Twitter for election forecasts: a joint machine learning and complex network approach applied to an italian case study
| Autor: | Coletto M., Lucchese C., Orlando S., Perego R., Chessa A., Puliga M. |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | International Conference on Computational Social Science (ICCSS 2015), Helsinki, Finland, 08-11/06/2015 ISTI Technical reports, 2015 info:cnr-pdr/source/autori:Coletto M.; Lucchese C.; Orlando S.; Perego R.; Chessa A.; Puliga M./congresso_nome:International Conference on Computational Social Science (ICCSS 2015)/congresso_luogo:Helsinki, Finland/congresso_data:08-11%2F06%2F2015/anno:2015/pagina_da:/pagina_a:/intervallo_pagine |
| Popis: | Several studies have shown how to approximately predict real-world phenomena, such as political elections, by ana- lyzing user activities in micro-blogging platforms. This ap- proach has proven to be interesting but with some limita- tions, such as the representativeness of the sample of users, and the hardness of understanding polarity in short mes- sages. We believe that predictions based on social network analysis can be significantly improved by exploiting machine learning and complex network tools, where the latter pro- vides valuable high-level features to support the former in learning an accurate prediction function. |
| Databáze: | OpenAIRE |
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