@inproceedings{shrestha-etal-2016-age,
title = "Age and Gender Prediction on Health Forum Data",
author = "Shrestha, Prasha and
Rey-Villamizar, Nicolas and
Sadeque, Farig and
Pedersen, Ted and
Bethard, Steven and
Solorio, Thamar",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1541",
pages = "3394--3401",
abstract = "Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users{'} content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user{'}s age and gender from their forum posts. We use a mix of features from a user{'}s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.",
}
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<abstract>Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users’ content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user’s age and gender from their forum posts. We use a mix of features from a user’s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.</abstract>
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%0 Conference Proceedings
%T Age and Gender Prediction on Health Forum Data
%A Shrestha, Prasha
%A Rey-Villamizar, Nicolas
%A Sadeque, Farig
%A Pedersen, Ted
%A Bethard, Steven
%A Solorio, Thamar
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F shrestha-etal-2016-age
%X Health support forums have become a rich source of data that can be used to improve health care outcomes. A user profile, including information such as age and gender, can support targeted analysis of forum data. But users might not always disclose their age and gender. It is desirable then to be able to automatically extract this information from users’ content. However, to the best of our knowledge there is no such resource for author profiling of health forum data. Here we present a large corpus, with close to 85,000 users, for profiling and also outline our approach and benchmark results to automatically detect a user’s age and gender from their forum posts. We use a mix of features from a user’s text as well as forum specific features to obtain accuracy well above the baseline, thus showing that both our dataset and our method are useful and valid.
%U https://aclanthology.org/L16-1541
%P 3394-3401
Markdown (Informal)
[Age and Gender Prediction on Health Forum Data](https://aclanthology.org/L16-1541) (Shrestha et al., LREC 2016)
ACL
- Prasha Shrestha, Nicolas Rey-Villamizar, Farig Sadeque, Ted Pedersen, Steven Bethard, and Thamar Solorio. 2016. Age and Gender Prediction on Health Forum Data. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3394–3401, Portorož, Slovenia. European Language Resources Association (ELRA).