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Addict Behav. 2018 Nov 14. pii: S0306-4603(18)31314-5. doi: 10.1016/j.addbeh.2018.11.015. [Epub ahead of print]

Strategies to find audience segments on Twitter for e-cigarette education campaigns.

Author information

1
Center for Research on Media, Technology, and Health, University of Pittsburgh, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States. Electronic address: chuk@pitt.edu.
2
Department of Preventive Medicine, University of Southern California, 2001 North Soto Street, 3rd Floor, Los Angeles, CA 90032, United States.

Abstract

The development of public health education campaigns about tobacco products requires an understanding of specific audience segments including their views, intentions, use of media, perceived barriers, and benefits of change. For example, identifying and targeting individuals who express ambivalence about e-cigarette use on Twitter may be helpful in devising and focusing public health campaigns to reduce e-cigarette use. This study developed a novel analytic strategy using social network analysis to identify audience segments on Twitter based on positive, negative, and neutral e-cigarette sentiment. Using Twitter data collected from April 2015 to March 2016, we identified different sub-groups of users who retweeted about e-cigarettes, and measured each sub-group's clustering coefficient (CC), which describes how tightly people cluster together. Ten high CC and ten low CC groups were randomly selected; then 100 randomly selected tweets from each group were coded for e-cigarette sentiment (positive, negative, neutral). Results indicate that differences in e-cigarette sentiment are associated with clustering of Twitter network ties. Statistical analyses revealed that high CC groups were more likely to have strong e-cigarette sentiments, suggesting that tightly clustered groups may be "echo chambers" (i.e., like-minded people repeating the same messages). By contrast, low CC groups were more likely to have neutral sentiments, and had greater fluctuation in sentiment over time, suggesting that they may be more flexible in their opinions about e-cigarettes and may be particularly receptive to targeted public health campaigns. Informatics techniques such as determination of clusters using social network analysis can be useful in identifying audience segments for future public health campaigns.

KEYWORDS:

E-cigarette; Sentiment analysis; Social network analysis; Tobacco regulatory science; Twitter

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