Twitter Data Import Models
https://doi.org/10.25205/1818-7900-2021-19-2-76-91
Abstract
In this paper, the authors describe an algorithm for importing data from the social network Twitter and building weighted social graphs. To import data, the given posts are taken as a basis, users who have had any of the recorded interactions with them are downloaded. Further, the algorithm focuses on the given configuration and uses it to calculate the weights on the edges of the resulting graph. The configuration takes into account the type of user interaction with each other. The authors introduce the concept of (F, L, C, R)-model of information interaction.
The authors describe the developed algorithm and implemented software for constructing weighted graphs. The paper shows the application of the algorithm and three models on the example of both a single post and a series of posts.
Keywords
social network analysis,
import of data from social networks,
information interaction models,
community detection
Supporting agencies: The study was funded by RFBR according to the research project no. 19-07-00806
About the Authors
V. A. Popov
National Research University Higher School of Economics
Russian Federation
Russian Federation
Vladimir A. Popov - Master's student, National Research University Higher school of Economics.
Moscow.
A. A. Chepovskiy
National Research University Higher School of Economics
Russian Federation
Russian Federation
Alexander A. Chepovskiy - PhD (Mathematics), Associate Professor, National Research University Higher school of Economics.
Moscow.
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Review
For citations:
Popov V.A., Chepovskiy A.A. Twitter Data Import Models. Vestnik NSU. Series: Information Technologies. 2021;19(2):76-91. (In Russ.) https://doi.org/10.25205/1818-7900-2021-19-2-76-91
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