@inproceedings{wang-etal-2021-self,
title = "Self Promotion in {US} Congressional Tweets",
author = "Wang, Jun and
Cui, Kelly and
Yu, Bei",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.388",
doi = "10.18653/v1/2021.naacl-main.388",
pages = "4893--4899",
abstract = "Prior studies have found that women self-promote less than men due to gender stereotypes. In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. After analyzing 2 million Congressional tweets from July 2017 to March 2021, controlling for a number of factors that include political party, chamber, age, number of terms in Congress, number of daily tweets, and number of followers, we found that women in Congress actually perform more self-promotion on Twitter, indicating a reversal of traditional gender norms where women self-promote less than men.",
}
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<abstract>Prior studies have found that women self-promote less than men due to gender stereotypes. In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. After analyzing 2 million Congressional tweets from July 2017 to March 2021, controlling for a number of factors that include political party, chamber, age, number of terms in Congress, number of daily tweets, and number of followers, we found that women in Congress actually perform more self-promotion on Twitter, indicating a reversal of traditional gender norms where women self-promote less than men.</abstract>
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%0 Conference Proceedings
%T Self Promotion in US Congressional Tweets
%A Wang, Jun
%A Cui, Kelly
%A Yu, Bei
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-self
%X Prior studies have found that women self-promote less than men due to gender stereotypes. In this study we built a BERT-based NLP model to predict whether a Congressional tweet shows self-promotion or not and then used this model to examine whether a gender gap in self-promotion exists among Congressional tweets. After analyzing 2 million Congressional tweets from July 2017 to March 2021, controlling for a number of factors that include political party, chamber, age, number of terms in Congress, number of daily tweets, and number of followers, we found that women in Congress actually perform more self-promotion on Twitter, indicating a reversal of traditional gender norms where women self-promote less than men.
%R 10.18653/v1/2021.naacl-main.388
%U https://aclanthology.org/2021.naacl-main.388
%U https://doi.org/10.18653/v1/2021.naacl-main.388
%P 4893-4899
Markdown (Informal)
[Self Promotion in US Congressional Tweets](https://aclanthology.org/2021.naacl-main.388) (Wang et al., NAACL 2021)
ACL
- Jun Wang, Kelly Cui, and Bei Yu. 2021. Self Promotion in US Congressional Tweets. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4893–4899, Online. Association for Computational Linguistics.