Cited By
View all- Sumikawa YJatowt A(2020)Analyzing history-related posts in twitterInternational Journal on Digital Libraries10.1007/s00799-020-00296-2Online publication date: 28-Oct-2020
Generating Labeled Datasets of Twitter Users
Pages 191 - 196
Abstract
In this paper we present a simple, yet powerful approach to generating labeled datasets of Twitter1 users. Our focus falls on sensitive personal details, shared as background information in tweets. Such tweets avoid the focus of user's attention and also tend to resist the vast amounts of humor, wishes or hypothetical thinking typical for tweets.
Our approach combines selecting search queries, followed up by a semi-supervised filtering of indicative messages. We create datasets in several unrelated domains and prove that all sorts of target groups can be built with minimal manual annotator effort.
The generated datasets include separate groups of users with specific characteristics: pet ownership, blood pressure, diabetes and psychotropic medicine usage, for which to our knowledge manually labeled data was previously not available.
Our search-based approach is also used to generate a cross-domain corpus, matching Twitter users with their Yelp2 profiles.
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Marco Vicente, Fernando Batista, and Joao Paulo Carvalho. 2016. Creating Extended Gender Labelled Datasets of Twitter Users. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer, 690--702.
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Index Terms
- Generating Labeled Datasets of Twitter Users
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Published In
July 2017
456 pages
ISBN:9781450350679
DOI:10.1145/3099023
- Editors:
- Marko Tkalcic,
- Dhaval Thakker,
- Panagiotis Germanakos,
- Kalina Yacef,
- Cecile Paris,
- Olga Santos,
- General Chairs:
- Maria Bielikova,
- Eelco Herder,
- Program Chairs:
- Michel Desmarais,
- Federica Cena
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Publication History
Published: 09 July 2017
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- National Science Fund
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UMAP '17: 25th Conference on User Modeling, Adaptation and Personalization
July 9 - 12, 2017
Bratislava, Slovakia
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Overall Acceptance Rate 162 of 633 submissions, 26%
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View all- Sumikawa YJatowt A(2020)Analyzing history-related posts in twitterInternational Journal on Digital Libraries10.1007/s00799-020-00296-2Online publication date: 28-Oct-2020
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