Dataset Information

Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA.

ABSTRACT:

Background

Little empirical evidence exists on the effectiveness of using Twitter as a two-way communication tool for public health practice, such as Twitter chats.

Objective

We analyzed whether Twitter chats facilitate engagement in two-way communications between public health entities and their audience. We also describe how to measure two-way communications, incoming and outgoing mentions, between users in a protocol using free and publicly available tools (Symplur, OpenRefine, and Gephi).

Methods

We used a mixed-methods approach, social network analysis, and content analysis. The study population comprised individuals and organizations participating or who were mentioned in the first #LiveFitNOLA chat, during a 75-min period on March 5, 2015, from 12:00 PM to 1:15 PM Central Time. We assessed audience engagement in two-way communications with two metrics: engagement ratio and return on engagement (ROE).

Results

The #LiveFitNOLA chat had 744 tweets and 66 participants with an average of 11 tweets per participant. The resulting network had 134 network members and 474 engagements. The engagement ratios and ROEs for the #LiveFitNOLA organizers were 1:1, 40% (13/32) (@TulanePRC) and 2:1, -40% (-25/63) (@FitNOLA). Content analysis showed information sharing (63.9%, 314/491) and health information (27.9%, 137/491) as the most salient theme and sub-theme, respectively.

Conclusions

Our findings suggest Twitter chats facilitate audience engagement in two-way communications between public health entities and their audience. The #LiveFitNOLA organizers' engagement ratios and ROEs indicated a moderate level of engagement with their audience. The practical significance of the engagement ratio and ROE depends on the audience, context, scope, scale, and goal of a Twitter chat or other organized hashtag-based communications on Twitter.

SUBMITTER: Rabarison KM

PROVIDER: S-EPMC5481664 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA.

Rabarison Kristina M KM Croston Merriah A MA Englar Naomi K NK Bish Connie L CL Flynn Shelbi M SM Johnson Carolyn C CC

JMIR public health and surveillance 20170608 2

<h4>Background</h4>Little empirical evidence exists on the effectiveness of using Twitter as a two-way communication tool for public health practice, such as Twitter chats.<h4>Objective</h4>We analyzed whether Twitter chats facilitate engagement in two-way communications between public health entities and their audience. We also describe how to measure two-way communications, incoming and outgoing mentions, between users in a protocol using free and publicly available tools (Symplur, OpenRefine, ...[more]

PMID: 28596149

Similar Datasets

Project description:BackgroundPublic health organizations have begun to use social media to increase awareness of health harm and positively improve health behavior. Little is known about effective strategies to disseminate health education messages digitally and ultimately achieve optimal audience engagement.ObjectiveThis study aims to assess the difference in audience engagement with identical antismoking health messages on three social media sites (Twitter, Facebook, and Instagram) and with a referring link to a tobacco prevention website cited in these messages. We hypothesized that health messages might not receive the same user engagement on these media, although these messages were identical and distributed at the same time.MethodsWe measured the effect of health promotion messages on the risk of smoking among users of three social media sites (Twitter, Facebook, and Instagram) and disseminated 1275 health messages between April 19 and July 12, 2017 (85 days). The identical messages were distributed at the same time and as organic (unpaid) and advertised (paid) messages, each including a link to an educational website with more information about the topic. Outcome measures included message engagement (ie, the click-through rate [CTR] of the social media messages) and educational website engagement (ie, the CTR on the educational website [wCTR]). To analyze the data and model relationships, we used mixed effects negative binomial regression, z-statistic, and the Hosmer-Lemeshow goodness-of-fit test.ResultsComparisons between social media sites showed that CTRs for identical antitobacco health messages differed significantly across social media (P<.001 for all). Instagram showed the statistically significant highest overall mean message engagement (CTR=0.0037; 95% CI 0.0032-0.0042), followed by Facebook (CTR=0.0026; 95% CI 0.0022-0.0030) and Twitter (CTR=0.0015; 95% CI 0.0013-0.0017). Facebook showed the highest as well as the lowest CTR for any individual message. However, the message CTR is not indicative of user engagement with the educational website content. Pairwise comparisons of the social media sites differed with respect to the wCTR (P<.001 for all). Messages on Twitter showed the lowest CTR, but they resulted in the highest level of website engagement (wCTR=0.6308; 95% CI 0.5640-0.6975), followed by Facebook (wCTR=0.2213; 95% CI 0.1932-0.2495) and Instagram (wCTR=0.0334; 95% CI 0.0230-0.0438). We found a statistically significant higher CTR for organic (unpaid) messages (CTR=0.0074; 95% CI 0.0047-0.0100) compared with paid advertisements (CTR=0.0022; 95% CI 0.0017-0.0027; P<.001 and P<.001, respectively).ConclusionsOur study provides evidence-based insights to guide the design of health promotion efforts on social media. Future studies should examine the platform-specific impact of psycholinguistic message variations on user engagement, include newer sites such as Snapchat and TikTok, and study the correlation between web-based behavior and real-world health behavior change. The need is urgent in light of increased health-related marketing and misinformation on social media.

Project description:The Covid-19 pandemic has highlighted the importance of citizens' behaviors in the containment of the virus. Individuals might change their intention to adhere to public health prescriptions depending on various personal characteristics, including their own emotional status, which has been recognized to be a crucial psychological factor in orienting people's adherence to public health recommendation during emergency settings. In particular, it is crucial to support citizens' alliance with authorities and feeling of trust: public engagement is a concept that refers to the general involvement of citizens into public affairs which is generally considered an effective approach to enhance citizens' understanding of their crucial role in public affairs. However, so far there is no agreement on the metrics and indexes that should be used to measures public engagement during a health crisis. The aim of this paper is to validate a psychometric scale (PHEs-E), which intends to measure the readiness of individuals to adhere to the prescribed behavioral change to contain the emergency. Data were collected throughout the pandemic in Italy: in particular, five independent samples were recruited starting from March 2020 to March 2021. Results showed that the proposed measure has good psychometric characteristics. A general linear model was computed to assess the differences of public engagement across the different data points and among citizens with different sociodemographic characteristics. Correlations with other psychological constructs (i.e. Anxiety, Depression and Self-Efficacy) were also tested, showing that more engaged citizens have a lower level of anxiety and depression, and a higher self-efficacy. This study's findings indicate that individuals' characteristics may differentiate citizens' motivation to engage in public health behavioral recommendation to prevent the COVID-19 contagion. However the scale could be useful to perform a psychological monitoring of psychological readiness to engage in public health strategies to face critical events and settings.

Dataset Information

Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA.

Background

Objective

Methods

Results

Conclusions

Publications

Measuring Audience Engagement for Public Health Twitter Chats: Insights From #LiveFitNOLA.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets