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Distance Decay Effect and Spatial Interaction during the COVID-19 Pandemic

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dc.contributor.authorWolz, Nicolas
dc.contributor.authorXu, Manning
dc.contributor.authorWang, Tiantian
dc.contributor.editorGesellschaft für Informatik
dc.date.accessioned2021-12-15T10:17:09Z
dc.date.available2021-12-15T10:17:09Z
dc.date.issued2021
dc.description.abstractIn computational communication science, social network data can be used to analyze trends in the communication behavior of people. For this work, a data set containing english Tweets was provided by the University of Technology Ilmenau, which was collected during the begining of the COVID-19 pandemic. The goal was to find hidden patterns within the data to show if and how the pandemic influenced our communication. This paper looks at the Distance Decay Effect, which says that near things are more related to each other than distant things, and therefore communication should get more sparse the greater the distance between users. Modeling the data with a Gravity Model shows that this relationship is true for the data provided, therefore reproducing earlier research on this topic. We were not successful in finding any clear trend showing that the strengh of the Distance Decay Effect changed over the course of the first weeks of the pandamic.en
dc.identifier.isbn978-3-88579-751-7
dc.identifier.pissn1614-3213
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37777
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofSKILL 2021
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Seminars, Volume S-17
dc.subjectDistance Decay Effect
dc.subjectGravity Model
dc.subjectCOVID-19
dc.subjectTwitter
dc.titleDistance Decay Effect and Spatial Interaction during the COVID-19 Pandemicen
gi.citation.endPage196
gi.citation.startPage185
gi.conference.date28. September und 01. Oktober 2021
gi.conference.locationBerlin
gi.conference.sessiontitleSKILL 2021

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