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 Outdated and inaccurate information in user profiles, the absence of location data in tweet texts, and the limited number of geotagged posts are among the challenges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%.
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Evidential fine-grained event localization using Twitter
1. Evidential fine-grained event localization using Twitter
By:
Zahra Khodabandeh Shahraki
Supervisors:
Dr. Afsaneh Fatemi Khorasgani
Dr. Hadi Tabatabaee Malazi
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
18/26
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
19/26
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.
25/26
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
26/26