Monday, in London, was nerve wracking as fans waited anxiously for the start of the Premier League match. This is the ultimate division of English football. Manchester and Arsenal’s Twitter Profiles were very active because of the smart, creative editorial plan made for commenting in real time. By Monday, every club had chosen an official hashtag for the match. The official hashtag for Manchester was #arsenalvcity while the official hashtag for Arsenal was #afcvmcfc. In a way, there were two matches being played: one on the field and one on Twitter!
The metrics are easily discernible: Arsenal won the match 2-1 and who wants to talk after a defeat? Arsenal also prevailed over Manchester City on Twitter as tweet activity was 97 vs. 77 and in terms of received re-tweets: 58.197 vs 8.388
The number of interactions was dramatically high, especially in terms of tweet-re-tweets. For this event we seamlessly mix two complementary approaches for analysis to optimizet the collateral information available. We monitored the number of interactions around the hahstags #arsenalvcity and #afcvmcfc, than we profiled two influencers, to understand if they had other interests besides soccer.
This chart shows the top active users on Twitter
This chart shows the top 5 Influencers
This chart shows the Hashtag Cloud
ROIALTY profiled influencers that represented the main hub in the conversation flow: They achieved high numbers of interactions indegree. ROIALTY can more fully know these users by understanding their interests: are these influencers more than just soccer lovers? Do their interactions on Twitter show interests in other sports or in other sectors?
This Chart shows the social graph in which are underlined the influencers revealed with ROIALTY Platform. It is useful to mix the Community Detection activity with ROIALTY Platform to help understand if, among centrality metrics and ROIALTY system, there are collateral aspects useful for brands and in particular, for event organization to improve performance and increase social engagement.
How does SNA differ from ROIalty’s rankings?
The most interesting difference between the SNA approach and ROIalty’s approach is that SNA identifies several users that appear as influential in the network but have a much smaller number of followers than celebrities like Piers Morgan, who has over 4 million followers, and Matt Lucas, who has almost one million. @CarlBovis_AFC, for example, is identified as highly influential, despite having only 148,000 followers. @SeanMacaulay, also identified as influential, has just 1,100 followers.
While accounts such as @piersmorgan are influential whether you look at their number of followers or their place in the network, users such as @SeanMacaulay are only seen as influential if you look at the network structure.
With SNA, we can also understand who users are talking to. For example, we can see two distinct clusters of users around the two competing teams. @piersmorgan, for example, is clearly associated with the Arsenal cluster. If a brand wanted to engage with Manchester fans, then, he might be a poor choice.
Influencer Marketing is a thing that concerns the interests of the influencer and not solely the number of interactions. Obviously, the number of interactions is what makes these people immediately important, and in this direction the quantitative aspect is crucial to define which people have the most interactions indegree and create engagement. Our study is useful before an event, when a brand wants to choose the right influencers and is based on people reached but also interests positioning. Social Network Analysis is useful and collateral to show a complete analytical view of the network especially concerning the relations and ties between users.
(Co-Author – Daniel Carter)