You Shall Know a Place by the Conversations it Seeds
In this work, we look at problems of urban sensing from the lens of conversations (tweets) on Twitter. Using techniques from statistical natural language processing on geotagged tweets, we identify areas which exhibit similar aggregate behavior, infer the land-use of areas and predict types of indiv...
Uloženo v:
| Vydáno v: | 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) s. 359 - 362 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
New York, NY, USA
ACM
31.07.2017
|
| Edice: | ACM Conferences |
| Témata: |
Human-centered computing
> Visualization
> Visualization application domains
> Geographic visualization
Information systems
> Information systems applications
> Spatial-temporal systems
> Geographic information systems
|
| ISBN: | 1450349935, 9781450349932 |
| ISSN: | 2473-991X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | In this work, we look at problems of urban sensing from the lens of conversations (tweets) on Twitter. Using techniques from statistical natural language processing on geotagged tweets, we identify areas which exhibit similar aggregate behavior, infer the land-use of areas and predict types of individual establishments. We demonstrate our inferences using over two years of Twitter data, for a wide variety of spatial contexts and evaluate our results against existing open data sets. Our results are novel in extremely detailed resolution of their mapping, and demonstrate that tweets can be a very effective urban sensor and in many regards are superior to other data sources for studying urban spaces. Our techniques are language agnostic, and can be applied to any city where enough similar data is available. |
|---|---|
| ISBN: | 1450349935 9781450349932 |
| ISSN: | 2473-991X |
| DOI: | 10.1145/3110025.3110123 |