Social Network Influence: Making the Case for Semantic Analysis

Influence on the social network platform has become an interesting research area. A typical social network influencer is known by the amount of reactions that he/she attracts based on the posts made. The amount of reactions received is usually a reflection of the overall visibility of the influencer on the social network arena. This visibility has attracted commercial benefits to such influencers through word of mouth marketing, political endorsements as well as ambassadorial appointments. However, existing methods in literature that are used to scientifically quantify the amount of social influence that can be attributed to an influencer, tend to lump the amount of social influence in a single basket labelled either as positive or negative influence. The effect is a binary classification of influencers as either negative or positive. In this article, we make the case that this is not necessarily accurate. The reason being that in a collection of comments that a post from an influencer attracts, it it seldom that all the comments would be absolutely positive or negative. There is a fuzzy space defined by comments that do not necessarily belong to either of these categories. In this work, we have used data harvested from Facebook comments and grouped into four different categories - those in full support of the opinion expressed, those in full opposition of the opinion, those that somewhat support and those that somewhat oppose the opinion expressed. Results have shown that the fuzzy space created by the comments in between the full support and full opposition negates the assumption that an influencer can be a positive or negative influencer just because the number of positive or negative comments are the majority.