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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Opinion mining and social influence on social media |
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| Institution | University of Zurich |
| Faculty | Faculty of Economics, Business Administration and Information Technology |
| Number of Pages | 29 |
| Date | 2016 |
| Abstract Text | Understanding how people influence or are influenced by their peers can help us understand the flow of market trends, product adoption and diffusion processes. Recent research has shown that social networks can be leveraged to accelerate behavior change, improve organizational efficiency, enhance social change and improve dissemination and diffusion of innovation. The objective of this thesis is threefold. In the first place the student will search the literature for methods to identify opinion leaders in a wide variety of contexts. In the second step he will analyze a dataset collected from a social media platform and identify the opinion leaders. To this end he will first perform a text mining analysis of the data. Based on the results, he will select a subset of the data and manually identify the opinion leaders. In the last step he will evaluate the accuracy of existing methods by comparing the results with the manual identification. He will conclude with a recommendation of the best algorithms together with an overview of the challenges raised by using such methods to analyze social media data. After finishing the thesis the student will have a good knowledge of social networks and will be familiar with the use of R for scientific research. |
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