Social Media Best Practices

6. MEASURING IMPACT IN SOME

6.1. SoMe analytics

SoMe analytics is the practice of gathering and analysing data from SoMe platforms (Figure 2) [140]. These analytics provide data on how effective information is being disseminated to our target audience. These data are often quantitative and allow us to measure and monitor the impact of SoMe activities. This chapter discusses SoMe metrics and social network analysis.

Figure 2: Types and implications of SoMe metrics

6.1.1. SoMe metrics

SoMe metrics are quantitative measures used to assess and monitor the performance and progress of SoMe activities [140]. These metrics are often predefined for each SoMe platform, and the exact definitions may differ across different SoMe platforms [140]. The general principles, however, are similar.

‘Impressions’ are the number of times a post is on screen for the first time. A post’s ‘reach’ is measured as the number of people who see a post at least once; reach does not change when the same post is view by the same person multiple times. ‘Engagement’ is the total number of interactions that people had with a post; the same person can therefore have multiple engagements with the same post. Engagement activity includes (but is not restricted to) liking, sharing and commenting on a post, viewing a photo or video, or clicking on a link.

Each of the SoMe metrics serves a different purpose and its value depends on what the user wishes to achieve. ‘Impressions’ are important when the user wishes to increase awareness of a particular subject especially to new users [140]. ‘Reach’ is important when the user wishes to deliver new information to as many people as possible within an existing audience [140]. ‘Engagement’ is important when the user wants an interested audience to take some form of action after reading a post [140]. Although no single metric has absolute superiority over the others, ‘engagement’ is generally regarded as the most important metric as it indicates the actual receipt of information and translation into action.

Newer metrices include the Altmetric Attention Score, which uses an algorithm to weigh mentions of an article across various SoMe platforms and news media [141-143]. The Altmetric Attention Score is derived from three main factors: volume, sources and authors [143]. Volume refers to how often the article is being mentioned. Sources refers to the media platforms on which the article is being shared. Authors refers to how often the author of each mention talks about scholarly articles, whether there is any bias towards a particular journal or publisher, and who the audience is. The Altmetric Attention Score is commonly used as a composite indicator of the media/SoMe presence of a particular article [141-143].

6.1.2. Social network analysis

On SoMe platforms, there are often key opinion leaders who wield significant influence in their fields. The ‘importance’ of a particular person can be determined based on their structural relationships and influence on other people within a social network. Such relationships and influence can be quantified using numerous centrality measures. The most common centrality measures include eigenvector centrality, betweenness centrality and closeness centrality [140].

Eigenvector centrality measures how connected an individual is to other very connected people [140]. This individual may have greater opportunities to trigger responses from key opinion leaders within the field. Betweenness centrality quantifies the number of times an individual acts as a bridge along the shortest path between two other people [140]. This individual serves as a middleman connecting different people within a social network. Closeness centrality measures the shortest distances between an individual with other people [140]. It reflects how close an individual is with other people within a social network. These centrality measures are important to identify key opinion leaders within a community. In order to disseminate urological information in a more effective manner, it is important to engage people with high centrality in a more proactive manner.

6.2. Implications of SoMe analytics

Over the past decade, SoMe is increasingly being recognised as an important aspect to be addressed in urological conferences [14, 87, 144, 145]. Conference-specific hashtags, consisting of the name and the year of the conference (e.g. #EAU23, which stands for the 2023 EAU Annual Congress), are often used. An individual can post any conference-related content alongside the conference-specific hashtags to facilitate viewing of relevant information and engagement among the various users. These hashtags also enable conference organisers to track online audience engagement across different years of conferences [14, 87, 144, 145]. Nolte et al. examined posts associated with new scientific studies presented at a national urology conference and demonstrated that research with more likes/retweets was more likely to be subsequently published (p=0.001); among published studies, a modest positive correlation was found between the number of likes/retweets and publication journal impact factor (r2=0.36) [146].

Many urology journals have utilised SoMe platforms to disseminate information about their publications [96]. The importance of SoMe engagement has been demonstrated in numerous ways. One study investigated the effect of SoMe platforms on the impact factor of paediatric urology journals. The study found that the presence of a X feed was statistically significant for a rise in impact factor over four years (p = 0.017) [147]. Another study that examined 230 papers from seven urology journals found that the number of citations was positively associated with the number of mentions on X [100]. Articles mentioned on X have two-fold more Scopus citations (p < 0.01), and 2.3-fold more Google Scholar Citations (p < 0.01) compared to articles with no X mentions [100]. Altmetric score was found to have significant correlations with citation counts and journal impact factors, although the correlations were only weakly positive [148]. Sathianathen et al. developed the #UroSoMe_Score using a stepwise regression model comprising various sources of online attention, and it was found to predict two-year citation counts for urological publications. The #UroSoMe_Score was also found to have a higher predictive ability than Altmetric score [99]. The Twitter Impact Factor, which is calculated based on the number of retweets per original relevant tweet, was found to have a positive but insignificant association with the journal impact factor (r = 0.64, p= 0.12) [101]. A strongly positive correlation was, however, identified between Twitter Impact Factor and Klout score (r = 0.84, p= 0.009) [101].

SoMe presence is also important for academic urologists and institutions. A study showed a steady increase in X engagement amongst urology programmes and academic physicians in North America [149]. Another study examined the association of academic rank and productivity with metrics of X utilisation among kidney cancer specialists [150]. It was found that associate professors had a higher number of followers than assistant professors and full professors. The most predictive variables for the number of followers were the number of tweets, H-index and percentage of medical tweets [150]. Ciprut et al. examined the X accounts of urology departments in the United States and identified a positive correlation between X metrics (number of followers, number of followings and number of tweets) and U.S. News and World Report reputation scores [93].