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Social media and Social Network Analysis (SNA)acquired a huge popularity and represent one of the mostimportant social and computer science phenomena of recentyears. One of the most studied problems in this research area isinfluence and information propagation. The aim of this paper isto analyze the information diffusion process and predict theinfluence (represented by the rate of infected nodes at the end ofthe diffusion process) of an initial set of nodes in two networks:Flickr user’s contacts and YouTube videos users commentingthese videos. These networks are dissimilar in their structure(size, type, diameter, density, components), and the type of therelationships (explicit relationship represented by the contactslinks, and implicit relationship created by commenting onvideos), they are extracted using NodeXL tool. Three modelsare used for modeling the dissemination process: LinearThreshold Model (LTM), Independent Cascade Model (ICM)and an extension of this last called Weighted Cascade Model(WCM). Networks metrics and visualization were manipulatedby NodeXL as well. Experiments results show that the structureof the network affect the diffusion process directly. Unlikeresults given in the blog world networks, the information canspread farther through explicit connections than throughimplicit relations

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Social media users can easily be offended or hurt on those platforms, which leads to discomfort and health issues such as stress and anxiety. Forgiveness plays an important role to maintain healthy online relationships, which is the central constituent of social dynamics, from cooperation to social cohesion. While most prior studies have focused on analyzing forgiveness factors in offline settings using statistical methods, this study offers a new perspective using a two-staged approach whereby a research model was tested using structural equation modeling (SEM), and then the results were used as inputs for artificial neural network (ANN) and fuzzy logic (FL) models. An agent-based simulation was then performed to shed light on a possible use of the implemented models. Combining ANN and FL provided more accurate prediction results. In addition, simulation experiments call attention to the potential benefits of forgiveness in maintaining connectedness in a social network. The main purpose of this investigation was to evaluate the applicability of soft computing techniques on forgiveness prediction. Instead of relying on data mining techniques, we looked into questions that can improve our understanding of how society works in a digital age. In addition, this study provides an interesting example of a different and insightful way of doing computational social science that is useful to both researchers and practitioners.

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