A method to evaluate the reliability of social media data for social network analysis

Derek Weber; Mehwish Nasim; Lewis Mitchell; Lucia Falzon

doi:10.1109/ASONAM49781.2020.9381461

A method to evaluate the reliability of social media data for social network analysis

Derek Weber, Mehwish Nasim, Lewis Mitchell, Lucia Falzon

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

9 Citations (Scopus)

Abstract

In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Editors	Martin Atzmuller, Michele Coscia, Rokia Missaoui
Publisher	Institute of Electrical and Electronics Engineers
Pages	317-321
Number of pages	5
ISBN (Electronic)	9781728110561
DOIs	https://doi.org/10.1109/ASONAM49781.2020.9381461
Publication status	Published - 7 Dec 2020
Externally published	Yes
Event	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands Duration: 7 Dec 2020 → 10 Dec 2020

Publication series

Name	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/Territory	Netherlands
City	Virtual, Online
Period	7/12/20 → 10/12/20

Keywords

social media data
data reliability
social network analysis
online social network (OSN)
real-world

Access to Document

10.1109/ASONAM49781.2020.9381461Licence: In Copyright

Cite this

Weber, D., Nasim, M., Mitchell, L., & Falzon, L. (2020). A method to evaluate the reliability of social media data for social network analysis. In M. Atzmuller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 (pp. 317-321). Article 9381461 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ASONAM49781.2020.9381461

Weber, Derek ; Nasim, Mehwish ; Mitchell, Lewis et al. / A method to evaluate the reliability of social media data for social network analysis. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. editor / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Institute of Electrical and Electronics Engineers, 2020. pp. 317-321 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

@inproceedings{1496e83ee6ef45a9b66c3413757a013e,

title = "A method to evaluate the reliability of social media data for social network analysis",

abstract = "In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.",

keywords = "social media data, data reliability, social network analysis, online social network (OSN), real-world",

author = "Derek Weber and Mehwish Nasim and Lewis Mitchell and Lucia Falzon",

year = "2020",

month = dec,

day = "7",

doi = "10.1109/ASONAM49781.2020.9381461",

language = "English",

series = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "317--321",

editor = "Martin Atzmuller and Michele Coscia and Rokia Missaoui",

booktitle = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",

address = "United States",

note = "12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 ; Conference date: 07-12-2020 Through 10-12-2020",

}

Weber, D, Nasim, M, Mitchell, L & Falzon, L 2020, A method to evaluate the reliability of social media data for social network analysis. in M Atzmuller, M Coscia & R Missaoui (eds), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020., 9381461, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Institute of Electrical and Electronics Engineers, pp. 317-321, 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Virtual, Online, Netherlands, 7/12/20. https://doi.org/10.1109/ASONAM49781.2020.9381461

A method to evaluate the reliability of social media data for social network analysis. / Weber, Derek; Nasim, Mehwish; Mitchell, Lewis et al.
Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers, 2020. p. 317-321 9381461 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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

TY - GEN

T1 - A method to evaluate the reliability of social media data for social network analysis

AU - Weber, Derek

AU - Nasim, Mehwish

AU - Mitchell, Lewis

AU - Falzon, Lucia

PY - 2020/12/7

Y1 - 2020/12/7

N2 - In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

AB - In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.

KW - social media data

KW - data reliability

KW - social network analysis

KW - online social network (OSN)

KW - real-world

UR - http://www.scopus.com/inward/record.url?scp=85102672932&partnerID=8YFLogxK

U2 - 10.1109/ASONAM49781.2020.9381461

DO - 10.1109/ASONAM49781.2020.9381461

M3 - Conference contribution

AN - SCOPUS:85102672932

T3 - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

SP - 317

EP - 321

BT - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

A2 - Atzmuller, Martin

A2 - Coscia, Michele

A2 - Missaoui, Rokia

PB - Institute of Electrical and Electronics Engineers

T2 - 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Y2 - 7 December 2020 through 10 December 2020

ER -

Weber D, Nasim M, Mitchell L, Falzon L. A method to evaluate the reliability of social media data for social network analysis. In Atzmuller M, Coscia M, Missaoui R, editors, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. Institute of Electrical and Electronics Engineers. 2020. p. 317-321. 9381461. (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). doi: 10.1109/ASONAM49781.2020.9381461

A method to evaluate the reliability of social media data for social network analysis

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this