FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall

Aimilia Panagiotou; Bogdan Ghita; Stavros Shiaeles; Keltoum Bendiab

doi:10.1007/978-3-030-30859-9_11

FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall

Aimilia Panagiotou, Bogdan Ghita, Stavros Shiaeles^*, Keltoum Bendiab

^*Corresponding author for this work

School of Computing

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

336 Downloads (Pure)

Abstract

Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.

Original language	English
Title of host publication	Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings
Editors	Olga Galinina, Sergey Andreev, Yevgeni Koucheryavy, Sergey Balandin
Publisher	Springer
Pages	125-134
Number of pages	10
ISBN (Print)	9783030308582
DOIs	https://doi.org/10.1007/978-3-030-30859-9_11
Publication status	Published - 12 Sept 2019
Event	19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019 - St. Petersburg, Russian Federation Duration: 26 Aug 2019 → 28 Aug 2019

Publication series

Name	Lecture Notes in Computer Science
Volume	11660
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019
Country/Territory	Russian Federation
City	St. Petersburg
Period	26/08/19 → 28/08/19

Keywords

Facebook
Information collection
Machine learning
OSINT
Social media
Web crawler

Access to Document

10.1007/978-3-030-30859-9_11

FaceWallGrap
This is a post-peer-review, pre-copyedit version of an article published in Galinina O., Andreev S., Balandin S., Koucheryavy Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2019, ruSMART 2019. Lecture Notes in Computer Science, vol 11660.. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-30859-9_11.
Accepted author manuscript (Post-print), 483 KB

Cite this

Panagiotou, A., Ghita, B., Shiaeles, S., & Bendiab, K. (2019). FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall. In O. Galinina, S. Andreev, Y. Koucheryavy, & S. Balandin (Eds.), Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings (pp. 125-134). (Lecture Notes in Computer Science; Vol. 11660). Springer. https://doi.org/10.1007/978-3-030-30859-9_11

Panagiotou, Aimilia ; Ghita, Bogdan ; Shiaeles, Stavros et al. / FaceWallGraph : using machine learning for profiling user behaviour from Facebook Wall. Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. editor / Olga Galinina ; Sergey Andreev ; Yevgeni Koucheryavy ; Sergey Balandin. Springer, 2019. pp. 125-134 (Lecture Notes in Computer Science).

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title = "FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall",

abstract = "Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.",

keywords = "Facebook, Information collection, Machine learning, OSINT, Social media, Web crawler",

author = "Aimilia Panagiotou and Bogdan Ghita and Stavros Shiaeles and Keltoum Bendiab",

year = "2019",

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day = "12",

doi = "10.1007/978-3-030-30859-9_11",

language = "English",

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booktitle = "Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings",

note = "19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019 ; Conference date: 26-08-2019 Through 28-08-2019",

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Panagiotou, A, Ghita, B, Shiaeles, S & Bendiab, K 2019, FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall. in O Galinina, S Andreev, Y Koucheryavy & S Balandin (eds), Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. Lecture Notes in Computer Science, vol. 11660, Springer, pp. 125-134, 19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019, St. Petersburg, Russian Federation, 26/08/19. https://doi.org/10.1007/978-3-030-30859-9_11

FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall. / Panagiotou, Aimilia; Ghita, Bogdan; Shiaeles, Stavros et al.
Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. ed. / Olga Galinina; Sergey Andreev; Yevgeni Koucheryavy; Sergey Balandin. Springer, 2019. p. 125-134 (Lecture Notes in Computer Science; Vol. 11660).

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

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T2 - 19th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2019, and 12th Conference on Internet of Things and Smart Spaces, ruSMART 2019

AU - Panagiotou, Aimilia

AU - Ghita, Bogdan

AU - Shiaeles, Stavros

AU - Bendiab, Keltoum

PY - 2019/9/12

Y1 - 2019/9/12

N2 - Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.

AB - Facebook represents the current de-facto choice for social media, changing the nature of social relationships. The increasing amount of personal information that runs through this platform publicly exposes user behaviour and social trends, allowing aggregation of data through conventional intelligence collection techniques such as OSINT (Open Source Intelligence). In this paper, we propose a new method to detect and diagnose variations in overall Facebook user psychology through Open Source Intelligence (OSINT) and machine learning techniques. We are aggregating the spectrum of user sentiments and views by using N-Games charts, which exhibit noticeable variations over time, validated through long term collection. We postulate that the proposed approach can be used by security organisations to understand and evaluate the user psychology, then use the information to predict insider threats or prevent insider attacks.

KW - Facebook

KW - Information collection

KW - Machine learning

KW - OSINT

KW - Social media

KW - Web crawler

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U2 - 10.1007/978-3-030-30859-9_11

DO - 10.1007/978-3-030-30859-9_11

M3 - Conference contribution

AN - SCOPUS:85072979105

SN - 9783030308582

T3 - Lecture Notes in Computer Science

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BT - Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings

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A2 - Andreev, Sergey

A2 - Koucheryavy, Yevgeni

A2 - Balandin, Sergey

PB - Springer

Y2 - 26 August 2019 through 28 August 2019

ER -

Panagiotou A, Ghita B, Shiaeles S, Bendiab K. FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall. In Galinina O, Andreev S, Koucheryavy Y, Balandin S, editors, Internet of Things, Smart Spaces, and Next Generation Networks and Systems - 19th International Conference, NEW2AN 2019, and 12th Conference, ruSMART 2019, Proceedings. Springer. 2019. p. 125-134. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-30859-9_11

FaceWallGraph: using machine learning for profiling user behaviour from Facebook Wall

Abstract

Publication series

Conference

Keywords

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Cite this