Abstract
A growing number of people are exchanging their opinions in online social networks and influencing one another. Thus, companies should observe opinion formation concerning their products in order to identify risks at an early stage. By doing so counteractive measures can be initiated by marketing managers. A neuro fuzzy system detects critical situations in the process of opinion formation and issues warnings for the marketing managers. The system learns rules for identifying critical situations on the basis of the opinions of the network members, the influence of the opinion leaders and the structure of the network. The opinions and characteristics of the network are identified by text mining techniques and social network analysis. Simulations based on swarm intelligence are used to derive recommendations which help the marketing managers influencing the right opinion leaders to prevent the negative opinions from spreading. The approach is illustrated by an exemplary application.
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Kaiser, C., Schlick, S., Bodendorf, F. (2013). Early Warning and Decision Support in Critical Situations of Opinion Formation within Online Social Networks. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2010. Communications in Computer and Information Science, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29764-9_7
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DOI: https://doi.org/10.1007/978-3-642-29764-9_7
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