9:35 - 9:55 amSunday, September 18
LK 101
Understanding online conversations: where, how and why to identify the right online influencers for effective health strategies
LK 101
Understanding online conversations: where, how and why to identify the right online influencers for effective health strategies
ePatient; Manager, Digital Marketing & Internal Communications
Background Where do patients turn for information when not in front of their health care providers (HCPs)? Online, of course. One of the biggest changes as a result of an Internet-enabled world is that... Read more

Description

Background
Where do patients turn for information when not in front of their health care providers (HCPs)? Online, of course. One of the biggest changes as a result of an Internet-enabled world is that HCPs are no longer the sole sources of health information. However, the 1:9:90 model of online behaviors tells us that only 1% online are creating content; 9% share and repackage content; and 90% primarily listen and learn. To influence the majority of people, first effectively reach and engage the 10%.

Hypothesis
Studying online posts categorized by hashtags will help identify how to identify the 1% creating content, plus the 9% sharing and repackaging content to distribute to their communities – key information for creating effective health communication strategies to target the appropriate audiences.

Method
To illustrate this with a practical example, an analysis was performed over a three-month period (June 1-August 31, 2015) to compare engagement levels by HCPs, media, and other individuals, from tweets with a variety* of diabetes-specific hashtags: ranging from generic hashtags to hashtags that “flag” posts, in addition to scheduled community hashtag chats and conference hashtags. During that time period, there were 46,321 tweets with those hashtags from 3,387 unique individuals. The following analyses were performed on the data set: hashtag usage; mention patterns and interactions; link and domain sharing patterns; and N-grams by 1, 2, and 3-word frequency – all by volume and audience.

*Hashtag groupings
Generic: #diabetes, #t1d, #type2diabetes, etc.
“Flags”: #dblog, #WeAreNotWaiting, #doc
Chats: #DSMA, #DCDE, #ourD
Conferences: #2015ADA/#ADA2015, #AADE15

Results
All hashtags are not created, nor should be used, equal.

#Diabetes is most used by all audiences, but patients and other individuals differ from HCPs and media by otherwise avoiding generic hashtags and instead frequently using hashtags around diabetes chats and communities (#doc, “diabetes online community”; #dblog, “diabetes blog”; #DSMA, weekly diabetes social media chat). HCPs frequently participate in conference-related hashtags, and less often directly engage in diabetes-specific chats
Different audiences share content from different sources. HCPs are more likely than patients to post links, and they tend to post diabetes-specific news and general resource information being shared by healthcare brands
1-dimensional metrics are not enough to assess the quality and dynamic engagement levels in online communities. Some online users try to “game” the system by frequently posting their own links or overrunning hashtags. To find the 10%, you must use additional metrics to validate who is creating original content (that we wouldn’t consider to be spamming) and others who are sharing. The same approach can be applied to tracking “@mentions” of other people and organizations, to better gauge conversationality and influence on varying topics, and to help determine if they’re in the 10% (the combination of 1% and 9% groups for creating, sharing, and repackaging content to their communities)
Influence should not purely be determined by number of followers or number of posts; the impact someone has on their network (regardless of size) may be even more meaningful. This methodology can help identify relative impact among individuals in a community or network
Conclusions This methodology shows that studying the content being shared across conversations and by different segments of healthcare, the channels (and hashtags) being used to distribute content, and the deeper dynamics of conversationality among community members, is key to enable the identification of influencers and the development of a more effective strategy. At a high level, this shows the consideration of individuals online based on their behavior and interactions is possible, and we no longer must solely rely on self-identification in online bios and follower counts to determine influence.

Dana Lewis created and moderates the internationally-recognized #hcsm (health care communications and social media) conversation and community on Twitter. She is also the manager of digital marketing and internal communications for Swedish (in Seattle, Washington), where she implements social and digital health strategies across the organization both internally with employees and externally to connect with patients and improve the patient experience. She is passionate about using technology to facilitate conversations and collaboration to benefit our communities and improve health care. She frequently speaks, writes, and teaches on topics related to the implementation and utilization of social media across health care.

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