fine_grained
Location
Coarse-graind
Location
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M.hassan, M. Orgun, R. Schwitter (2016)
O. Ozdikis, H. Oğuztüzün, P. Karagoz (2017)
O. Ozdikis, H. Oğuztüzün, P. Karagoz (2017)

5. Challenges
Tweet Text
User Profile
Place Attachments
Geo Tags
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6. Localization Techniques
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Majority
Voting
Clustering
Probabilistic
Techniques
O. Ozdikis, H. Oğuztüzün, P. Karagoz (2017)

7. Dempster-Shafer Theory
mass functions (m), belief function (Bel), plausibility function (PL)
• 𝑚: 2 𝑥
→ 0,1
• 𝑚 ∅ = 0
• 𝐴𝜖2 𝑥 𝑚 𝐴 = 1
• 𝑃𝐿 𝐴 = 𝐵|𝐵∩𝐴≠∅ 𝑚(𝐵)
• 𝐵𝑒𝑙 𝐴 = 𝐵⊆𝐴,𝐵≠∅ 𝑚(𝐵)
• 𝐵𝑒𝑙(𝐴) ≤ 𝑃(𝐴) ≤ 𝑃𝐿(𝐴)
• 𝑄 𝐴 = 𝐴⊆𝐵 𝑚(𝐵)
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8. Combination rules of Dempster-Shafer theory
 Dempster-Shafer Combination Rule
• 𝒎 𝟏,𝟐 𝑨 = 𝒎 𝟏 𝑩  𝒎 𝟐 𝑪
=
𝟏
𝟏−𝑲 𝑩∩𝑪=𝑨≠∅ 𝒎 𝟏(𝑩)𝒎 𝟐(𝑪)
that
• 𝑲 = 𝑩∩𝑪=∅ 𝒎 𝟏 𝑩 𝒎 𝟐 𝑪
 Dubois And Prade Combination Rule
• 𝒎 𝟏,𝟐 𝑨 = 𝒎 𝟏 𝑩 ′
𝒎 𝟐 𝑪 = 𝑨=𝑩∩𝑪 𝒎 𝟏(𝑩) ∗ 𝒎 𝟐(𝑪) + 𝑨=𝑩∪𝑪,𝑩∩𝑪=∅

𝒎 𝟏(𝑩) ∗ 𝒎 𝟐(𝑪)
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9. The comparison of studies conducted on event
localization
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10. The devised localization framework
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11. Data Collection
Twitter
Streaming
API Twitter4j API
Search query
Filter results
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12. Data Pruning
Not related
to accident
Quotes
Repeat
URLsShort
contents
Not
related
emoji
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13. Pre-Processing
Text
Tokenization
Replace
Slang and
Abbrevations
Delete URLs
Eliminate #
and @
Eliminate
Stop words
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14. Clustering
Feature
Vectors
𝒗 𝒕𝟏. . 𝒗 𝒕𝒏
𝑡𝑓𝑖𝑑𝑓
𝑣𝑐
𝑣𝑐
𝑣𝑐𝑣𝑐
Calculate Cosine
Similarity for
𝒗 𝒕𝒊 𝐚𝐧𝐝 𝒗 𝒄𝒊
Similarity>
=
Threshold
Add tweet
to cluster
Create
new
cluster
Update cluster’s
centroid vector
No
Yes
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15. Localization
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16. User-location
Cities
Mass
function
𝒄 → 𝒎 𝒄
𝒄 → 𝒎 𝒑
𝒄 → 𝒎 𝒖
𝒎 𝒄𝒑
𝒎 𝒄𝒑𝒖
Combine
Combine
W1
W2
Coarse-grained
location
Commonalityvalues
Time weighting
𝑓𝑐
𝑓𝑝
𝑓𝑢
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17. POS tagging
Implement
Grammar
Rules
Fine-grained
Coarse-
grained
Address
Geographical
coordinates
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18. Data preparation
Tweet No. 1 Accident on I-271 SB at Richmond Rd #traffic
https://t.co/IXfI2jHBEJ
Tweet No. 2 A car fire has the two right lanes blocked. in
#SanDiego on 5 SB at Carmel Mtn Rd, stopped traffic
back to 56, delay of 3 mins #SDtraffic
Tweet No. 3 Accident, shoulder blocked in #Jefferson on Hwy 280
EB at Cahaba River Rd #traffic
https://t.co/ZmMl2wIeqD
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19. Average
number of
tweets in
clusters
Number of
clusters
Number of
clusters tweets
Number of
outlier tweets
Number of
selected
tweets
Total number
of tweets
3.4629010036572800222,873
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20. Evaluation metrics
𝑬𝒓𝒓𝑫𝒊𝒔𝒕 𝒆 = 𝒅𝒊𝒔𝒕𝒂𝒏𝒄𝒆 𝒍𝒐𝒄 𝒂𝒄𝒕 𝒆 , 𝒍𝒐𝒄 𝒆𝒔𝒕 𝒆
𝑨𝒗𝒈𝑬𝒓𝒓𝑫𝒊𝒔𝒕 𝑬 =
𝒆∈𝑬 𝑬𝒓𝒓𝑫𝒊𝒔𝒕(𝒆)
𝑬
𝒅 = 𝟐 𝐑 𝒂𝒓𝒄𝒔𝒊𝒏 𝒔𝒊𝒏 𝟐
𝜶 𝟐 − 𝜶 𝟏
𝟐
+ 𝒄𝒐𝒔 𝜶 𝟏 𝒄𝒐𝒔 𝜶 𝟐 𝒔𝒊𝒏 𝟐
𝜷 𝟐 − 𝜷 𝟏
𝟐
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21. First scenario: Investigating the role of the tweets’
temporal attributes in event localization
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22. Second scenario: investigating the influence of
combining local attributes with the Dempster–
Shafer Theory vs Majority voting
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23. Comparison of average error distance on
evaluated methods for 220 events
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24. Third scenario: Investigating the estimated
locations vs the actual location on the map
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25. Conclusion
• The results indicate improved performance regarding error distance
and average error distance compared to previously developed
methods.
• The results show that weighting the mass functions based on time
features have improved the performance of the Dempster–Shafer
approach.
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26. Future Work
• online localization
• Applying proposed framework to any type of events
• Detecting time of the events
• fine-grained localization
based on unsupervised methods independent of grammatical rules
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