Background: smbolic was tasked to create meaningful measurements for social media listening and analysis as it relates to the healthcare industry, specifically around governmental agency interests. In an era of Big Data, it is easy to gather lots of information but have no understanding of what it means. smbolic works (and has worked) to provide human insight (utilizing a handful of tools) to healthcare clients that want an understanding of their role in social media.
Program background: Since early 2013, smbolic has been working on two major projects: social media strategy for the Traveler’s Health division of the CDC and a one-year historical analysis on the intersection of Health IT and healthcare quality for the Agency for Healthcare Research and Quality (AHRQ).
Evaluation Methods and Results:
- Created and categorized a coherent set of search/listening terms
- Categories are important because terms are often best run in proximity to other related terms (otherwise known as Boolean searches).
- Began listening
- Depending upon budget, there are a plethora of listening tools in the marketplace to help understand the conversations. Each tool provides unique and valuable information.
- At smbolic, we have used Radian 6, Sysomos, and Ubervu as part of our toolkit.
- Reevaluated search terms based on human understanding of results
- Searches can often result in unintended information. For example: HIT and Quality will return a lot of baseball conversations (not relevant to Healthcare IT and Quality of Care conversations).
- Collected data
- Applied human analysis to results
- Spikes in conversations – how do they correlate to efforts taking place within the industry? What other events outside of organization’s control might lead to spikes?
- Sentiment understanding – do the tools truly understand sentiment? Is something that is considered neutral in fact positive because of the fact that it is being shared?
- Results
- Results can include strategies for responding and conversing with various audiences.
- Other instances, the results are used to identify the landscape and to begin laying the foundation for future social media plans.
Conclusions: While specific projects can create results, the process of active social media listening should be ongoing. The result over the long term is to truly understand the audiences and to develop deep relationships with those people. Moreover, it is important to determine data relevancy to use for future conversations on social media, discerning the interesting elements in social chatter.
Implications for research and/or practice: Utilizing a social media monitoring tool is not enough by itself. The tools provide information but do not provide the guidance needed to bridge the decision gap. And just because something is measured does mean that the information is meaningful. Both time and human insight are needed to be truly listening and then responding in the most appropriate manner. Social media needs to be viewed as an extension of personal conversations, responding to people appropriately after listening intently.