23,210 research outputs found
Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events
Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains
Negative emotions boost users activity at BBC Forum
We present an empirical study of user activity in online BBC discussion
forums, measured by the number of posts written by individual debaters and the
average sentiment of these posts. Nearly 2.5 million posts from over 18
thousand users were investigated. Scale free distributions were observed for
activity in individual discussion threads as well as for overall activity. The
number of unique users in a thread normalized by the thread length decays with
thread length, suggesting that thread life is sustained by mutual discussions
rather than by independent comments. Automatic sentiment analysis shows that
most posts contain negative emotions and the most active users in individual
threads express predominantly negative sentiments. It follows that the average
emotion of longer threads is more negative and that threads can be sustained by
negative comments. An agent based computer simulation model has been used to
reproduce several essential characteristics of the analyzed system. The model
stresses the role of discussions between users, especially emotionally laden
quarrels between supporters of opposite opinions, and represents many observed
statistics of the forum.Comment: 29 pages, 6 figure
Emotional persistence in online chatting communities
How do users behave in online chatrooms, where they instantaneously read and
write posts? We analyzed about 2.5 million posts covering various topics in
Internet relay channels, and found that user activity patterns follow known
power-law and stretched exponential distributions, indicating that online chat
activity is not different from other forms of communication. Analysing the
emotional expressions (positive, negative, neutral) of users, we revealed a
remarkable persistence both for individual users and channels. I.e. despite
their anonymity, users tend to follow social norms in repeated interactions in
online chats, which results in a specific emotional "tone" of the channels. We
provide an agent-based model of emotional interaction, which recovers
qualitatively both the activity patterns in chatrooms and the emotional
persistence of users and channels. While our assumptions about agent's
emotional expressions are rooted in psychology, the model allows to test
different hypothesis regarding their emotional impact in online communication.Comment: 34 pages, 4 main and 12 supplementary figure
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
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