SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text

TL;DR

This paper introduces a new dataset of 3144 tobacco-related tweets with fine-grained labels, enabling detailed classification of tobacco mentions, topics, and demographics for improved research and surveillance.

Contribution

The creation of a labeled Twitter dataset with hierarchical annotations for fine-grained tobacco-related classification and the demonstration of standard text classification methods on it.

Findings

Standard classifiers perform effectively on the dataset

Hierarchical classification enables detailed topic and demographic analysis

Dataset facilitates future sentiment and style analysis in tobacco research

Abstract

Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a fine-grained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification. This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.