Comparing Social Physics with Machine Learning and Deep Learning
Accuracy and performance
The customer group that was detected by the Machine Learning model comprised of customers who will indeed respond positively to a marketing offer by the bank (“True Positives”), and those who will not (“False Positives”).
By way of illustration, let’s assume that the True Positives comprise 10% of the model’s results. Extensive experiments reveal that we can expect the vast majority of those 10% to also be detected by Endor’s Social Physics engine, with two main differences: (a) many of the False Positives of the Machine Learning model will not be reported by the Endor engine; (b) the Endor results list will contain many additional True Positives, which are not detected by the traditional model. The implication of this is a significant improvement in sales efforts (thanks to the better precision / recall trade-off).
In a recent test 15 million Tweets’ meta-data were provided to Endor as raw data for analysis. In addition, the customer revealed the identity of 50 Twitter accounts known to be ISIS activists that were contained in the input data, and tested Endor’s ability to detect an additional 74 accounts that were hidden within the data. Endor’s engine completed the task on a single laptop in only 24 minutes (measured from the time the raw data was introduced into the system until the final results were available), identifying 80 Twitter accounts as “lookalikes” to the provided example, 45 of which (56%) turned out to be part of the list of the 74 hidden accounts. Importantly, this provided an extremely low false alarm rate (35 False Positive results), so that the customer could easily afford to have human experts investigate the identified targets
Human reality is composed of many small temporary events and changes.
Endor, grounded in Social Physics, incorporates the underlying dynamics of human behavior and is therefore better equipped to uncover small groups in the population who are likely to behave in a certain way due to recent, unexpected events.
Endor is therefore uniquely capable of identifying dynamic signals in human behavior data that no other method can sense. This is because traditional Machine Learning and Deep Learning methods would not be able to distinguish between these signals and noise. Without Social Physics, these signals lack any sort of statistical significance.
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