Quantitative analysis of empirical data from online social networks revealsgroup dynamics in which emotions are involved (\v{S}uvakov et al). Fullunderstanding of the underlying mechanisms, however, remains a challengingtask. Using agent-based computer simulations, in this paper we study dynamicsof emotional communications in online social networks. The rules that guide howthe agents interact are motivated, and the realistic network structure and someimportant parameters are inferred from the empirical dataset of\texttt{MySpace} social network. Agent's emotional state is characterized bytwo variables representing psychological arousal---reactivity to stimuli, andvalence---attractiveness or aversiveness, by which common emotions can bedefined. Agent's action is triggered by increased arousal. High-resolutiondynamics is implemented where each message carrying agent's emotion along thenetwork link is identified and its effect on the recipient agent is consideredas continuously aging in time. Our results demonstrate that (i) aggregatedgroup behaviors may arise from individual emotional actions of agents; (ii)collective states characterized by temporal correlations and dominant positiveemotions emerge, similar to the empirical system; (iii) nature of the drivingsignal---rate of user's stepping into online world, has profound effects onbuilding the coherent behaviors, which are observed for users in online socialnetworks. Further, our simulations suggest that spreading patterns differ forthe emotions, e.g., "enthusiastic" and "ashamed", which have entirely differentemotional content. {\bf {All data used in this study are fully anonymized.}}
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