Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Sift Descriptor for Social Media User Accounts Matching. / Korepanova, Anastasia A. ; Abramov, Maxim V. .
Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). Cham : Springer Nature, 2022. p. 142-151 (Lecture Notes in Networks and Systems; Vol. 566).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Sift Descriptor for Social Media User Accounts Matching
AU - Korepanova, Anastasia A.
AU - Abramov, Maxim V.
N1 - Korepanova, A.A., Abramov, M.V. (2023). Sift Descriptor for Social Media User Accounts Matching. In: Kovalev, S., Sukhanov, A., Akperov, I., Ozdemir, S. (eds) Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). IITI 2022. Lecture Notes in Networks and Systems, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-031-19620-1_14
PY - 2022/10/31
Y1 - 2022/10/31
N2 - Book coverInternational Conference on Intelligent Information Technologies for IndustryIITI 2022: Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22) pp 142–151Cite asSift Descriptor for Social Media User Accounts MatchingAnastasia A. Korepanova & Maxim V. Abramov Conference paperFirst Online: 31 October 202212 AccessesPart of the Lecture Notes in Networks and Systems book series (LNNS,volume 566)AbstractThe task of user accounts matching in different social media and determining those belonging to the same user is relevant in various contexts related to the analysis of social media. The solution of this problem is of both theoretical importance and allows you to expand the understanding of the behavior of users in social media, as well as practical and can be applied to collect data about a single user. This work is devoted to solving the problem of automation of matching the accounts of social media users by analyzing the graphic content posted in the accounts. Previously, a method was proposed to solve this problem, based on the use of a number of content features, the current study develops the proposed approach by searching for duplicate images using SIRF. As a result, a new model for classifying pairs of accounts into the classes “belongs to one user” and “does not belong to one user” was proposed, which achieved higher accuracy and f-score compared to previous results.
AB - Book coverInternational Conference on Intelligent Information Technologies for IndustryIITI 2022: Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22) pp 142–151Cite asSift Descriptor for Social Media User Accounts MatchingAnastasia A. Korepanova & Maxim V. Abramov Conference paperFirst Online: 31 October 202212 AccessesPart of the Lecture Notes in Networks and Systems book series (LNNS,volume 566)AbstractThe task of user accounts matching in different social media and determining those belonging to the same user is relevant in various contexts related to the analysis of social media. The solution of this problem is of both theoretical importance and allows you to expand the understanding of the behavior of users in social media, as well as practical and can be applied to collect data about a single user. This work is devoted to solving the problem of automation of matching the accounts of social media users by analyzing the graphic content posted in the accounts. Previously, a method was proposed to solve this problem, based on the use of a number of content features, the current study develops the proposed approach by searching for duplicate images using SIRF. As a result, a new model for classifying pairs of accounts into the classes “belongs to one user” and “does not belong to one user” was proposed, which achieved higher accuracy and f-score compared to previous results.
KW - social media
KW - Account matching
KW - Image processing
KW - Machine learning
KW - Social engineering attacks
UR - https://link.springer.com/book/9783031196218
M3 - Conference contribution
SN - 978-3-031-19619-5
T3 - Lecture Notes in Networks and Systems
SP - 142
EP - 151
BT - Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22)
PB - Springer Nature
CY - Cham
T2 - 6th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2022
Y2 - 31 October 2022 through 6 November 2022
ER -
ID: 99501912