@inproceedings{obadic-etal-2022-nlpop,
title = "{NLPOP}: a Dataset for Popularity Prediction of Promoted {NLP} Research on {T}witter",
author = "Obadi{\'c}, Leo and
Tutek, Martin and
{\v{S}}najder, Jan",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Barriere, Valentin and
Tafreshi, Shabnam and
Alqahtani, Sawsan and
Sedoc, Jo{\~a}o and
Klinger, Roman and
Balahur, Alexandra",
booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wassa-1.32",
doi = "10.18653/v1/2022.wassa-1.32",
pages = "286--292",
abstract = "Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research. The trend of researchers promoting work not yet peer-reviewed (preprints) by posting concise summaries presented itself as an opportunity to collect and combine multiple modalities of data. In scope of this paper, we (1) construct a dataset of Twitter threads in which researchers promote NLP preprints and (2) evaluate whether it is possible to predict the popularity of a thread based on the content of the Twitter thread, paper content and user metadata. We experimentally show that it is possible to predict popularity of threads promoting research based on their content, and that predictive performance depends on modelling textual input, indicating that the dataset could present value for related areas of NLP research such as citation recommendation and abstractive summarization.",
}
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%0 Conference Proceedings
%T NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter
%A Obadić, Leo
%A Tutek, Martin
%A Šnajder, Jan
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Alqahtani, Sawsan
%Y Sedoc, João
%Y Klinger, Roman
%Y Balahur, Alexandra
%S Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F obadic-etal-2022-nlpop
%X Twitter has slowly but surely established itself as a forum for disseminating, analysing and promoting NLP research. The trend of researchers promoting work not yet peer-reviewed (preprints) by posting concise summaries presented itself as an opportunity to collect and combine multiple modalities of data. In scope of this paper, we (1) construct a dataset of Twitter threads in which researchers promote NLP preprints and (2) evaluate whether it is possible to predict the popularity of a thread based on the content of the Twitter thread, paper content and user metadata. We experimentally show that it is possible to predict popularity of threads promoting research based on their content, and that predictive performance depends on modelling textual input, indicating that the dataset could present value for related areas of NLP research such as citation recommendation and abstractive summarization.
%R 10.18653/v1/2022.wassa-1.32
%U https://aclanthology.org/2022.wassa-1.32
%U https://doi.org/10.18653/v1/2022.wassa-1.32
%P 286-292
Markdown (Informal)
[NLPOP: a Dataset for Popularity Prediction of Promoted NLP Research on Twitter](https://aclanthology.org/2022.wassa-1.32) (Obadić et al., WASSA 2022)
ACL