An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks

Obaida Hanteer; Luca Rossi

doi:10.3389/fdata.2019.00009

An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks

Obaida Hanteer
, Luca Rossi

Networks, Data, and Society

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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

Original language	English
Title of host publication	Frontiers in Big Data. Workshop Proceedings of the 13th International AAAI Conference on Web and Social Media
Publisher	Frontiers
Publication date	6 Jun 2019
DOIs	https://doi.org/10.3389/fdata.2019.00009
Publication status	Published - 6 Jun 2019

Keywords

Twitter interactions
Multiplex network
Community detection
Thematic analysis
Social media modeling

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10.3389/fdata.2019.00009

fdata-02-00009Final published version, 1.27 MB

VIRT-EU: Values and ethics in Innovation for Responsible Technology in EUrope
Shklovski, I. (PI), Rossi, L. (CoI), Douglas-Jones, R. (CoI), Fritsch, E. (CoI), Hanteer, O. (CoI), Nino Carreras, B. P. (CoI) & Memic, I. (CoI)
European Commission
01/01/2017 → 31/12/2019
Project: Research

Cite this

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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",

keywords = "Twitter interactions, Multiplex network, Community detection, Thematic analysis, Social media modeling, Twitter interactions, Multiplex network, Community detection, Thematic analysis, Social media modeling",

author = "Obaida Hanteer and Luca Rossi",

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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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KW - Twitter interactions

KW - Multiplex network

KW - Community detection

KW - Thematic analysis

KW - Social media modeling

KW - Twitter interactions

KW - Multiplex network

KW - Community detection

KW - Thematic analysis

KW - Social media modeling

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 -

An Innovative Way to Model Twitter Topic-Driven Interactions Using Multiplex Networks

Abstract

Keywords

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VIRT-EU: Values and ethics in Innovation for Responsible Technology in EUrope

Cite this