Identifying communities in social media with deep learning

Pedro Barros; Isadora Cardoso-Pereira; Keila Barbosa; Alejandro C. Frery; Héctor Allende-Cid; Ivan Martins; Heitor S. Ramos

doi:10.1007/978-3-319-91485-5_13

Identifying communities in social media with deep learning

Pedro Barros, Isadora Cardoso-Pereira, Keila Barbosa, Alejandro C. Frery, Héctor Allende-Cid, Ivan Martins, Heitor S. Ramos

SCHOOL OF COMPUTER ENGINEERING

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This work aims at analyzing twitter data to identify communities of Brazilian Senators. To do so, we collected data from 76 Brazilian Senators and used autoencoder and bi-gram to the content of tweets to find similar subjects and hence cluster the senators into groups. Thereafter, we applied an unsupervised sentiment analysis to identify the communities of senators that share similar sentiments about a selected number of relevant topics. We find that is able to create meaningful clusters of tweets of similar contents. We found 13 topics all of them relevant to the current Brazilian political scenario. The unsupervised sentiment analysis shows that, as a result of the complex political system (with multiple parties), many senators were identified as independent (19) and only one (out of 11) community can be classified as a community of senators that support the current government. All other detected communities are not relevant.

Original language	English
Title of host publication	Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings
Editors	Gabriele Meiselwitz
Publisher	Springer Verlag
Pages	171-182
Number of pages	12
ISBN (Print)	9783319914848
DOIs	https://doi.org/10.1007/978-3-319-91485-5_13
State	Published - 2018
Event	10th International Conference on Social Computing and Social Media, SCSM 2018 Held as Part of HCI International 2018 - Las Vegas, United States Duration: 15 Jul 2018 → 20 Jul 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10914 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Social Computing and Social Media, SCSM 2018 Held as Part of HCI International 2018
Country/Territory	United States
City	Las Vegas
Period	15/07/18 → 20/07/18

Keywords

Autoencoder
Community detection
Convolutional networks
Deep learning
Text classification

Access to Document

10.1007/978-3-319-91485-5_13

Cite this

Barros, P., Cardoso-Pereira, I., Barbosa, K., Frery, A. C., Allende-Cid, H., Martins, I., & Ramos, H. S. (2018). Identifying communities in social media with deep learning. In G. Meiselwitz (Ed.), Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings (pp. 171-182). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10914 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-91485-5_13

Barros, Pedro ; Cardoso-Pereira, Isadora ; Barbosa, Keila et al. / Identifying communities in social media with deep learning. Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings. editor / Gabriele Meiselwitz. Springer Verlag, 2018. pp. 171-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3f459cc663b6478fae24cfec098ca72f,

title = "Identifying communities in social media with deep learning",

abstract = "This work aims at analyzing twitter data to identify communities of Brazilian Senators. To do so, we collected data from 76 Brazilian Senators and used autoencoder and bi-gram to the content of tweets to find similar subjects and hence cluster the senators into groups. Thereafter, we applied an unsupervised sentiment analysis to identify the communities of senators that share similar sentiments about a selected number of relevant topics. We find that is able to create meaningful clusters of tweets of similar contents. We found 13 topics all of them relevant to the current Brazilian political scenario. The unsupervised sentiment analysis shows that, as a result of the complex political system (with multiple parties), many senators were identified as independent (19) and only one (out of 11) community can be classified as a community of senators that support the current government. All other detected communities are not relevant.",

keywords = "Autoencoder, Community detection, Convolutional networks, Deep learning, Text classification",

author = "Pedro Barros and Isadora Cardoso-Pereira and Keila Barbosa and Frery, {Alejandro C.} and H{\'e}ctor Allende-Cid and Ivan Martins and Ramos, {Heitor S.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 10th International Conference on Social Computing and Social Media, SCSM 2018 Held as Part of HCI International 2018 ; Conference date: 15-07-2018 Through 20-07-2018",

year = "2018",

doi = "10.1007/978-3-319-91485-5_13",

language = "English",

isbn = "9783319914848",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Barros, P, Cardoso-Pereira, I, Barbosa, K, Frery, AC, Allende-Cid, H, Martins, I & Ramos, HS 2018, Identifying communities in social media with deep learning. in G Meiselwitz (ed.), Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10914 LNCS, Springer Verlag, pp. 171-182, 10th International Conference on Social Computing and Social Media, SCSM 2018 Held as Part of HCI International 2018, Las Vegas, United States, 15/07/18. https://doi.org/10.1007/978-3-319-91485-5_13

Identifying communities in social media with deep learning. / Barros, Pedro; Cardoso-Pereira, Isadora; Barbosa, Keila et al.
Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings. ed. / Gabriele Meiselwitz. Springer Verlag, 2018. p. 171-182 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10914 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identifying communities in social media with deep learning

AU - Barros, Pedro

AU - Cardoso-Pereira, Isadora

AU - Barbosa, Keila

AU - Frery, Alejandro C.

AU - Allende-Cid, Héctor

AU - Martins, Ivan

AU - Ramos, Heitor S.

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - This work aims at analyzing twitter data to identify communities of Brazilian Senators. To do so, we collected data from 76 Brazilian Senators and used autoencoder and bi-gram to the content of tweets to find similar subjects and hence cluster the senators into groups. Thereafter, we applied an unsupervised sentiment analysis to identify the communities of senators that share similar sentiments about a selected number of relevant topics. We find that is able to create meaningful clusters of tweets of similar contents. We found 13 topics all of them relevant to the current Brazilian political scenario. The unsupervised sentiment analysis shows that, as a result of the complex political system (with multiple parties), many senators were identified as independent (19) and only one (out of 11) community can be classified as a community of senators that support the current government. All other detected communities are not relevant.

AB - This work aims at analyzing twitter data to identify communities of Brazilian Senators. To do so, we collected data from 76 Brazilian Senators and used autoencoder and bi-gram to the content of tweets to find similar subjects and hence cluster the senators into groups. Thereafter, we applied an unsupervised sentiment analysis to identify the communities of senators that share similar sentiments about a selected number of relevant topics. We find that is able to create meaningful clusters of tweets of similar contents. We found 13 topics all of them relevant to the current Brazilian political scenario. The unsupervised sentiment analysis shows that, as a result of the complex political system (with multiple parties), many senators were identified as independent (19) and only one (out of 11) community can be classified as a community of senators that support the current government. All other detected communities are not relevant.

KW - Autoencoder

KW - Community detection

KW - Convolutional networks

KW - Deep learning

KW - Text classification

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U2 - 10.1007/978-3-319-91485-5_13

DO - 10.1007/978-3-319-91485-5_13

M3 - Conference contribution

AN - SCOPUS:85050549337

SN - 9783319914848

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 171

EP - 182

BT - Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings

A2 - Meiselwitz, Gabriele

PB - Springer Verlag

T2 - 10th International Conference on Social Computing and Social Media, SCSM 2018 Held as Part of HCI International 2018

Y2 - 15 July 2018 through 20 July 2018

ER -

Barros P, Cardoso-Pereira I, Barbosa K, Frery AC, Allende-Cid H, Martins I et al. Identifying communities in social media with deep learning. In Meiselwitz G, editor, Social Computing and Social Media. Technologies and Analytics - 10th International Conference, SCSM 2018, Held as Part of HCI International 2018, Proceedings. Springer Verlag. 2018. p. 171-182. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-91485-5_13

Identifying communities in social media with deep learning

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