Reason Based Text Alignment Discovery Model

The hottest topic currently is fake news and the proliferation of disinformation. Though it would be counter to the wishes of corporations more interested in profits than human collective good, a system is sorely required to analyse text content that is shared as & undistinguishable from facts. Small statements with no backing and linking to articles written based on pure emotion rather than concrete evidence, backed by nothing aside from straw man arguments or espousing opiníon and rhetoric as law or common decree.

An outline for a model that would generate a small visual graph which would allow for a glance into the substance of the content before consumption:
  • Emotive language ratio to detailing of findings and reason based “disprove first” approach
  • Inference of subtext and agenda of the content
  • Links + source references count & meta score of their own merit
  • Ratio of steel man or straw man arguments
  • Count of balacing statements and justification/testing of hypothesis
  • Count for reference to opposing views with contextual ratio of emotional tone vs analytical debate
Such a model would need a vast knowledge of textual reference and some careful tuning + coaching to avoid biases and proliferation of skewed views. True balancing and reason based “disprove first” approach would need to be cornerstone of the algorithm.

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