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1 Hundred: An AI assisted analysis of Cybercrimeology
Summary:
The main points of this episode are:
About our guests:
Alloy:
https://platform.openai.com/docs/guides/text-to-speech
voicing generations from
ChatGPT
https://openai.com/blog/chatgpt
Papers or resources mentioned in this episode:
The BART model:
https://huggingface.co/docs/transformers/model_doc/bart
The DistilBERT model:
https://huggingface.co/docs/transformers/model_doc/distilbert
Results:
Which terms were spoken about the most and what was the sentiment around those ?
NounOccurrencesFilesOccurredInSentimentScoreSumpeople25299492.60830581188202time11338379.5210649research13968079.49750900268553way10057473.79837167263031things12387372.45885318517685lot11177170.87118428945543data9034644.24124717712402kind6674443.9891608crime8854342.725725710392005cyber8054139.68457114696503cybercrime4813836.90566980838775thing3933635.59294366836548security5273130.89444762468338information4672928.87013864517212
Was there a change in the sentiment of the podcast after the end of pandemic conditions, assuming that the pandemic ended at the end of Q3 2021?
The model is given by:
yi∼Normal(μi,σ)yi∼Normal(μi,σ)
where
μi=β0+βafter_event⋅xiμi=β0+βafter_event⋅xi
Here, the parameters are defined as follows:
This provided the results as follows:
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.37 0.06 0.26 0.48 1.00 3884 2917
after_event 0.39 0.08 0.23 0.54 1.00 3561 2976
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 0.38 0.03 0.33 0.44 1.00 3608 2817
Other:
The model overlooked Mike Levi's contribution to the History series. That is a bit unfair.
Where there were multiple guests, I did not include them all in the database, hence "no specific guest listed"
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