An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks
Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
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An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks. / Hanteer, Obaida; Rossi, Luca.
Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media. Frontiers, 2019.Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review
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TY - GEN
T1 - An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks
AU - Hanteer, Obaida
AU - Rossi, Luca
PY - 2019/6/6
Y1 - 2019/6/6
N2 - We propose a way to model topic-based implicit interactions among Twitter users. Our model relies on grouping Twitter hashtags, in a given context, into themes/topics and then using the multiplex network model to construct a thematic multiplex where each layer corresponds to a topic/theme, and users within a layer are connected if and only if they used the same hashtag. We show, by testing our model on a real-world Twitter dataset, that applying multiplex community detection on the thematic multiplex can reveal new types of communities that were not observed before using the traditional ways of modeling Twitter interactions
AB - We propose a way to model topic-based implicit interactions among Twitter users. Our model relies on grouping Twitter hashtags, in a given context, into themes/topics and then using the multiplex network model to construct a thematic multiplex where each layer corresponds to a topic/theme, and users within a layer are connected if and only if they used the same hashtag. We show, by testing our model on a real-world Twitter dataset, that applying multiplex community detection on the thematic multiplex can reveal new types of communities that were not observed before using the traditional ways of modeling Twitter interactions
U2 - 10.3389/fdata.2019.00009
DO - 10.3389/fdata.2019.00009
M3 - Article in proceedings
BT - Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media
PB - Frontiers
ER -
ID: 84707808