Summary
The “Graphs and Ontologies for Literary Evolution Models” (GOLEM) project will create statistically robust models explaining how fiction evolves, based on the analysis of millions of stories and the effects they have on readers. This is the first time in history that this kind of data is available on such a large scale, thanks to the fact that readers all over the world use digital and social media to share fictional stories and to comment on them, e.g. on fanfiction websites or on publishing platforms like Wattpad. GOLEM will use computational literary studies and cultural evolution theory to create accurate models of how the (formal and content-related) cultural traits found in fiction spread and combine. The basis of this evolutionary analysis of fiction will be a knowledge graph database – an infrastructure of interlinked data about stories and reader response – which will be used to test hypotheses related to the accumulation of cultural traits in stories and their effectiveness in achieving cognitive and emotional effects on readers. State-of-the-art machine learning algorithms and advanced statistical modelling tools will be employed to create a major breakthrough in computational literary studies, possibly also contributing to the revision of cultural evolution theories. By focusing on the relations between stories in five different languages, collected from countries in all continents, GOLEM will provide an unprecedented insight into how storytelling, one of the most ancient cultural systems, evolves. Literary history and criticism have offered refined accounts of how fiction works, mostly relying on case studies of limited extent. It is now time to provide robust statistical evidence of the anthropological function of fiction and of how it adapts to different circumstances and cultures, empowering readers to cope with their cultural or societal contexts.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101040938 |
| Start date: | 01-01-2023 |
| End date: | 31-12-2027 |
| Total budget - Public funding: | 1 194 088,75 Euro - 1 194 088,00 Euro |
Cordis data
Original description
The “Graphs and Ontologies for Literary Evolution Models” (GOLEM) project will create statistically robust models explaining how fiction evolves, based on the analysis of millions of stories and the effects they have on readers. This is the first time in history that this kind of data is available on such a large scale, thanks to the fact that readers all over the world use digital and social media to share fictional stories and to comment on them, e.g. on fanfiction websites or on publishing platforms like Wattpad. GOLEM will use computational literary studies and cultural evolution theory to create accurate models of how the (formal and content-related) cultural traits found in fiction spread and combine. The basis of this evolutionary analysis of fiction will be a knowledge graph database – an infrastructure of interlinked data about stories and reader response – which will be used to test hypotheses related to the accumulation of cultural traits in stories and their effectiveness in achieving cognitive and emotional effects on readers. State-of-the-art machine learning algorithms and advanced statistical modelling tools will be employed to create a major breakthrough in computational literary studies, possibly also contributing to the revision of cultural evolution theories. By focusing on the relations between stories in five different languages, collected from countries in all continents, GOLEM will provide an unprecedented insight into how storytelling, one of the most ancient cultural systems, evolves. Literary history and criticism have offered refined accounts of how fiction works, mostly relying on case studies of limited extent. It is now time to provide robust statistical evidence of the anthropological function of fiction and of how it adapts to different circumstances and cultures, empowering readers to cope with their cultural or societal contexts.Status
SIGNEDCall topic
ERC-2021-STGUpdate Date
09-02-2023
Images
No images available.
Geographical location(s)
Structured mapping
