ITU

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

Standard

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

Harvard

Hanteer, O & Rossi, L 2019, An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks. in Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media. Frontiers. https://doi.org/10.3389/fdata.2019.00009

APA

Hanteer, O., & Rossi, L. (2019). An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks. In Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media Frontiers. https://doi.org/10.3389/fdata.2019.00009

Vancouver

Hanteer O, Rossi L. An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks. In Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media. Frontiers. 2019 https://doi.org/10.3389/fdata.2019.00009

Author

Hanteer, Obaida ; Rossi, Luca. / An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks. Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media. Frontiers, 2019.

Bibtex

@inproceedings{4ea232c859df4ed9a5af93292f5ce16c,
title = "An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks",
abstract = "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",
author = "Obaida Hanteer and Luca Rossi",
year = "2019",
month = jun,
day = "6",
doi = "10.3389/fdata.2019.00009",
language = "English",
booktitle = "Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media",
publisher = "Frontiers",

}

RIS

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