Discovering Flow of Sentiment and Transient Behavior of Online Social Crowd: An Analysis Through Social Insects

Social media is growing at substantially faster rates, with millions of people across the globe generating, sharing and referring content on a scale apparently impossible a few years back. This has cumulated in huge participation with plenty of updates, opinions, news, blogs, comments and product reviews being constantly posted and churned in social Websites such as Facebook, Digg and Twitter to name a few. Even the events that are offline fetch the attention of social crowds, and considerably, their rapid sharing of views could signify the sentiment and emotional state of crowds at that particular instance. In the recent past, social media during terrorist strikes or natural disasters or in panic situations exhibits a tremendous impact in propagating messages among different communities and people. But the crowd participation in these interactions is grouped on the fly, and once the events fade out, they slowly disappear from the social media. We continuously iterate the challenges of identifying the behavioral pattern of the so-called transient crowd and their dispersion or convergence of sentiment and broadly answer how that could tell upon the offline events as well. While modeling the dynamics of such crowds, relevant clustering techniques have been consulted, although any method alone was not found compatible with the social media setup. The continuous cognitive pattern like a homophilic or curious and intuitive crowd with vector attributes on such social interaction motivates to incorporate an ant’s or swarm’s colonial behavior. Ants and swarms demonstrate well-defined chemical communication signals known as pheromones to segregate and distinguish specific communication patterns from cells of high concentration to those of low concentration. Hence, the positive and negative sentiment of transient crowds could be modeled, and the local influence can be measured on their posts through pheromone modeling and reinforcement of the shortest path of an ant or swarm’s life cycle. The primary objective of the chapter is to introduce a comparative smart methodology of ants and swarms as agent-based paradigms for investigating the community identification, namely, for Facebook and Twitter. The social media platforms are large enough to accommodate the ant and swarm graph for a pheromone model, tuning the time complexity of pheromone deposition and evaporation. Subsequently, the strength of association between transient users also could vary in terms of edge distribution and decay over stochastic measures of social events. We inculcate a couple of test cases fetched from Facebook on recent terror strikes of Mumbai, India, modeled using ants’ and swarms’ behavior. The results are encouraging and still in process. Empirically, the flow of sentiment and the corresponding dispersion of the crowd effect should infer or ignore a particular event, will leave a socio-computational benchmark for the mentioned proposition and will assist the ant alive in the system to reciprocate.