#dh2016 — Public Fediverse posts

Small personal note: It was lovely to be back in Kraków after the memorable #DH2016, for the #IAHR2025 as the big international conference of my second field of research, the study of religion(s). Also, it’s very nice to see how these discussions about #DigitalHumanities methodologies now also take place within #ReligiousStudies conferences.

Small personal note: It was lovely to be back in Kraków after the memorable #DH2016, for the #IAHR2025 as the big international conference of my second field of research, the study of religion(s). Also, it’s very nice to see how these discussions about #DigitalHumanities methodologies now also take place within #ReligiousStudies conferences.

Small personal note: It was lovely to be back in Kraków after the memorable #DH2016, for the #IAHR2025 as the big international conference of my second field of research, the study of religion(s). Also, it’s very nice to see how these discussions about #DigitalHumanities methodologies now also take place within #ReligiousStudies conferences.

Small personal note: It was lovely to be back in Kraków after the memorable #DH2016, for the #IAHR2025 as the big international conference of my second field of research, the study of religion(s). Also, it’s very nice to see how these discussions about #DigitalHumanities methodologies now also take place within #ReligiousStudies conferences.

Small personal note: It was lovely to be back in Kraków after the memorable #DH2016, for the #IAHR2025 as the big international conference of my second field of research, the study of religion(s). Also, it’s very nice to see how these discussions about #DigitalHumanities methodologies now also take place within #ReligiousStudies conferences.

Now up at #DH2024, Maciej Eder, developer of #stylo and co-organizer of #DH2016 in #Krakow, on various distance measures for #Stylometry: "Manhattan, Euclidean and their Siblings. Exploring Exotic Measures of Text Similarities...".

Key idea: Manhattan distance is L1-norm based, Euclidean is L2. But we can vary this parameter for a wide range of values, from 0.1 to 10. Then evaluate accuracy for authorship attribution.

Result: For longer vectors, it pays off to use a value of less than 1!

Now up at #DH2024, Maciej Eder, developer of #stylo and co-organizer of #DH2016 in #Krakow, on various distance measures for #Stylometry: "Manhattan, Euclidean and their Siblings. Exploring Exotic Measures of Text Similarities...".

Key idea: Manhattan distance is L1-norm based, Euclidean is L2. But we can vary this parameter for a wide range of values, from 0.1 to 10. Then evaluate accuracy for authorship attribution.

Result: For longer vectors, it pays off to use a value of less than 1!

Now up at #DH2024, Maciej Eder, developer of #stylo and co-organizer of #DH2016 in #Krakow, on various distance measures for #Stylometry: "Manhattan, Euclidean and their Siblings. Exploring Exotic Measures of Text Similarities...".

Key idea: Manhattan distance is L1-norm based, Euclidean is L2. But we can vary this parameter for a wide range of values, from 0.1 to 10. Then evaluate accuracy for authorship attribution.

Result: For longer vectors, it pays off to use a value of less than 1!

Now up at #DH2024, Maciej Eder, developer of #stylo and co-organizer of #DH2016 in #Krakow, on various distance measures for #Stylometry: "Manhattan, Euclidean and their Siblings. Exploring Exotic Measures of Text Similarities...".

Key idea: Manhattan distance is L1-norm based, Euclidean is L2. But we can vary this parameter for a wide range of values, from 0.1 to 10. Then evaluate accuracy for authorship attribution.

Result: For longer vectors, it pays off to use a value of less than 1!

Now up at #DH2024, Maciej Eder, developer of #stylo and co-organizer of #DH2016 in #Krakow, on various distance measures for #Stylometry: "Manhattan, Euclidean and their Siblings. Exploring Exotic Measures of Text Similarities...".

Key idea: Manhattan distance is L1-norm based, Euclidean is L2. But we can vary this parameter for a wide range of values, from 0.1 to 10. Then evaluate accuracy for authorship attribution.

Result: For longer vectors, it pays off to use a value of less than 1!

Fun fact: having to fight this same battle in 2016 on the program committee for #DH2016 is how Jennifer Guiliano & I became friends and so y’all can thank Europeans Who Feel Some Kind of Way About Diversity and the DH Conference™️ for #ReviewsInDH

Fun fact: having to fight this same battle in 2016 on the program committee for #DH2016 is how Jennifer Guiliano & I became friends and so y’all can thank Europeans Who Feel Some Kind of Way About Diversity and the DH Conference™️ for #ReviewsInDH

Fun fact: having to fight this same battle in 2016 on the program committee for #DH2016 is how Jennifer Guiliano & I became friends and so y’all can thank Europeans Who Feel Some Kind of Way About Diversity and the DH Conference™️ for #ReviewsInDH

Fun fact: having to fight this same battle in 2016 on the program committee for #DH2016 is how Jennifer Guiliano & I became friends and so y’all can thank Europeans Who Feel Some Kind of Way About Diversity and the DH Conference™️ for #ReviewsInDH

Fun fact: having to fight this same battle in 2016 on the program committee for #DH2016 is how Jennifer Guiliano & I became friends and so y’all can thank Europeans Who Feel Some Kind of Way About Diversity and the DH Conference™️ for #ReviewsInDH