Thanks for reading @SK53! I appreciate the due diligence, and I will consider it in my current work moving forward! :)

Great call out, @GRUBERND! I am actually working on creating filters for each language. Your examples absolutely highlight the obstacle of a strict, one-size-fits-all filter; a profane word in one culture or language may be benign in another, or vice versa. :) Thanks for reading.

This is awesome! Great work @manoharuss :)

@Piskvor, thanks for taking the time to read! Currently, my logic only handles the English language. Expansion to other languages is my top priority! Hopefully I post another blog post when that is done. :D

Thanks for reading, @Adamant1, and bringing up some interesting points! I will try to consider them in my next iteration. :)

@Chetan_Gowda Hi! Thanks for the read. :) I’m glad you liked it, and I think your idea is great! (see comment above to @Carnildo). :D Thanks again!

Hi @Carnildo! Looking at strictly labels, the ratio of false positives to true positives for the examples you listed was too high for me to consider including the feature that surfaces such labels. In the future, I want to find some combination of features that can discern a better decision boundary between poor and high quality. I think this may include, as others have suggested, bringing in other data sources/indicators of reputation.

@GinaroZ Totally fair! I think there’s a lot of complexity with detecting anomalous low-quality labels, and I envision my techniques as something to augment or flag human review, such as what you’ve done. I’ve noticed that a lot of labels in hiking areas have a lot more variability/flexibility. Thanks for the due diligence!

@iandees Yes! I plan to continue running, improving, and working with other teams to better flag low-quality labels. :)