Standard

Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions. / Непиющих, Дмитрий Викторович; Блеканов, Иван Станиславович; Тарасов, Никита Андреевич; Максимов, Алексей Юрьевич.

Social Computing and Social Media. HCII 2024.. Springer Nature, 2024. p. 72-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14703 LNCS).

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

Harvard

Непиющих, ДВ, Блеканов, ИС, Тарасов, НА & Максимов, АЮ 2024, Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions. in Social Computing and Social Media. HCII 2024.. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14703 LNCS, Springer Nature, pp. 72-81, Social Computing and Social Media: 16th International Conference, Washington, United States, 29/06/24. https://doi.org/10.1007/978-3-031-61281-7_5

APA

Непиющих, Д. В., Блеканов, И. С., Тарасов, Н. А., & Максимов, А. Ю. (2024). Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions. In Social Computing and Social Media. HCII 2024. (pp. 72-81). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14703 LNCS). Springer Nature. https://doi.org/10.1007/978-3-031-61281-7_5

Vancouver

Непиющих ДВ, Блеканов ИС, Тарасов НА, Максимов АЮ. Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions. In Social Computing and Social Media. HCII 2024.. Springer Nature. 2024. p. 72-81. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-031-61281-7_5

Author

Непиющих, Дмитрий Викторович ; Блеканов, Иван Станиславович ; Тарасов, Никита Андреевич ; Максимов, Алексей Юрьевич. / Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions. Social Computing and Social Media. HCII 2024.. Springer Nature, 2024. pp. 72-81 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{f389986a546b40dba623324cce5a5198,
title = "Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions",
abstract = "It is widely accepted that nowadays a significant part of the content on the internet is generated by users of social media platforms which form the basis of Web 2.0. That is why modern media researchers use user-generated content to test their scientific hypotheses using automated data analysis methods and data mining tools. In this study we examine the main approaches to user opinion data crawling in modern social media platforms for subsequent analysis for scientific and research purposes. We propose a data collection approach based on reverse engineering of APK applications, which allows for data extraction from social networks that will not differ in completeness from data from mobile applications. A comparative analysis of the proposed methods in terms of completeness and execution speed is also carried out. According to our findings, implementing a custom REST API is the best approach as it is both reliable and computationally efficient.",
keywords = "API, Data Collection, Data Crawling, SSL Pinning, User Opinion, Web Crawler, gRPC",
author = "Непиющих, {Дмитрий Викторович} and Блеканов, {Иван Станиславович} and Тарасов, {Никита Андреевич} and Максимов, {Алексей Юрьевич}",
year = "2024",
doi = "10.1007/978-3-031-61281-7_5",
language = "English",
isbn = "978-3-031-61280-0",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "72--81",
booktitle = "Social Computing and Social Media. HCII 2024.",
address = "Germany",
note = "Social Computing and Social Media: 16th International Conference : Held as Part of the 26th HCI International Conference, SCSM 2024 ; Conference date: 29-06-2024 Through 04-07-2024",

}

RIS

TY - GEN

T1 - Methods of User Opinion Data Crawling in Web 2.0 Social Network Discussions

AU - Непиющих, Дмитрий Викторович

AU - Блеканов, Иван Станиславович

AU - Тарасов, Никита Андреевич

AU - Максимов, Алексей Юрьевич

PY - 2024

Y1 - 2024

N2 - It is widely accepted that nowadays a significant part of the content on the internet is generated by users of social media platforms which form the basis of Web 2.0. That is why modern media researchers use user-generated content to test their scientific hypotheses using automated data analysis methods and data mining tools. In this study we examine the main approaches to user opinion data crawling in modern social media platforms for subsequent analysis for scientific and research purposes. We propose a data collection approach based on reverse engineering of APK applications, which allows for data extraction from social networks that will not differ in completeness from data from mobile applications. A comparative analysis of the proposed methods in terms of completeness and execution speed is also carried out. According to our findings, implementing a custom REST API is the best approach as it is both reliable and computationally efficient.

AB - It is widely accepted that nowadays a significant part of the content on the internet is generated by users of social media platforms which form the basis of Web 2.0. That is why modern media researchers use user-generated content to test their scientific hypotheses using automated data analysis methods and data mining tools. In this study we examine the main approaches to user opinion data crawling in modern social media platforms for subsequent analysis for scientific and research purposes. We propose a data collection approach based on reverse engineering of APK applications, which allows for data extraction from social networks that will not differ in completeness from data from mobile applications. A comparative analysis of the proposed methods in terms of completeness and execution speed is also carried out. According to our findings, implementing a custom REST API is the best approach as it is both reliable and computationally efficient.

KW - API

KW - Data Collection

KW - Data Crawling

KW - SSL Pinning

KW - User Opinion

KW - Web Crawler

KW - gRPC

UR - https://link.springer.com/chapter/10.1007/978-3-031-61281-7_5

UR - https://www.mendeley.com/catalogue/e6f87ecd-4720-3fcf-bad3-afc77be8db4c/

U2 - 10.1007/978-3-031-61281-7_5

DO - 10.1007/978-3-031-61281-7_5

M3 - Conference contribution

SN - 978-3-031-61280-0

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

SP - 72

EP - 81

BT - Social Computing and Social Media. HCII 2024.

PB - Springer Nature

T2 - Social Computing and Social Media: 16th International Conference

Y2 - 29 June 2024 through 4 July 2024

ER -

ID: 125271477