Overview of Digital Influence, Impacts and Measurement
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Many concepts and terminologies are being used to explain the types and levels of interaction between social media users. One of such is the term ‘digital influence’. In this essay, I will be discussing digital influence; what it is, its attributes and how it can be measured. I will also be comparing the various metrics used to measure digital influence and will round up by highlighting how digital influence metrics differ from one aspect of society to another. The social media platform used for this study is Twitter, the measuring tool used was Tweet Binder.
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What is Digital Influence?
According to Techopedia, digital influence refers to the ability to produce an effect, alter behaviours and perspectives and cause outcomes which are measurable online. Digital influence is a phenomenon associated with the use of social networks and it rides on the assumption that the average social media user is subjected to a barrage of noise and content every day. This makes it possible to replace individual judgement with group judgement or at least judgment that's assisted by others online.
Deloitte Digital (2015) defines digital influence in the retail space as the impact of digital in the shopping journey of consumers on in-store sales. This impact was found to be growing at an increasing rate such that it will soon be possible for businesses to be connected to 100 percent of their consumers at the same time. The study also showed that within different categories in the retail, the effect of digital influence on the consumer varies from one category to another.
Figure 1: Digital influence by retail category
Source: Deloitte Digital (2015)
In a study of online influence, Solis and weber (2012) posited that word of mouth is gaining grounds as a tangible means of reaching consumers. Companies have come to realise that the internet is a big sphere for engaging with consumers and in view of this, they are leaning a lot more towards digital or online marketing more than before.
In some other areas like education, computing, business; digital influence is seen from the lens of positioning on the ranking systems associated with that industry. For graduate schools and colleges in the US for example, the annual rankings by U.S. News and World Report are used by prospective students, parents, prospective employers and the schools alike for various purposes. Prospective students and parents use the rankings to identify which schools to attend based on how the schools not only fit their selection criteria but also based on other metrics such as alumni network, consistency in position in the rank. The schools on their own part, take the rankings seriously and many schools use outcomes of the annual rankings to adjust their management practices. Critics of the school rankings opine that using the same set of metrics to assess schools that have different specialities tilts the scale against schools who do not have elitist aspirations. (Espeland and Sauder, 2009).
In the computing industry, procedural rankings are done to assess hardware and software capabilities as new versions or systems are introduced by the industry players. The outcome is a ranking based on findings of the reviewers. Some publications that publish regular reviews include American Statistician, eWeek, Info World and PC Magazine. A key thing to note about procedural reviews is the need for consistency in the metrics being used to evaluate similar products. Using this approach does a lot for credibility and acceptance of the results. It also ensures that product owners see that a level playing field was used for the assessment. PC Magazine has built its reputation as a very successful computing magazine and this can be attributed to their being able to do extensive procedural reviews (Blank 2007).
Fombrun and Shanley (1990) pointed out the need for the public to further investigate the methods and principles ranking systems use to arrive at firms’ reputations. Their study opined that the judgement of the public creates reputations for firms based on the type of information the firms are able to push out into the public domain about themselves. Thus, the respondents to a ranking survey for example may base their responses on information which isn’t related to the survey.
In activism, influence could be seen differently. An example case is the BBOG movement popularly known as #bringbackourgirls which started in 2014 after about 276 girls were kidnapped from their secondary school in Chibok, a small town in North Eastern Nigeria. The hashtag started when people rose up world over to demand that the girls be returned and to try and force government to act in rescuing the girls (The Guardian, UK 2018). To date The BBOG movement still relies heavily and still uses social media especially Twitter and Facebook for engagement, communication and to drive its social message and create awareness of its programs. This is an example of various groups coming together to use digital influence to fight for a cause which achieved positive results.
How is Digital Influence Measured?
Digital influence is measured for a variety of reasons. For example, the measurement could be to assess my influence over other social media users or the influence of other social media users over me. It could also be to ascertain how far or how well a narrative or campaign is being driven. It could also be to gauge public opinion about trending issues. Indicators used to assess influence include followership, likes, retweets, mentions, reach etc.
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Followership refers to the number of users following a social media account. Nowadays, a large number of followers confers on a user the ‘influencer’ status; which means that such a user is expected to be able to influence the behaviour or choices of his followers to a certain degree. This may not totally be true. While user count can be seen as a good indicator of public interest, it cannot be used as a single yard stick for measuring influence. this is because users follow other users for various reasons, ranging from popularity or simply to just stalk them. It could be to associate with them, or because they like the content of the messages these influencers post online, or even believing in the same causes. In some other cases, when a user trends for good or bad behaviour, the user count is found to increase as a result.
Studies have shown that follower size, used singly is not a true measure of influence. The fact that a user has many followers does not mean that those followers can be controlled to do what the influencer says or does. It’s better to measure in addition to the number of followers, the number of posts, and the number of interactions an influencer produces, and track how these indicators change over a period.
There is no commonly accepted set of indicators for measuring digital influence, rather many of the existing tools analyse a stream of users’ activities from the different social media sites to arrive at the required measurements. This makes measuring influence in a generic way or on a particular social media network quite complicated.
It was found in a study by Centola (2010) that there was no direct effect of population size i.e. followership on behavioural diffusion. The results showed that unlike volume of users, network structure had a more significant impact on behavioural diffusion. User networks with higher degree of clustering were seen to be more effective for spreading behaviour.
Another reason for measuring influence may be to confirm how closely related users in a network are. In another study, Campagnolo (2014) used some simple monitoring tools to analyse a remote event, in this case a UK Analysts Forum (AR). He used tools like TweetBinder, Gephi and NodeXL. From his study, he opined that the UK analyst scene was more tightly connected than other AR communities in other countries such as US and Europe. He observed that many of those who tweeted about the AR Forum knew about 2 people who were physically attending the event. Campagnolo pointed out that the good performance of a user organisation on one analyst ranking was independent of its performance on other ranking systems as it might not even make a top position on some rankings. He also concluded that since the UK AR community was more connected, it would be able to do better at controlling its heterogeneity.
From a political perspective, there are strong insinuations about the use of Social media to propagate misconceptions while political campaigns are on. Popular social media platforms such as Facebook and Twitter are used to calculate inaccurate or biased opinions about candidates. Studying presidential elections in the US, Garrett, (2019) concluded that the no evidence of social media influence on voters’ belief accuracy was found. The group of users on Facebook however showed a unique trait and the results confirmed that Social Media can indeed influence a voter’s endorsement of inaccurate information during elections though the effects are relatively small.
In conclusion, there is a need to harmonise how digital influence is measured, so as to standardise how the outcomes and key indicators for initiatives or studies can be tracked effectively. The existence of many tools or approaches isn’t a bad thing in itself but the opacity of the various criteria used by these tools in arriving at the measurements needs to be curbed.
- Fombrun, Charles; Shanley, Mark (1990) What's in a Name? Reputation Building and Corporate Strategy
- The Academy of Management Journal, 1 June 1990, Vol.33(2), pp.233-258
- Deloitte Digital, 2015. NAVIGATING THE NEW DIGITAL DIVIDE: Capitalizing on digital influence in retail.
- Espeland, W. N., & Sauder, M. (2007). Rankings and Reactivity: How Public Measures Recreate Social Worlds1. American Journal of Sociology, 113(1), 1-40.
- Espeland, W., Sauder, M. (2009). Rating the Rankers. Contexts, Vol. 8, No. 2, pp. 16–21.
- Blank, G. (2007). Critics, ratings, and society the sociology of reviews. (chapter 3 and 4)
- Fombrun, C., Shanley, M. (1990) What's in a Name? Reputation Building and Corporate Strategy, The Academy of Management Journal, 33 (2), pp. 233-258.
- Damon Centola; 2010. The Spread of Behavior in an Online Social Network Experiment
- Source: Science, New Series, Vol. 329, No. 5996 (3 September 2010), pp. 1194-1197
- Published by: American Association for the Advancement of Science
- https://www.briansolis.com/ [accessed 09 December, 2019]
- Brian Sooy (2012) Social Media Metrics: Engagement and influence. https://www.aespire.com/ [accessed 09 December 2019]
- Dustin Hawley (2019) HOW TO MEASURE SOCIAL MEDIA INFLUENCE https://www.viralnation.com/ [accessed 10 December 2019]
- Bring-back-our-girls-documentary-stolen-daughters-kidnapped-boko-haram; https://www.theguardian.com [accessed 15 November 2019]
how-bring-back-our-girls-went-from-hashtag-to-social-movement-while-rejecting-funding-from-donors https://oxfamblogs.org/fp2p/ / [accessed 15 November 2019]
- https://www.newsweek.com/chibok-girls-boko-haram [accessed 15 November 2019] https://en.wikipedia.org/wiki/Chibok_schoolgirls_kidnapping [accessed 15 November 2019]
- R. Kelly Garrett, 2019, Social media’s contribution to political misperceptions in U.S. Presidential elections [accessed 10 December 2019]
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