Persuasion: What Jane Austin Would Have Written

Moumita Sinha, Jennifer Healey, Faran Ahmad, Varun Gupta, Niloy Ganguly
Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing, PMLR 122:36-43, 2020.

Abstract

This paper presents preliminary results for developing an online "persuasion score" that will enable digital marketing content authors to compose and edit materials with better persuasive capability. Inspired by initial insights with digital marketing professionals and research on the foundations of persuasion: pathos, ethos and logos, we extracted features from a data set of over three million consumer reactions to email marketing campaigns covering a three month period. We report on the most significant features of the content, including image position and text readability as well as the most salient customer features such as time since registration and time since last opened email from the same marketing brand.

Cite this Paper

BibTeX
@InProceedings{pmlr-v122-sinha20a, title = {Persuasion: What {J}ane {A}ustin Would Have Written}, author = {Sinha, Moumita and Healey, Jennifer and Ahmad, Faran and Gupta, Varun and Ganguly, Niloy}, booktitle = {Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing}, pages = {36--43}, year = {2020}, editor = {Hsu, William}, volume = {122}, series = {Proceedings of Machine Learning Research}, month = {10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v122/sinha20a/sinha20a.pdf}, url = {https://proceedings.mlr.press/v122/sinha20a.html}, abstract = {This paper presents preliminary results for developing an online "persuasion score" that will enable digital marketing content authors to compose and edit materials with better persuasive capability. Inspired by initial insights with digital marketing professionals and research on the foundations of persuasion: pathos, ethos and logos, we extracted features from a data set of over three million consumer reactions to email marketing campaigns covering a three month period. We report on the most significant features of the content, including image position and text readability as well as the most salient customer features such as time since registration and time since last opened email from the same marketing brand.} }
Endnote
%0 Conference Paper %T Persuasion: What Jane Austin Would Have Written %A Moumita Sinha %A Jennifer Healey %A Faran Ahmad %A Varun Gupta %A Niloy Ganguly %B Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing %C Proceedings of Machine Learning Research %D 2020 %E William Hsu %F pmlr-v122-sinha20a %I PMLR %P 36--43 %U https://proceedings.mlr.press/v122/sinha20a.html %V 122 %X This paper presents preliminary results for developing an online "persuasion score" that will enable digital marketing content authors to compose and edit materials with better persuasive capability. Inspired by initial insights with digital marketing professionals and research on the foundations of persuasion: pathos, ethos and logos, we extracted features from a data set of over three million consumer reactions to email marketing campaigns covering a three month period. We report on the most significant features of the content, including image position and text readability as well as the most salient customer features such as time since registration and time since last opened email from the same marketing brand.
APA
Sinha, M., Healey, J., Ahmad, F., Gupta, V. & Ganguly, N.. (2020). Persuasion: What Jane Austin Would Have Written. Proceedings of IJCAI 2019 3rd Workshop on Artificial Intelligence in Affective Computing, in Proceedings of Machine Learning Research 122:36-43 Available from https://proceedings.mlr.press/v122/sinha20a.html.

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