Name
Abstract | ||
|---|---|---|
Geo-tagged Twitter has been proven to be a useful proxy for urban mobility, this way helping to understand the structure of the city and the shape of its local neighborhoods. In the present work we approach this problem from another angle by leveraging additional information on Twitter customers mentioning each other, which might partially reveal their social relations. We propose a novel way of constructing a spatial social network based on such data, analyze its structure and evaluate its utility for delineating urban neighborhoods. This delineation happens to have substantial similarity to the earlier one based on the user mobility network. It leads to an assumption that the social connectivity between the users is strongly related with the similarity in their mobility patterns. We justify this hypothesis enabling extrapolation of the available user mobility patterns as a proxy for social connectivity and building a network of hidden ties based on the mobility pattern similarity. Finally, we evaluate the socio-economic characteristics of the partitions for all three networks of all mentioning, reciprocal mentioning and the hidden ties. |
Year | Venue | Field |
|---|---|---|
2018 | PROCEEDINGS OF THE 51ST ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS) | Social network,Social media,Public relations,Computer science,Knowledge management,Big data |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanislav Sobolevsky | 1 | 464 | 32.15 |
| Philipp Kats | 2 | 0 | 0.34 |
| sergey malinchik | 3 | 8 | 1.46 |
| Mark Hoffman | 4 | 0 | 1.69 |
| Brian Kettler | 5 | 17 | 3.17 |
| Constantine E. Kontokosta | 6 | 25 | 6.81 |