Name
Abstract | ||
|---|---|---|
Social media has changed the way we communicate. Social media data capture our social interactions and utterances in machine readable format. Searching and analysing massive and frequently updated social media data brings significant and diverse rewards across many different application domains, from politics and business to social science and epidemiology. A notable proportion of social media data comes with explicit or implicit spatial annotations, and almost all social media data has temporal metadata. We view social media data as a constant stream of data points, each containing text with spatial and temporal contexts. We identify challenges relevant to each context, which we intend to subject to context aware querying and analysis, specifically including longitudinal analyses on social media archives, spatial keyword search, local intent search, and spatio-temporal intent search. Finally, for each context, emerging applications and further avenues for investigation are discussed. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2452376.2452393 | EDBT |
Keywords | Field | DocType |
context aware querying,towards context-aware search,implicit spatial annotation,social media,data point,local intent search,social science,social interaction,social media data,social media archives,spatial keyword search,n grams | Data science,Data mining,Computer science,Machine-readable data,Social computing,Metadata,World Wide Web,Social media,Social media optimization,Keyword search,Automatic identification and data capture,Politics,Database | Conference |
Citations | PageRank | References |
23 | 0.83 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leon R. A. Derczynski | 1 | 23 | 0.83 |
| Bin Yang | 2 | 706 | 34.93 |
| Christian S. Jensen | 3 | 10651 | 1129.45 |