ISSN: 1550-7521

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Healthcare hashtag index development: distinguishing international impact in social media

John Wilmsherst*

Department of Civil Engineering, Aristotle University, Greece

*Corresponding Author:
John Wilmsherst
Department of Civil Engineering, Aristotle University, Greece

Received: 30-Nov-2022, Manuscript No. gmj-22-84459; Editor assigned: 02Dec-2022, PreQc No. 84459 (PQ); Reviewed: 16-Dec-2022, QC No. gmj-22-84459; Revised: 21-Dec-2022, Manuscript No. gmj-22-84459 (R); Published: 28-Dec-2022, DOI: 10.36648/1550-7521.20.58.344

Citation: Wilmsherst J (2022) Healthcare Hash Tag Index Development: Distinguishing International Impact in Social Media. Global Media Journal, 20:58.

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Purpose: Create associate degree index of worldwide reach for care hashtags and tweeters in that, filterable by topic of interest.

Materials and Ways: For this proof-of-concept study we tend to targeted on the sphere of medical aid and family practice. Six hashtags were elite supported their importance, from those enclosed within the ‘Healthcare Hash tag Project’. Hash tag international Reach (HGR) was calculated victimisation the additive aggregation of 5 weighted, normalized indicator variables: range of impressions, tweets, tweeters, user locations, and user languages. Information was obtained for the half-moon of 2014 and half-moon of 2015 victimisation Symplur Signals. Topic-specific HGR were calculated for the highest ten terms and for sets of quotes mapped once a thematic analysis. Individual international Reach, IGR, was calculated across hashtags as additive indexes of 3 indicators: replies, retweets and mentions.

Results: Using the HGR score we tend to were ready to rank six elite hashtags and observe their performance throughout the study amount. we tend to found that #PrimaryCare and #FMRevolution had the very best HGR score in each quarters; apparently, #FMChangeMakers tough a marked increase in its international visibility throughout the study amount. “Health Policy” was the most common theme, whereas “Care”, “Family” and “Health” were the foremost common terms.

Discussion: This is the primary study describing associate degree altmetric hash tag index. Forward analytical soundness, the Index may prove generalizable to different care hashtags. If free as a period of time business intelligence tool with customizable settings, it may aid business and strategic choices by netizens, organizations, and analysts. IGR may conjointly serve to enhance educational analysis and skilled development.

Conclusion: Our study demonstrates the feasibleness of victimisation associate degree index on the world reach of care hash tags and tweeter.


Primary health care; Social media; Family practice; Bibliometrics


Social media use has up exponentially every year on a world scale. As a gaggle of internet-based applications, they permit for the exchange of user-generated content, building on the idea of internet a pair of.0. These applications embody blogs, discussion boards, wikis, and social networking sites. within the medical domain, social media square measure [increasingly progressively more associate degraded more] employed by clinicians and researchers as an economical approach of sharing data, keeping up-to-date with knowledge base and collaborating with each peers and patients [1-3].

Twitter has significantly gained traction among care professionals and researchers. whereas permitting netizens to freely browse public messages up to a hundred and forty characters (“tweets”),solely registered users (“tweeters”) will write them, mention different users (by victimisation the image @ followed by the username) and mark keywords or topics in a very tweet victimisation hashtags (by adding the image # before the chosen word).

As care professionals’ discussions move onto social media, citations of the literature on Twitter (“tweetations”) associate degreed quotes of an argument or passage (nano-publications) are getting progressively common. With ample health-related tweets per day, the avalanche of information probably suffocates care professionals’ ability to faucet into the training resources and collaboration opportunities provided by such digital conversations.

Traditional publications have varied ways that calculate the influence and reach of medical literature. Such a ranking, or impact issue, proves important by quantifying and comparison a journal’s aggressiveness and importance to the medical profession. Yet, as medical and scientific publication moves to the net world, ancient metrics fail to understand the complete image - missing communication on social media, like Twitter. Social media-based metrics, conjointly termed “altmetrics”, produce new ways in which to assess such communication. Up heretofore there's no homologous ranking to determine the standard or worth of the net conversations.

We aim to make a reach index for care hashtags; such index ought to be filterable by topic of interest; from it we tend to aim to derive the individual impact of participants on those hashtags. Secondarily, the dynamics of the chosen care hash tag communities’ square measure to be examined and therefore the themes addressed in tweets to be explored. So as to attain these aims, we tend to perform a symbol of idea study on elite hashtags at intervals the context of medical aid and family practice [4].

Materials and ways

Hash tag Regulating

Hashtags were collated employing a democratic approach incorporating the researchers and Twitter users. Such hashtags turned around medical aid and family practice, in accordance to the researchers’ background. We tend to excluded those that on 03/15/2015 weren't a part of the care Hashtag Project the most important publically on the market information of care hashtags. The information is maintained by Symplur, a care social media analytics company, whereas care stakeholders will contribute with hashtags thereto. For this study, hashtags for conferences were outlined as fugacious and excluded. Afterwards, Symplur. com was accustomed notice every hash tag’s total range of impressions for the immediate past ninety days (12/16/2014 12:00 AM UTC-7 to 03/15/2015 12:00 AM UTC-7). Total impressions square measure calculated by multiplying the amount of tweets per participant by the followers count for that participant, and summing these numbers across all participants throughout the amount beneath analysis [5].

The 5 hashtags with the very best total range of impressions were then elite for indexation: #PrimaryCare, #MakeHealthPrimary, #FMRevolution, #FMChangeMakers, #1care. A sixth hash tag, #1carejc, was conjointly indexed because it derived from one in all the highest 5, though it controls a lower range of impressions.

Hashtag analysis

Each hash tag was retrospectively characterised for the half-moon of 2014 (Q414) and half-moon of 2015 (Q115), victimisation Symplur Signals. The studied variables were: (a) range of participants, (b) user locations, (c) user languages, (d) impressions and (e) tweets. Information was severally abstracted by 2 researchers.

The theoretical framework for the choice of those variables (and later combining them into a meaning composite indicator, HGR) was supported a fitness-for-purpose principle with the involvement of specialists and stakeholders World Health Organization have participated in a very specially run tweet chat.

Hashtag international Reach (HGR)

HGR was calculated victimisation the additive aggregation of weighted and normalized indicator variables.

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The distance to a reference hash tag was used because the standardisation technique. for every indicator variable, the reference was established because the leading, best performing arts hash tag and therefore the relative position of the hashtags were measured vis-à-vis the reference. Hence, for a given indicator variable, the reference hash tag includes a worth of one, whereas different hashtags square measure given proportion points far away from the reference, betting on their distance from the leader; standardized indicator variables that square measure nearer to one indicate hashtags with the very best reach [6].

The 5 indicator variables got equal weight and therefore the index computed as: HGR = Σ zero.20 Ii, wherever “i” represents the index of summation and indexed variable “I” represents every indicator term within the series; “i” starts out up to “1” and is incremented by “1” for every sequent indicator variable, stopping once “i” equals “5”. Equal weight was chosen with relevancy the theoretical framework, once democratic ways that incorporated the team of researchers in such weight negotiations, as antecedent delineated. Hashtags were then graded in keeping with HGR [7].


Our study is that the initial to explain the utilization of altmetrics to get a rank of hashtags. we tend to found that associate degree index of Hashtag international Reach can be delivered and accustomed assess the leading hashtags. By filtering for keywords, we've got incontestable the Index holds its consistency at term-specific and theme-specific levels. Moreover, consistent trends at the individual level can be seen, creating it attainable to develop the IGR. The analysis was replicated over time, for 2 consecutive quarters of year. we've got conjointly shown that HGR, topic-specific HGR, IGR and IGR per tweet is also accustomed study and describe the dynamics of activities in a very care hash tag community likewise as found 10 specific themes of interest within the conversations control victimisation the indexed hashtags.

Our measures square measure a primary step to map and measure relevant care conversations on social media by communities (hashtags), contributors and topics. on condition that the results of this proof-of-concept study counsel that the projected ways will add the “real world” conditions they were designed to work beneath, a complete study is currently necessary to assess the responsibility of the projected idea, specifically against different ways. so as to guide the look of such giant scale study, some modifications to boost feasibleness square measure mentioned during this section. to boot, as there's no gold normal to live the impact of such care conversations on social media, in future studies our measures ought to be evaluated against stakeholders’ connectedness scores likewise as against ancient metrics (e.g., impact factors in a very sub-analysis for journals; University/Faculty/Department rankings in a very sub-analysis for institutions), in accordance with antecedent revealed ways. Correlations ought to be measured between such scores/metrics and HGR/IGR likewise as its components. Temporal stability ought to be determined by determinant correlations across time, within the short and long run. any optimisation ought to be achieved by testing changed versions of our formulas and adjusting the formulas to the simplest correlations.


This study proves the idea feasibleness of making associate degree index of care hashtags supported altmetrics around international reach likewise as victimisation it to explain the dynamics of the activities in care hashtag communities. Studies square measure required to substantiate the analytical soundness of the composite indicators.

Conflicts of Interest

All authors declare no competitory interests: no support from any organization for the submitted work, no money relationships with associate degree organizations that may have an interest within the submitted work, no different relationships or activities that would seem to own influenced the submitted work.


We convey the multiple contributors throughout the tweet chat group action that became the cornerstone for our scientific research. We tend to conjointly convey Harris Lygidakis for his contributions to writing the study protocol and drafting the Thematic Analysis protocol. Finally, we might prefer to convey Anne-Marie professional dancer, Viviana Martinez-Bianchi, Canan Tuz and Seda Coskun for his or her input throughout the event of the study protocol.


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