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Sampling labelled profile data for identity resolution

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Sampling labelled profile data for identity resolution. / Edwards, Matthew; Wattam, Stephen Michael; Rayson, Paul Edward; Rashid, Awais.

Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016). IEEE, 2016. p. 540-547.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Edwards, M, Wattam, SM, Rayson, PE & Rashid, A 2016, Sampling labelled profile data for identity resolution. in Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016). IEEE, pp. 540-547, 2016 IEEE International Conference on Big Data (Big Data), Washington, D.C., United States, 5/12/16. https://doi.org/10.1109/BigData.2016.7840645

APA

Edwards, M., Wattam, S. M., Rayson, P. E., & Rashid, A. (2016). Sampling labelled profile data for identity resolution. In Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016) (pp. 540-547). IEEE. https://doi.org/10.1109/BigData.2016.7840645

Vancouver

Edwards M, Wattam SM, Rayson PE, Rashid A. Sampling labelled profile data for identity resolution. In Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016). IEEE. 2016. p. 540-547 https://doi.org/10.1109/BigData.2016.7840645

Author

Edwards, Matthew ; Wattam, Stephen Michael ; Rayson, Paul Edward ; Rashid, Awais. / Sampling labelled profile data for identity resolution. Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016). IEEE, 2016. pp. 540-547

Bibtex

@inproceedings{2664fc9ff4a04ec9afe91f453c4dfea2,
title = "Sampling labelled profile data for identity resolution",
abstract = "Identity resolution capability for social networking profiles is important for a range of purposes, from open-source intelligence applications to forming semantic web connections. Yet replication of research in this area is hampered by the lack of access to ground-truth data linking the identities of profiles from different networks. Almost all data sources previously used by researchers are no longer available, and historic datasets are both of decreasing relevance to the modern social networking landscape and ethically troublesome regarding the preservation and publication of personal data. We present and evaluate a method which provides researchers in identity resolution with easy access to a realistically-challenging labelled dataset of online profiles, drawing on four of the currently largest and most influential online social networks. We validate the comparability of samples drawn through this method and discuss the implications of this mechanism for researchers as well as potential alternatives and extensions.",
author = "Matthew Edwards and Wattam, {Stephen Michael} and Rayson, {Paul Edward} and Awais Rashid",
note = "{\circledC}2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2016",
month = "12",
day = "5",
doi = "10.1109/BigData.2016.7840645",
language = "English",
isbn = "9781467390064",
pages = "540--547",
booktitle = "Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Sampling labelled profile data for identity resolution

AU - Edwards, Matthew

AU - Wattam, Stephen Michael

AU - Rayson, Paul Edward

AU - Rashid, Awais

N1 - ©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2016/12/5

Y1 - 2016/12/5

N2 - Identity resolution capability for social networking profiles is important for a range of purposes, from open-source intelligence applications to forming semantic web connections. Yet replication of research in this area is hampered by the lack of access to ground-truth data linking the identities of profiles from different networks. Almost all data sources previously used by researchers are no longer available, and historic datasets are both of decreasing relevance to the modern social networking landscape and ethically troublesome regarding the preservation and publication of personal data. We present and evaluate a method which provides researchers in identity resolution with easy access to a realistically-challenging labelled dataset of online profiles, drawing on four of the currently largest and most influential online social networks. We validate the comparability of samples drawn through this method and discuss the implications of this mechanism for researchers as well as potential alternatives and extensions.

AB - Identity resolution capability for social networking profiles is important for a range of purposes, from open-source intelligence applications to forming semantic web connections. Yet replication of research in this area is hampered by the lack of access to ground-truth data linking the identities of profiles from different networks. Almost all data sources previously used by researchers are no longer available, and historic datasets are both of decreasing relevance to the modern social networking landscape and ethically troublesome regarding the preservation and publication of personal data. We present and evaluate a method which provides researchers in identity resolution with easy access to a realistically-challenging labelled dataset of online profiles, drawing on four of the currently largest and most influential online social networks. We validate the comparability of samples drawn through this method and discuss the implications of this mechanism for researchers as well as potential alternatives and extensions.

U2 - 10.1109/BigData.2016.7840645

DO - 10.1109/BigData.2016.7840645

M3 - Conference contribution/Paper

SN - 9781467390064

SP - 540

EP - 547

BT - Proceedings of IEEE International Conference on Big Data (IEEE BigData 2016)

PB - IEEE

ER -