Bot-Human Twitter Messages Classification

Carolina Martín-del-Campo-Rodríguez; Grigori Sidorov; Ildar Batyrshin

doi:10.1007/978-3-030-60887-3_7

Bot-Human Twitter Messages Classification

Carolina Martín-del-Campo-Rodríguez, Grigori Sidorov, Ildar Batyrshin

Centro de Investigación en Computación (CIC)

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

Abstract

Bots identification has gained relevance within social networks due to its ability to influence the opinion of users on political, consumer and ideological issues. This is why research related to bot identification has grown in recent years. Various models have been proposed for the identification of bots, but this is an issue that has not been resolved yet. In this article, a model is proposed that, through the use of specific preprocessing and a four-layer neural network, improves the bot-human classification accuracy of Twitter messages, reaching a precision of 0.9462, which represents an advance with respect to what is presented in the state of the art with the same corpus.

Original language	English
Title of host publication	Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings
Editors	Lourdes Martínez-Villaseñor, Hiram Ponce, Oscar Herrera-Alcántara, Félix A. Castro-Espinoza
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	74-80
Number of pages	7
ISBN (Print)	9783030608866
DOIs	https://doi.org/10.1007/978-3-030-60887-3_7
State	Published - 2020
Event	19th Mexican International Conference on Artificial Intelligence, MICAI 2020 - Mexico City, Mexico Duration: 12 Oct 2020 → 17 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12469 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Mexican International Conference on Artificial Intelligence, MICAI 2020
Country/Territory	Mexico
City	Mexico City
Period	12/10/20 → 17/10/20

Keywords

Bots identification
Neural networks
Text preprocessing
Twitter

Access to Document

10.1007/978-3-030-60887-3_7

Cite this

Martín-del-Campo-Rodríguez, C., Sidorov, G., & Batyrshin, I. (2020). Bot-Human Twitter Messages Classification. In L. Martínez-Villaseñor, H. Ponce, O. Herrera-Alcántara, & F. A. Castro-Espinoza (Eds.), Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings (pp. 74-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12469 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60887-3_7

Martín-del-Campo-Rodríguez, Carolina ; Sidorov, Grigori ; Batyrshin, Ildar. / Bot-Human Twitter Messages Classification. Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. editor / Lourdes Martínez-Villaseñor ; Hiram Ponce ; Oscar Herrera-Alcántara ; Félix A. Castro-Espinoza. Springer Science and Business Media Deutschland GmbH, 2020. pp. 74-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Bots identification has gained relevance within social networks due to its ability to influence the opinion of users on political, consumer and ideological issues. This is why research related to bot identification has grown in recent years. Various models have been proposed for the identification of bots, but this is an issue that has not been resolved yet. In this article, a model is proposed that, through the use of specific preprocessing and a four-layer neural network, improves the bot-human classification accuracy of Twitter messages, reaching a precision of 0.9462, which represents an advance with respect to what is presented in the state of the art with the same corpus.",

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Martín-del-Campo-Rodríguez, C, Sidorov, G & Batyrshin, I 2020, Bot-Human Twitter Messages Classification. in L Martínez-Villaseñor, H Ponce, O Herrera-Alcántara & FA Castro-Espinoza (eds), Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12469 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 74-80, 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Mexico City, Mexico, 12/10/20. https://doi.org/10.1007/978-3-030-60887-3_7

Bot-Human Twitter Messages Classification. / Martín-del-Campo-Rodríguez, Carolina; Sidorov, Grigori ; Batyrshin, Ildar.
Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. ed. / Lourdes Martínez-Villaseñor; Hiram Ponce; Oscar Herrera-Alcántara; Félix A. Castro-Espinoza. Springer Science and Business Media Deutschland GmbH, 2020. p. 74-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12469 LNAI).

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

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T1 - Bot-Human Twitter Messages Classification

AU - Martín-del-Campo-Rodríguez, Carolina

AU - Sidorov, Grigori

AU - Batyrshin, Ildar

PY - 2020

Y1 - 2020

N2 - Bots identification has gained relevance within social networks due to its ability to influence the opinion of users on political, consumer and ideological issues. This is why research related to bot identification has grown in recent years. Various models have been proposed for the identification of bots, but this is an issue that has not been resolved yet. In this article, a model is proposed that, through the use of specific preprocessing and a four-layer neural network, improves the bot-human classification accuracy of Twitter messages, reaching a precision of 0.9462, which represents an advance with respect to what is presented in the state of the art with the same corpus.

AB - Bots identification has gained relevance within social networks due to its ability to influence the opinion of users on political, consumer and ideological issues. This is why research related to bot identification has grown in recent years. Various models have been proposed for the identification of bots, but this is an issue that has not been resolved yet. In this article, a model is proposed that, through the use of specific preprocessing and a four-layer neural network, improves the bot-human classification accuracy of Twitter messages, reaching a precision of 0.9462, which represents an advance with respect to what is presented in the state of the art with the same corpus.

KW - Bots identification

KW - Neural networks

KW - Text preprocessing

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SN - 9783030608866

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

SP - 74

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BT - Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings

A2 - Martínez-Villaseñor, Lourdes

A2 - Ponce, Hiram

A2 - Herrera-Alcántara, Oscar

A2 - Castro-Espinoza, Félix A.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020

Y2 - 12 October 2020 through 17 October 2020

ER -

Martín-del-Campo-Rodríguez C, Sidorov G , Batyrshin I. Bot-Human Twitter Messages Classification. In Martínez-Villaseñor L, Ponce H, Herrera-Alcántara O, Castro-Espinoza FA, editors, Advances in Computational Intelligence - 19th Mexican International Conference on Artificial Intelligence, MICAI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 74-80. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60887-3_7

Bot-Human Twitter Messages Classification

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