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| include("src/narrative_construction.jl") | |
| # Create a new claim | |
| claim1 = createClaim("Antarctica is gaining ice and is not actually warming", | |
| "Antarctical is losing ice year by year due to the effects of climate change", | |
| "Facebook post", | |
| ["antarctica", "global warming", "climate change"]) | |
| claim2 = createClaim("It's natural cycles/variation in weather, not global warming", | |
| "There is substantial evidence that the current warming is not due to natural cycles", | |
| "Facebook post", | |
| ["natural cycles", "global warming", "climate change"]) | |
| climate_narrative = Narrative( | |
| randid(), | |
| "Climate Change Denial", | |
| "Climate Change", | |
| "Scientists, Elites", | |
| "The science behind climate change inconclusive or flawed", | |
| Claim[]) | |
| # Add the claims to the narrative | |
| add_claim!(climate_narrative, claim1) | |
| add_claim!(climate_narrative, claim2) | |
| ## show the narrative | |
| climate_narrative | |
| ## Now, let's deploy the narrative to the database | |
| candidate_data = DataFrame() | |
| """ | |
| ## Deploy narrative does the following: | |
| 1. Retrieves the data from the database | |
| 2. Filters the data based on the keywords in the claims | |
| 3. Generates embeddings for the claims and the data | |
| 4. Computes the similarity between the claims and the data | |
| 5. Returns the top 10 most similar data points | |
| """ | |
| deploy_narrative_model!(climate_narrative, threshold=.2) |