In this paper, we investigate the feasibility of mining the heterogeneous data scattered on social media to identify drinking-related contents, which is the ...
Our results show that combining heterogeneous data types, we are able to identify drinking related posts with an overall accuracy of 82%. Prediction models ...
Detecting Drinking-Related Contents on Social Media by Classifying Heterogeneous Data Types. https://doi.org/10.1007/978-3-319-60045-1_38 ·.
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Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81.
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Apr 29, 2024 · Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of ...
Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81.
This project investigated the feasibility of mining the heterogeneous data (e.g. text, images, and videos) on Facebook to identify drinking-related contents ...
Detecting drinking-related contents on social media by classifying heterogeneous data types. O ElTayeby, T Eaglin, M Abdullah, D Burlinson, W Dou, L Yao.
Our machine learning models show that by combining heterogeneous data types, we were able to identify drinking-related posts with an F1-score of 0.81.