A lightweight and multilingual framework for crisis information extraction from Twitter data
Bibliographic Data
| ID | 4699853 |
|---|---|
| Authors | Roberto Interdonato (0000-0002-0536-6277, Centre National de la Recherche Scientifique, corresponding author), Jean-Loup Guillaume, Jean‐Loup Guillaume (0000-0002-4615-1563, La Rochelle Université), Antoine Doucet (0000-0001-6160-3356, La Rochelle Université) |
| Year | 2019 |
| Volume | 9 |
| Issue | 1 |
| Pages | 65 |
| Publication date | 2019-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-019-0608-4 |
| OpenAlex | W2989078043 |
| Language | EN |
| Citations received | 5 |
| References cited | 31 |
Cluster analysis · Crisis management · Data science · Information extraction · Information overload · Information retrieval · Microblogging · Natural disaster · Political science · Social media · World Wide Web · Advanced Text Analysis Techniques · Complex Network Analysis Techniques · Computer Science · Engineering · Public Relations and Crisis Communication · Artificial Intelligence
Colombian political leaders on Twitter during the Covid‐19 pandemic
Dees
Topics extraction in incremental short texts based on LSTM
Ranking and grouping social media requests for emergency services using serviceability model
On detecting urgency in short crisis messages using minimal supervision and transfer learning
| Unique citing works | 5 |
|---|---|
| Citations per year | 0.83 |
| Citation span | 2020 - 2022 (3) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 5 |