African Influencers: Twitter users segmentation
Mining social data from Twitter
Influencer marketing has matured as an industry, it has attracted support companies and apps to simplify the process for both brands and influencers. Organic influencer marketing can be a slow and tedious process, particularly when it comes to finding and wooing influencers who are not biased towards ethnic and religious issues to promote your company’s products or services.
The data is mined from Twitter, Atlantic council website, and entiate website. The methodology adopted here in measuring the influence of these brands is based on that proposed by Levy and Windhal (1985).
The key index for estimating an influencer’s influence proposed in the study are listed below;
popularity_score = #retweets + #likes
reach_score = #followers — #they follow
Table 1 below shows a sample of the data to estimate the metrics for Africa on Influencers in the African region
Table 2 is a cross-section of the score of selected influencers based on the parameters listed above.
The reach of influencers can be significantly affected if they contribute to the same issue Government are interested in or Government official are interested in trends set by this influencer. This relationship cannot be fully understood with this study. Figure 1 shows the common trend that has seen contributions from both Influencers and Government officials
From the data gathered, it will be highly profitable to patronize the COVID 19 thread, this has more influence and contributor as shown in figure 1
The code for this exercise can be found in this repository
Levy, M. R., & Windahl, S. (1985). The concept of audience activity. In K. E. Rosengren, L. A. Wenner, & P. Palmgreen (Eds.), Media gratifications research: Current perspectives (pp. 109–122). Newbury Park, CA: Sage.