Table 1.
Manual classification of Twitter users who tweet about e-cigarettes: user type definitions and proportion of each type in manually labeled sample.
| Type | Definition | Sample, N | |
| Individual | The account of a real person whose Twitter profile information and tweets reflect their individual thoughts and interests. An individual is someone whose primary post content is not about vaping. | 2168 | |
| Vaper enthusiast | The account of a person or organization whose primary content is related to promoting e‑cigarettes but is not primarily trying to sell e‑cigarettes or related products. | 334 | |
| Informed agencya | 622 | ||
| News media | The account of a newspaper, magazine, news channel, etc. News media does not include vaping-specific news sources. | ||
| Health community | The account of a public health organization, coalition, agency, or credible individual affiliated with an organization. These may also be the accounts of organizations with authority on a topic that should be thought of as trusted sources. | ||
| Marketera | 752 | ||
| Marketer | An account marketing e‑cigarette or vaping products. These accounts can belong to a Web-based or brick-and-mortar retailer or an individual who is an affiliate marketer. | ||
| Information aggregator | An account that primarily aggregates information about e‑cigarettes/vaping and where most or all tweets are news articles related to e‑cigarettes/vaping. This account could also aggregate vaping coupons or deals. | ||
| Spammer | An account that does not fall into one of the other coding categories. These accounts often post on a broad range of topics unrelated to this project, and their content can be nonsensical. Anecdotally, it was observed that many of these accounts exhibited bot behaviors. | 1021 | |
aDuring manual annotation of data, we initially categorized subtypes of informed agency (ie, news media and health community) and marketer (ie, marketer and information aggregator) user types, but we did not identify sufficient numbers of user handles for these subtypes to conduct meaningful analyses. Thus, during the feature selection and modeling phases, we collapsed across user subtypes to define five total user types.