Quantitative Analysis of Bloggers Collective Behavior Powered by Emotions

Large-scale data resulting from users online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study emergence of the emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite network of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion-classifier developed for this type of texts. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore robustness of these critical states, we design a network automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states

Sale the seven Cs: Teaching/training aid for the (e-)retail mix

The ‘4Ps’ of the marketing mix have long been popular with students, tutors, trainers and practitioners as a learning and teaching aid. The purpose of this paper is to present an equivalent tool for retail and e-retail: ‘Sale the 7Cs’. The approach is by reference to other authors’ versions of the marketing, retail and e-retail mixes, distilled into a simplified framework: C1 Convenience; C2 Customer value and benefit; C3 Cost to the customer; C4 Computing and category management; C5 Customer franchise; C6 Customer care and service; C7 Communication and customer relationships. This simplified mnemonic is new for (e-)retail. Mini case examples are used to illustrate the applicability. These have a practical value for trainers and educators as specimen answers to activity exercises. Retailers may find the convenient 7Cs structure useful when planning strategies and tactics

Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms

Online social networks are the perfect test bed to better understand large-scale human behavior in interacting contexts. Although they are broadly used and studied, little is known about how their terms of service and posting rules affect the way users interact and information spreads. Acknowledging the relation between network connectivity and functionality, we compare the robustness of two different online social platforms, Twitter and Gab, with respect to dismantling strategies based on the recursive censor of users characterized by social prominence (degree) or intensity of inflammatory content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab) character of the network is not a discriminating factor for intervention effectiveness. We find, however, that more complex strategies based upon the combination of topological and content features may be effective for network dismantling. Our results provide useful indications to design better strategies for countervailing the production and dissemination of anti-social content in online social platforms