A Review of Internet Topology Research at the Autonomous System Level

Witono, Timotius; Yazid, Setiadi

doi:10.1007/978-981-16-2377-6_54

Cited by 4 publications

(1 citation statement)

References 58 publications

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“…In particular, in the case of crisis scenarios, such as identifying a request for help, it is important not only to know that someone is in danger, but also where these people are located. Nevertheless, geolocation still remains as an unfinished problem, since it is difficult to make precise geolocation predictions based on tweets' content [12]. Identifying the location of tweets is a difficult task, since coordinates are rarely available: according to Twitter 2 , only 1-2% of the tweets are geographically tagged.…”

Section: Introductionmentioning

confidence: 99%

A Knowledge Graph-Based Method for the Geolocation of Tweets

Lovera

Cardinale

Buscaldi

et al. 2023

Workshop Proceedings of the 19th International Conference on Intelligent Environments (IE2023)

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Twitter geolocation is useful for various purposes, including tracking COVID-19 perceptions, analyzing political trends, and managing natural disasters. However, accurately predicting geolocations based on tweet content remains a challenge. In the past, machine learning approaches have tried to solve this problem by training prediction models on previously seen data, but these models often struggle to generalize to unseen places. To overcome these limitations, in this work we present a framework based on Natural Language Processing (NLP), Knowledge Graphs (KG), and Semantic Web to find geographical entities on tweets’ content. KG facilitate the extraction of structured knowledge of texts in order to study their semantic analysis based on NLP techniques to search associated geographical coordinates to the entities of that KG; if there is explicit mention of places in the tweet, the Semantic Web is used to find geographical information associated with the entities present in the tweets’ content. To evaluate the precision of the prediction algorithm, we compare our predicted latitude and longitude coordinates with AlbertaT6 floods dataset. Our results show an F1 score up to 0.851 within a 10 kilometer radius.

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Section: Introductionmentioning

confidence: 99%