In this paper, we propose an approach to intelligent and automatic keyword selection for the purpose of Twitter data collection and analysis. The proposed approach makes use of a combination of deep learning and evolutionary computing. As some context for application, we present the proposed algorithm using the case study of public health surveillance over Twitter, which is a field with a lot of interest. We also describe an optimization objective function particular to the keyword selection problem, as well as metrics for evaluating Twitter keywords, namely: reach and tweet retreival power, on top of traditional metrics such as precision. In our experiments, our evolutionary computing approach achieved a tweet retreival power of 0.55, compared to 0.35 achieved by the baseline human approach.
|Title of host publication||Hybrid Artificial Intelligent Systems - 15th International Conference, HAIS 2020, Proceedings|
|Editors||Enrique Antonio de la Cal, José Ramón Villar Flecha, Héctor Quintián, Emilio Corchado|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||12|
|Publication status||Published - 2020|
|Event||15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020 - Gijón, Spain|
Duration: 11 Nov 2020 → 13 Nov 2020
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020|
|Period||11/11/20 → 13/11/20|
Bibliographical noteFunding Information:
Supported by Public Health England.
© 2020, Springer Nature Switzerland AG.
- Evolutionary computing
- Social media sensing
- Syndromic surveillance