A multi-site intervention using influencers to communicate about syringe service programmes.

Introduction: In the USA, syringe services programmes (SSPs) provide a range of harm reduction services and have numerous benefits for communities. However, stigma, misconceptions about SSPs and changing policies/legislation remain a challenge to effective implementation. This study reviews the implementation of two digital interventions, Appalachian Influence and Shared Influence, which used social media influencers and digital volunteers to communicate positive information about harm reduction and SSPs. Methods: The intervention was designed to deliver accurate and supportive messaging in locally relevant and meaningful ways. Messaging was informed by interviews with subject matter experts and community organisations, and was integrated into prompts used by local influencers (paid individuals with more than 1,000 followers) and digital volunteers (unpaid individuals with no following requirement, who joined the project independently). Results: In the first 6 months of implementation, Appalachian Influence and Shared Influence engaged a total of 9,014 individuals, 236 of whom were paid influencers and 8,778 of whom were digital volunteers. Paid influencer posts achieved a total of 868,943 impressions, 42,432 engagements and 1,567 comments. Comments on paid influencer posts were overwhelmingly positive, with 87.4% positive and 0.8% negative. Interviews showed the importance of understanding local realities, leading with compassion and emphasising the 'human' aspects of dependency and addiction in messaging. Conclusion: This study shows the potential to implement an influencer-led social media intervention to reach people with authentic and compassionate messaging about harm reduction and SSPs. Future research should examine intervention effectiveness and how this approach can be applied to other stigmatised topics.

Mental Health Stigma Reduction in the Midwestern United States: Evidence from a Digital Campaign Using a Collective Impact Model.

Addressing mental stigma is a key component of improving mental health outcomes. A digital media campaign was implemented to reduce mental health stigma in the Omaha Metropolitan area. The campaign used evidence-based approaches within a collective impact framework. Two surveys were conducted at baseline and at 10-month follow-up to evaluate the campaign within the Omaha and Council Bluffs intervention region, and a control region in Iowa. Analysis revealed significant improvements in desires for social distance and perceptions toward treatment efficacy within the intervention group. Improvements were seen across measures of personal and community attitudes towards mental health conditions, confidence in supporting others, and likelihood of disclosing a mental health condition. The trends were generally not replicated within the control group. Respondents who were aware of the campaign showed fewer stigmatizing views, including lower desires for social distance, improved attitudes toward treatment, and significant improvements in providing support and caring for their own mental health. The results suggest that the implemented evidenced-based approach could potentially create positive shifts in stigma reduction. This evaluation further supports the potential for scaling and adapting digital media campaigns for stigma reduction in different geographic locations.

Social media influencers can be used to deliver positive information about the flu vaccine: findings from a multi-year study.

Large-scale digital flu vaccine campaigns have experienced difficulty increasing vaccination coverage among African Americans and Hispanics, and are routinely inundated by negative responses from vaccine opponents. A digital campaign employing user-generated content from social media 'micro' influencers who are predominantly followed by African Americans and Hispanics was implemented during the 2018-19 and 2019-20 flu seasons to disseminate positive information about the flu vaccine. At the time, this constituted the largest influencer-driven health campaign focused on these communities in the United States. Comments on posts were qualitatively coded to determine content perceptions among those exposed to posts. Digital metrics were also analyzed. During Year 1, posts reached 9 million+ social media users and generated 64 612 likes or shares, and 1512 responses. In Year 2, posts reached 8 million+ users and generated 155 600 likes or shares, and 3122 responses. Around 94% of public responses to posts were positive, suggesting this is a promising strategy to communicate health information and could shift social norms, particularly for heavily debated topics such as vaccination. This strategy represents a more community-led and participatory approach than most large-scale vaccination campaigns have attempted, with immediate applicability to communications about the COVID-19 vaccine.

[Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019]. / Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019.

OBJECTIVES: To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. METHODS: We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition. RESULTS: The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes. CONCLUSIONS: Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019

[RESUMEN]. Objetivo. Informar sobre la oposición a las vacunas y la información errónea fomentadas en Twitter, destacando las cuentas de Twitter que dirigen estas conversaciones. Métodos. Utilizamos el aprendizaje automático supervisado para codificar todos los mensajes publicados en Twitter. En primer lugar, identificamos manualmente los códigos y los temas mediante un enfoque teórico fundamentado y, a continuación, los aplicamos a todo el conjunto de datos de forma algorítmica. Identificamos a los 50 autores más importantes un mes tras otro para determinar las fuentes influyentes de información relacionadas con la oposición a las vacunas. Resultados. El período de recopilación de datos fue del 1 de junio al 1 de diciembre del 2019, lo que dio lugar a 356 594 mensajes opuestos a las vacunas. Un total de 129 autores de Twitter reunieron los criterios de autor principal durante al menos un mes. Los autores principales fueron responsables del 59,5% de los mensajes opuestos a las vacunas y detectamos diez temas de conversación. Los temas se distribuyeron de forma similar entre los autores principales y todos los demás autores que declararon su oposición a las vacunas. Los autores principales parecían estar muy coordinados en su promoción de la información errónea sobre cada tema. Conclusiones. La salud pública se ha esforzado por responder a la información errónea sobre las vacunas. Los resultados indican que las fuentes de información errónea sobre las vacunas no son tan heterogéneas ni están tan distribuidas como podría parecer a primera vista, dado el volumen de mensajes. Existen fuentes identificables de información errónea, lo que puede ayudar a contrarrestar los mensajes y a fortalecer la vigilancia de la salud pública.

[ABSTRACT]. Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition. Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes. Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

Building Strong Futures: The Feasibility of Using a Targeted Digital Media Campaign to Improve Knowledge About Pregnancy and Low Birthweight Among Black Women.

OBJECTIVE: Low birthweight is one of the main causes of poor health outcomes among newborns, with Black women having a disproportionately high prevalence. A digital intervention targeted Black women in Orange County, Florida with information on positive pregnancy-related knowledge and attitudes related to low birthweight. This paper reports on campaign methods for the first 2.5 years of implementation. METHODS: Campaign content was tailored toward Black women, around a reproductive empowerment lens. Content focused on emphasizing healthy pregnancy-related behaviors and creating positive representations of Black women throughout the various stages of pregnancy through both static images and a web series. Digital metrics gauged campaign engagement. Three cross-sectional online surveys conducted in the intervention county examined Black women's pregnancy-related knowledge, attitudes, and behaviors. RESULTS: After two years of campaign implementation, social media accounts showed 1784 followers. While Facebook showed more average monthly impressions, Instagram showed more average monthly engagements. Survey results showed some increases in knowledge about prenatal care, weight gain, exercise, and the health impacts of low birthweight. CONCLUSIONS FOR PRACTICE: This study highlights the potential for a culturally-appropriate digital intervention to promote positive pregnancy outcomes among at-risk women. Digital interventions offer a potential way to achieve positive pregnancy-related behavior changes on a larger scale. This may be particularly important given that the COVID-19 pandemic may be changing the ways that pregnant women access information. Studies should examine the impact and feasibility of using culturally-appropriate digital interventions that directly address Black women and their specific experiences during pregnancy.

Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019 / Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019

RESUMEN Objetivo. Informar sobre la oposición a las vacunas y la información errónea fomentadas en Twitter, destacando las cuentas de Twitter que dirigen estas conversaciones. Métodos. Utilizamos el aprendizaje automático supervisado para codificar todos los mensajes publicados en Twitter. En primer lugar, identificamos manualmente los códigos y los temas mediante un enfoque teórico fundamentado y, a continuación, los aplicamos a todo el conjunto de datos de forma algorítmica. Identificamos a los 50 autores más importantes un mes tras otro para determinar las fuentes influyentes de información relacionadas con la oposición a las vacunas. Resultados. El período de recopilación de datos fue del 1 de junio al 1 de diciembre del 2019, lo que dio lugar a 356 594 mensajes opuestos a las vacunas. Un total de 129 autores de Twitter reunieron los criterios de autor principal durante al menos un mes. Los autores principales fueron responsables del 59,5% de los mensajes opuestos a las vacunas y detectamos diez temas de conversación. Los temas se distribuyeron de forma similar entre los autores principales y todos los demás autores que declararon su oposición a las vacunas. Los autores principales parecían estar muy coordinados en su promoción de la información errónea sobre cada tema. Conclusiones. La salud pública se ha esforzado por responder a la información errónea sobre las vacunas. Los resultados indican que las fuentes de información errónea sobre las vacunas no son tan heterogéneas ni están tan distribuidas como podría parecer a primera vista, dado el volumen de mensajes. Existen fuentes identificables de información errónea, lo que puede ayudar a contrarrestar los mensajes y a fortalecer la vigilancia de la salud pública.

ABSTRACT Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation. Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition. Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes. Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

Using social media influencers to increase knowledge and positive attitudes toward the flu vaccine.

Seasonal influenza affects millions of people across the United States each year. African Americans and Hispanics have significantly lower vaccination rates, and large-scale campaigns have had difficulty increasing vaccination among these two groups. This study assessed the feasibility of delivering a flu vaccination promotion campaign using influencers, and examined shifts in social norms regarding flu vaccine acceptability after a social media micro influencer campaign. Influencers were asked to choose from vetted messages and create their own original content promoting flu vaccination, which was posted to their social media pages. Content was intentionally unbranded to ensure that it aligned with the look and feel of their pages. Cross-sectional pre- and post-campaign surveys were conducted within regions that received the campaign and control regions to examine potential campaign impact. Digital metrics assessed campaign exposure. Overall, 117 influencers generated 69,495 engagements. Results from the region that received the campaign showed significant increases in positive beliefs about the flu vaccine, and significant decreases in negative community attitudes toward the vaccine. This study suggests that flu campaigns using a ground-up rather than top-down approach can feasibly reach at-risk groups with lower vaccination rates, and shows the potentials of using an influencer-based model to communicate information about flu vaccination on a large scale.

Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019.

Objectives. To report on vaccine opposition and misinformation promoted on Twitter, highlighting Twitter accounts that drive conversation.Methods. We used supervised machine learning to code all Twitter posts. We first identified codes and themes manually by using a grounded theoretical approach and then applied them to the full data set algorithmically. We identified the top 50 authors month-over-month to determine influential sources of information related to vaccine opposition.Results. The data collection period was June 1 to December 1, 2019, resulting in 356 594 mentions of vaccine opposition. A total of 129 Twitter authors met the qualification of a top author in at least 1 month. Top authors were responsible for 59.5% of vaccine-opposition messages. We identified 10 conversation themes. Themes were similarly distributed across top authors and all other authors mentioning vaccine opposition. Top authors appeared to be highly coordinated in their promotion of misinformation within themes.Conclusions. Public health has struggled to respond to vaccine misinformation. Results indicate that sources of vaccine misinformation are not as heterogeneous or distributed as it may first appear given the volume of messages. There are identifiable upstream sources of misinformation, which may aid in countermessaging and public health surveillance.

Evaluation of a campaign to reduce consumption of sugar-sweetened beverages in New Jersey.

Obesity is a leading cause of premature death in the U.S., in part due to consumption of sugar sweetened beverages (SSBs). In New Jersey, African Americans, Hispanics, and those of low income have the highest rates of SSB consumption. This study evaluates the impact of NJ Sugarfreed, a campaign designed to reduce sugar-sweetened beverage (SSB) consumption across New Jersey. From 12/1/17-9/30/18, we used a collective impact model to create targeted statewide campaigns that reduce SSB consumption among New Jersey residents, with an emphasis on African American and Hispanic low-income mothers/caregivers who are often gatekeepers to children's SSB consumption. Passaic County, New Jersey received a higher dose intervention. Messages were disseminated through social media, partner organizations, and community partnerships. Campaign impact was examined through evaluation surveys and analysis of beverage sales. Baseline and follow-up surveys (n = 800 baseline; n = 782 follow-up) showed positive trends toward decreased soda consumption and increased knowledge about SSBs. Passaic respondents showed a 5% decrease in those who consume 1+ soda per day, compared to a 1% decrease among New Jersey respondents. Analysis of overall SSB beverage sales showed the most pronounced decreases in Passaic (7% decrease) compared to New Jersey (6%). By drawing upon best practices in message development and the use of various platforms for dissemination, combined with community-based participation, we have provided more evidence to support the use of a collective impact model as a way of reducing unhealthy behaviors that impact health disparities.