TY  - THES
A1  - Thesing, Tobias
T1  - Time series influences in political communication
N2  - Current political issues are often reflected in social media discussions, gathering politicians and voters on common platforms. As these can affect the public perception of politics, the inner dynamics and backgrounds of such debates are of great scientific interest. This thesis takes user generated messages from an up-to-date dataset of considerable relevance as Time Series, and applies a topic-based analysis of inspiration and agenda setting to it. The Institute for Web Science and Technologies of the University Koblenz-Landau has collected Twitter data generated beforehand by candidates of the European Parliament Election 2019. This work processes and analyzes the dataset for various properties, while focusing on the influence of politicians and media on online debates. An algorithm to cluster tweets into topical threads is introduced. Subsequently, Sequential Association Rules are mined, yielding wide array of potential influence relations between both actors and topics. The elaborated methodology can be configured with different parameters and is extensible in functionality and scope of application.
KW  - Political Communication
KW  - 2019 European Parliament Election
KW  - Association Rules
Y1  - 2019
UR  - https://kola.opus.hbz-nrw.de/frontdoor/index/index/docId/2002
UR  - https://nbn-resolving.org/urn:nbn:de:kola-20022
ER  -