Study Centers (SCs) utilizing Facebook as part of their community outreach and engagement efforts were invited to participate. Four SCs (Queens County, New York; Waukesha County, Wisconsin; San Diego County, California; and Cumberland County, Maine) elected to participate thus providing geographic diversity.

Participating SCs collected 4 months of data between December 2011 and March 2012 from various social media accounts and websites (see Table 1). Only Facebook data is presented in this paper because it was the only modality consistently used by all of the participating locations. SCs met via 4 monthly conference calls to report and discuss social media use. All SCs downloaded their Facebook data and provided it to the MSC for analysis.

We used several metrics in our Facebook analysis. We started with basic Facebook use metrics including lifetime number of likes, average likes per month (number of lifetime likes divided by the number of months on Facebook) and number of posts. At the time of this analysis, EdgeRank score (see Textbox 1) was collected as an effectiveness measure because it is an algorithm developed by Facebook to govern what is displayed on the News Feed [18]. The EdgeRank formula includes the variables of affinity, time, and weight [19]. An EdgeRank score of 20 or higher is generally considered by social media experts as positive for potential exposure in the News Feed [20].

A multitude of metrics exist in Facebook to describe engagement but at the time of this analysis there was no standard metric for engagement with content. We conducted a literature search of existing Facebook metrics for assessing user engagement using Facebook Insights data [21-24]. Although no single common metric exists, a combined Insights data set is commonly used, albeit with distinctly different approaches. Facebook collects deidentified data for pages, as well as for individual posts (see Textbox 1). Page level data describes how users engage with the Facebook page, offering cumulative metrics for fans, reach, and engagement. Post level data is collected for each individual post, yielding a more focused assessment of what prompts fans (users who have liked the Facebook page) to interact with individual posts.

In page level analyses, the rate of Page Consumption (number of page clicks or video views) over Total Daily Page Reach (number of people who saw your page content) is commonly used as an engagement metric, however Page Consumption does not include post likes, shares, or comments. In Post level analyses, current engagement metrics measure the rate of comments and likes on a post over the number of impressions (number of times a post is displayed, regardless of whether it is seen or not in the News Feed).

The Facebook pages used in this study had a relatively low number of page likes. SC Facebook pages count likes in the mid to low hundreds compared to commercial products that count fans in the millions (Nike has over 22 million fans). The engagement metrics for Page and Post level Insights described above did not accurately describe the activity we saw on the pages. To better understand how our fans engaged with our posts, we determined that it was essential to include all activities of engagement when analyzing our data. This included any interaction between a fan and a post—any click on a post, like, comment, or share. We chose to use Post Total Reach in our engagement analysis as well. Post Total Reach is the total number of unique people who see the post in their News Feed, as defined and measured by Facebook.

Content analysis examines the topics that SCs posted about during the study period. Since Facebook does not collect metrics about the content of posts, we developed a unique analysis, conducted by collecting SC posts and assigning each post to one of nine content categories, classifying the overall intent of each post (see Textbox 2). Inter-rater reliability testing of post-content classification was performed with four raters assigning posts to categories and produced a kappa ranging from 0.67-0.75 indicating adequate inter-rater agreement.

By assigning each post to one category, the prevalence, or frequency of a SC posting about that topic was calculated. Prevalence of each post category was represented as a percentage of the total posts (see Figure 1). Using the lifetime total reach and lifetime engaged user metrics from each post, an overall total reach and engagement was summed for each content category. The engagement score was then calculated by dividing the total engagement for a category by its total reach. This method of analysis was applied to each study center.

This project was declared exempt from review by the Maine Medical Center Institutional Review Board. All social media activity strictly followed NCS social media policies [17,25].

The 4 sites experienced an average of 229 (range 185-299) Facebook Likes in December 2011; by the completion of the pilot in March 2012, this average jumped to 257 (range 219-321), an average increase of 28 likes (range 6-45) over the pilot period. Likes is a cumulative metric over the period of Facebook use, therefore we calculated “Likes per Month”. The SC Facebook pages averaged 16 (range 12-18) months of publishing and the Likes per Month ranged from 12 to 23 (see Figure 2). SCs averaged 17 posts per month (range 7-35) and generally increased to 37 posts per month (range 13-53) over the study period.

EdgeRank scores for the strength of post placement in the News Feed were variable over the sites, as shown in Figure 3, and the monthly average by site ranged between 34 and 42 (see Figure 3). We expect a positive correlation between EdgeRank scores and number of posts [19,20]. Waukesha County, WI and Queens County, NY demonstrates this relationship, however, Cumberland County, ME does not. As the number of posts increases in Cumberland County, ME, the EdgeRank score decreases.

For each SC we analyzed the post content throughout the pilot period (Figure 1). Analysis of post content showed that SCs posted most commonly about health education (24.3%, (range 21%-27%)), activity (14.3%, (range 8%-21%)), and nutrition (13.8%, (range 13%-16%)). Content of showed some variability between SCs. For example, San Diego County, CA posted mostly about health awareness issues (27%), while Waukesha County, WI posted most frequently about children’s topics (21%).

Table 2 shows the engagement analysis for each Study Center by content category. We calculated the overall engagement per view as well as the overall views per post for each Study Center. There was some variation in the views per post (ranging from 66.095-84.875) and engagement per view (range 0.053-0.039). Posts about the NCS have high engagement per view across all Study Centers. Posts about holidays had the highest views in Waukesha County, WI, while posts about nutrition had the most views per post in Cumberland County, ME. Additionally, some Study Centers had high engagement on activity and nutrition-related content. Content analysis demonstrated overall low engagement scores with health education topics, but regional variation was seen with other topics. Waukesha experienced high engagement on posts related to activity or awareness, but the other sites had significantly lower engagement in these categories. Interestingly, three sites (Cumberland County, ME; Queens County, NY; and San Diego County, CA) experienced higher engagement with child related content, but Waukesha County, WI experienced little engagement relative to the number of posts about this content.

Interestingly, in Cumberland County, ME, we notice an inverse relationship between views per post and engagement per post. When looking at the overall engagement in Cumberland County, ME, (Table 2) many people saw each post as reported by the high views per post, yet few people engaged with post as indicated by the lowest rate of engagement per view. This pattern supports an acknowledged relationship in Facebook where as a Page reaches more people, a lower proportion of that audience will engage with content. By comparing the engagement of each content category to the overall engagement, we can derive which topics are of most interest to each Study Center and tailor social media content to local community interests.

Social media use is new in research recruitment and retention and holds significant potential, however there are currently no well defined best practices and metrics for evaluation.

We describe here a pilot project testing the feasibility of data collection through Facebook, as well as the creation and analysis of social media metrics from multiple SCs with the primary goal of determining best practices for future use. Primarily, this pilot demonstrated the feasibility of creating linkages and databases to collect and measure social media metrics from multiple SCs for comparison of content and engagement. Shared posting schedules and best practices combined with coordinated engagement (eg liking each other’s posts to increase reach) contributed to a measurable increase in engagement during the study period.

Facebook was the only social media modality evaluated in this pilot study. Assessment of engagement across SCs and modalities was complicated by a lack of commonly used and/or accepted metrics. We describe here basic Facebook metrics, along with the nascent creation of content and engagement analysis metrics tailored for NCS evaluation. Likes are commonly used as a measure of engagement; however, likes are garnered and not often lost. Therefore likes are a cumulative, static metric and not a moving metric like engagement. Number of likes does not indicate engagement, but does represent a “fan base” and potential for engagement. The difference in likes per month demonstrates that these online communities can be quickly generated (see Figure 2). For example, Cumberland County, Maine was able to yield a similar number of likes as the two NCS locations that were the most established on Facebook in a shorter amount of time, although other factors (eg authorization for mass communications campaigns and the use of social media) must be considered in interpretation. Because of the limitations of using likes in measuring engagement, we describe here the evaluation of new engagement metrics.

EdgeRank is commonly used to measure overall success and impact on Facebook, however it does not allow analysis of the content of posts, nor does it inform on how to serve interesting and engaging content to individual communities. The algorithm that EdgeRank uses includes variables of affinity, time, and weight [18-20]. Cumberland County, Maine’s EdgeRank scores continued to be low despite posting significantly more frequently in the last study month, which, as a variable in EdgeRank’s algorithm, should have yield an increased score (see Figure 3). This example highlights the challenges of using this algorithm to see and respond to our community’s variable interests in content.

Content and engagement analysis yielded new and improved metrics for the use of social media in the NCS. These analyses allowed us to describe what content of posts SCs were using, what content they were posting about, and what the online NCS communities found most engaging (see Figure 1). Engagement metrics shed light on where to focus time and effort in determining the content of posts (see Table 2). The purpose of this study was to demonstrate the ability to measure differences in engagement, yet an important realization is that not all centers should necessarily post the same content—rather the content posted should reflect their community and study activity. Questions remain; What messages were different between centers when it came to the topics of children? What role does localized content play in engagement on Facebook? Future research could further explore the role of content on engagement, as well as the optimal metrics to describe engagement in the research setting.

This pilot project had several limitations and challenges. This pilot study was not a representative sample of all SCs. The study proposed new metrics that lack baseline data to which to compare our engagement scores, making it difficult to interpret the data relative to other Facebook sites. We minimized subjectivity through inter-rater analysis, however, subjectivity in content categories and the sorting of posts must be acknowledged. The use of social media in the research setting is necessarily restricted by regulations for human subject protections. As such, we were limited in the content we could post and were unable to directly solicit engagement from our target market— NCS participants— and instead targeted the general community corresponding to each Facebook site. While ethical review of using social media to support the conduct of research is evaluated by federal restrictions through institutional review, many social media users neither read nor understand complex social media terms of service agreements and may not see themselves as potential research subjects. This latter point will continue to be a source of challenge for this field of study.

This pilot developed the infrastructure to analyze social media use in 4 SCs in the NCS, as well as demonstrating some preliminary best practices. Our analysis supports the use of social media, specifically Facebook, to increase awareness of the Study. Facebook’s strengths include the ability to engage in a two way dialogue and the open sharing of information, but understanding how best to utilize this forum’s user base and strengths for the changing goals and regulations of the use of social media in research. Further research examining content analysis, the role of localized of content and stakeholders, and social media use in participant recruitment is warranted.