Name
Abstract | ||
|---|---|---|
We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1155/2015/712835 | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE |
Field | DocType | Volume |
Semantic similarity,Semantic integration,Computer science,Semantic network,Natural language processing,Artificial intelligence,Semantic grid,Social Semantic Web,Machine learning,Semantics,Semantic computing,Semantic compression | Journal | 2015 |
ISSN | Citations | PageRank |
1687-5265 | 3 | 0.40 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Facundo Carrillo | 1 | 4 | 1.82 |
| Guillermo A. Cecchi | 2 | 199 | 34.56 |
| Mariano Sigman | 3 | 79 | 13.24 |
| Diego Fernández Slezak | 4 | 32 | 5.86 |