Social Interaction Model

Benjamin Johannes Flesch

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper introduces a novel conceptual foundation for Social Set Analysis, the Social Interaction Model. It contributes to the state-of-the-art theory in Big Social Data by extending the existing Social Data Model through proposal of an improved concept which is formally grounded in set theory and relational algebra. The concept of Interactions based on one initial Action between two social media Actors, and zero or many Reactions, all referencing the initial Action, is presented. Furthermore, temporal and spatial dimensions are included as a core component of the proposed model, thus streamlining data analytics tasks in the realm of Social Set Analysis. Key differences between the existing Social Data Model and the Social Interaction Model contributed in this paper are the inclusion of non-textual artifact content types, the unification of a previously bipartite Social Data Model, the deprecation of an empirically vague notion of Activities, and improved interoperability between data sources.
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE International Conference on Big Data
EditorsNaoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey Saltz
Number of pages3
Place of PublicationLos Alamos, CA
PublisherIEEE
Publication date2019
Pages3656-3658
Article number8622346
ISBN (Print)9781538650363
ISBN (Electronic)9781538650349, 9781538650356
DOIs
Publication statusPublished - 2019
Event2018 IEEE International Conference on Big Data - The Westin Seattle, Seattle, United States
Duration: 10 Dec 201813 Dec 2018
Conference number: 6
http://cci.drexel.edu/bigdata/bigdata2018/index.html

Conference

Conference2018 IEEE International Conference on Big Data
Number6
LocationThe Westin Seattle
CountryUnited States
CitySeattle
Period10/12/201813/12/2018
Internet address

Bibliographical note

CBS Library does not have access to the material

Keywords

  • Big data analytics
  • Big social data
  • Computational social science
  • Social set analysis
  • Social data model

Cite this

Flesch, B. J. (2019). Social Interaction Model. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, Y. Song, D. Kossmann, B. Liu, K. Lee, J. Tang, J. He, ... J. Saltz (Eds.), Proceedings of the 2018 IEEE International Conference on Big Data (pp. 3656-3658). [8622346] Los Alamos, CA: IEEE. https://doi.org/10.1109/BigData.2018.8622346
Flesch, Benjamin Johannes. / Social Interaction Model. Proceedings of the 2018 IEEE International Conference on Big Data. editor / Naoki Abe ; Huan Liu ; Calton Pu ; Xiaohua Hu ; Nesreen Ahmed ; Mu Qiao ; Yang Song ; Donald Kossmann ; Bing Liu ; Kisung Lee ; Jiliang Tang ; Jingrui He ; Jeffrey Saltz. Los Alamos, CA : IEEE, 2019. pp. 3656-3658
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title = "Social Interaction Model",
abstract = "This paper introduces a novel conceptual foundation for Social Set Analysis, the Social Interaction Model. It contributes to the state-of-the-art theory in Big Social Data by extending the existing Social Data Model through proposal of an improved concept which is formally grounded in set theory and relational algebra. The concept of Interactions based on one initial Action between two social media Actors, and zero or many Reactions, all referencing the initial Action, is presented. Furthermore, temporal and spatial dimensions are included as a core component of the proposed model, thus streamlining data analytics tasks in the realm of Social Set Analysis. Key differences between the existing Social Data Model and the Social Interaction Model contributed in this paper are the inclusion of non-textual artifact content types, the unification of a previously bipartite Social Data Model, the deprecation of an empirically vague notion of Activities, and improved interoperability between data sources.",
keywords = "Big data analytics, Big social data, Computational social science, Social set analysis, Social data model, Big data analytics, Big social data, Computational social science, Social set analysis, Social data model",
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Flesch, BJ 2019, Social Interaction Model. in N Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He & J Saltz (eds), Proceedings of the 2018 IEEE International Conference on Big Data., 8622346, IEEE, Los Alamos, CA, pp. 3656-3658, Seattle, United States, 10/12/2018. https://doi.org/10.1109/BigData.2018.8622346

Social Interaction Model. / Flesch, Benjamin Johannes.

Proceedings of the 2018 IEEE International Conference on Big Data. ed. / Naoki Abe; Huan Liu; Calton Pu; Xiaohua Hu; Nesreen Ahmed; Mu Qiao; Yang Song; Donald Kossmann; Bing Liu; Kisung Lee; Jiliang Tang; Jingrui He; Jeffrey Saltz. Los Alamos, CA : IEEE, 2019. p. 3656-3658 8622346.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Social Interaction Model

AU - Flesch, Benjamin Johannes

N1 - CBS Library does not have access to the material

PY - 2019

Y1 - 2019

N2 - This paper introduces a novel conceptual foundation for Social Set Analysis, the Social Interaction Model. It contributes to the state-of-the-art theory in Big Social Data by extending the existing Social Data Model through proposal of an improved concept which is formally grounded in set theory and relational algebra. The concept of Interactions based on one initial Action between two social media Actors, and zero or many Reactions, all referencing the initial Action, is presented. Furthermore, temporal and spatial dimensions are included as a core component of the proposed model, thus streamlining data analytics tasks in the realm of Social Set Analysis. Key differences between the existing Social Data Model and the Social Interaction Model contributed in this paper are the inclusion of non-textual artifact content types, the unification of a previously bipartite Social Data Model, the deprecation of an empirically vague notion of Activities, and improved interoperability between data sources.

AB - This paper introduces a novel conceptual foundation for Social Set Analysis, the Social Interaction Model. It contributes to the state-of-the-art theory in Big Social Data by extending the existing Social Data Model through proposal of an improved concept which is formally grounded in set theory and relational algebra. The concept of Interactions based on one initial Action between two social media Actors, and zero or many Reactions, all referencing the initial Action, is presented. Furthermore, temporal and spatial dimensions are included as a core component of the proposed model, thus streamlining data analytics tasks in the realm of Social Set Analysis. Key differences between the existing Social Data Model and the Social Interaction Model contributed in this paper are the inclusion of non-textual artifact content types, the unification of a previously bipartite Social Data Model, the deprecation of an empirically vague notion of Activities, and improved interoperability between data sources.

KW - Big data analytics

KW - Big social data

KW - Computational social science

KW - Social set analysis

KW - Social data model

KW - Big data analytics

KW - Big social data

KW - Computational social science

KW - Social set analysis

KW - Social data model

U2 - 10.1109/BigData.2018.8622346

DO - 10.1109/BigData.2018.8622346

M3 - Article in proceedings

SN - 9781538650363

SP - 3656

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BT - Proceedings of the 2018 IEEE International Conference on Big Data

A2 - Abe, Naoki

A2 - Liu, Huan

A2 - Pu, Calton

A2 - Hu, Xiaohua

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A2 - Qiao, Mu

A2 - Song, Yang

A2 - Kossmann, Donald

A2 - Liu, Bing

A2 - Lee, Kisung

A2 - Tang, Jiliang

A2 - He, Jingrui

A2 - Saltz, Jeffrey

PB - IEEE

CY - Los Alamos, CA

ER -

Flesch BJ. Social Interaction Model. In Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, editors, Proceedings of the 2018 IEEE International Conference on Big Data. Los Alamos, CA: IEEE. 2019. p. 3656-3658. 8622346 https://doi.org/10.1109/BigData.2018.8622346