Conclusion and Future Research Directions
This study uses a mixed research methodology by combining the insights from social media analytics to model the spammers in Twitter using bio inspired computing. The proposed K-means integrated levy flight algorithm using Sinusoidal map for tuning the absorption coefficient produces the best results in terms of accuracy and a faster convergence rate. The proposed approach gives an accuracy of 97.98% by modeling 13 significant factors after a statistical t-test including emotion diversity, polarity diversity, hashtag frequency, unique words, user @ mentions, lexical diversity, tweet count, follower count, favorite count, friends count, added to lists, following rate and user reputation. In addition to the proposed integrated firefly approach, a Fuzzy C-Means approach is used to identify the overlap among the two spam and authentic fuzzy groups. However, when compared with the proposed approach, the convergence of the Fuzzy C-Means is slower than the proposed approach. The study thus effectively combines relevant factors from user, descriptive and semantic statistics to model the Twitter profiles for detecting social media spam. The proposed approach can prove to be beneficial when organizations seek to gauge the success rate of campaigns, for identifying potential influencers for promotion of content and viral marketing. Our study highlights that analytics driven approach in social media for analyzing spam needs to be developed based on multi-method research methodologies because of the nature of the user generated content as well as the volume of the instances of content creation per content creator
