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Authors: Julianne Zech 1 ; Lisa Singh 1 ; Kornraphop Kawintiranon 1 ; Naomi Mezey 2 and Jamillah Bowman Williams 2

Affiliations: 1 Department of Computer Science, Georgetown University, Washington DC, U.S.A. ; 2 Georgetown University Law Center, Washington DC, U.S.A.

Keyword(s): Social Media, #MeToo, Experience Prediction, Machine Learning Models, Neural Models.

Abstract: The #MeToo movement is one of several calls for social change to gain traction on Twitter in the past decade. The movement went viral after prominent individuals shared their experiences, and much of its power continues to be derived from experience sharing. Because millions of #MeToo tweets are published every year, it is important to accurately identify experience-related tweets. Therefore, we propose a new learning task and compare the effectiveness of classic machine learning models, ensemble models, and a neural network model that incorporates a pre-trained language model to reduce the impact of feature sparsity. We find that even with limited training data, the neural network model outperforms the classic and ensemble classifiers. Finally, we analyze the experience-related conversation in English during the first year of the #MeToo movement and determine that experience tweets represent a sizable minority of the conversation and are moderately correlated to major events.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Zech, J.; Singh, L.; Kawintiranon, K.; Mezey, N. and Williams, J. (2022). Inferring #MeToo Experience Tweets using Classic and Neural Models. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-583-8; ISSN 2184-285X, SciTePress, pages 107-117. DOI: 10.5220/0011278100003269

@conference{data22,
author={Julianne Zech. and Lisa Singh. and Kornraphop Kawintiranon. and Naomi Mezey. and Jamillah Bowman Williams.},
title={Inferring #MeToo Experience Tweets using Classic and Neural Models},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA},
year={2022},
pages={107-117},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011278100003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA
TI - Inferring #MeToo Experience Tweets using Classic and Neural Models
SN - 978-989-758-583-8
IS - 2184-285X
AU - Zech, J.
AU - Singh, L.
AU - Kawintiranon, K.
AU - Mezey, N.
AU - Williams, J.
PY - 2022
SP - 107
EP - 117
DO - 10.5220/0011278100003269
PB - SciTePress