Constructing Social Media Knowledge Graphs with Social Scientists

The increasing adoption and widespread use of social media provides significant opportunities for social scientists to discover novel insights of varying aspects of human behaviour. In response to increasing interest and research in this area, a wide variety of theoretical, methodological frameworks, guidelines and software tools have emerged. However, tools for collecting and analysing social media data are often inaccessible or unsuitable for social scientists. This is often due to interdisciplinary challenges that conflict with social scientists’ research aims, objectives and methodological approaches towards collecting and analysing social media. To address this, we are developing an extensible open source platform to support social scientists’ research in this area. This platform provides the means to collect and annotate social media data which can then be used to construct a knowledge graph. The knowledge graph provides social scientists with the means to consider their analysis within a broader context that may yield further insights.

Content

Author and article information

Contributors

John Paul Vargheese

Peter Travers

Jeff Pan

Kathryn Vincent

Claire Wallace

Anna Kabedeva

Conference

Publication date: July 2016

Publication date (Print): July 2016

Pages: 1-3

Affiliations

[0001]Computing Science

University of Aberdeen

Kings College

Aberdeen, AB24 5UA, UK

[0002]School of Social Science

University of Aberdeen

Kings College

Aberdeen, AB24 3QY, UK

Article

DOI: 10.14236/ewic/HCI2016.65

SO-VID: d1f377eb-366c-4f4f-9ba2-face1f9be490

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: Proceedings of the 30th International BCS Human Computer Interaction Conference

Conference acronym: HCI

Conference number: 30

Conference location: Bournemouth University, Poole, UK

Conference date: 11 - 15 July 2016

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Fusion

History

Product

1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2016.65

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

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A. M. KaplanM. Haenlein 2010 Users of the world, unite! the challenges and opportunities of social media. Business horizons 53 1 59 68
J. H. KietzmannK. HermkensI. P McCarthyB. S. Silvestre 2011 Social media? get serious! understanding the functional building blocks of social media. Business horizons 54 3 241 251
K. PetersY. ChenA. M. KaplanB. OgnibeniK. Pauwels 2013 Social media metrics?a framework and guidelines for managing social media. Journal of Interactive Marketing 27 4 281 298
B. Settles 2011 Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances. Proceedings of the Conference on Empirical Methods in Natural Language Processing 1467 1478 Association for Computational Linguistics
D. V. ShahJ. N. CappellaW. R. Neuman 2015 Big data, digital media, and computational social science possibilities and perils. The ANNALS of the American Academy of Political and Social Science 659 1 6 13
A. Singhal 2012 Introducing the knowledge graph: things, not strings. Official Google Blog, May Official Google Blog, May
S. StieglitzL. Dang-Xuan 2013 Social media and political communication: a social media analytics framework. Social Network Analysis and Mining 3 4 1277 1291

Comments

Comment on this article

[1] B. BatrincaP. C. Treleaven 2015 Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY 30 1 89 116

[2] N. CaoL. LuY.-R. LinF. WangZ. Wen 2015 Socialhelix: visual analysis of sentiment divergence in social media. Journal of Visualization 18 2 221 235

[3] J. GolbeckC. RoblesK. Turner 2011 Predicting personality with social media. CHI’11 Extended Abstracts on Human Factors in Computing Systems 253 262 ACM

[4] A. GuilleC. FavreH. HacidD. A. Zighed 2013 Sondy: An open source platform for social dynamics mining and analysis. Proceedings of the 2013 ACM SIG MOD International Conference on Management of Data 1005 1008 ACM

[5] D. J. HopkinsG. King 2010 A method of automated nonparametric content analysis for social science. American Journal of Political Science 54 1 229 247

[6] N. JaafarM. Al-JadaanR. Alnutaifi et al 2015 Framework for social media big data quality analysis. New Trends in Database and Information Systems II 301 314 Springer

[7] A. M. KaplanM. Haenlein 2010 Users of the world, unite! the challenges and opportunities of social media. Business horizons 53 1 59 68

[8] J. H. KietzmannK. HermkensI. P McCarthyB. S. Silvestre 2011 Social media? get serious! understanding the functional building blocks of social media. Business horizons 54 3 241 251

[9] K. PetersY. ChenA. M. KaplanB. OgnibeniK. Pauwels 2013 Social media metrics?a framework and guidelines for managing social media. Journal of Interactive Marketing 27 4 281 298

[10] B. Settles 2011 Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances. Proceedings of the Conference on Empirical Methods in Natural Language Processing 1467 1478 Association for Computational Linguistics

[11] D. V. ShahJ. N. CappellaW. R. Neuman 2015 Big data, digital media, and computational social science possibilities and perils. The ANNALS of the American Academy of Political and Social Science 659 1 6 13

[12] A. Singhal 2012 Introducing the knowledge graph: things, not strings. Official Google Blog, May Official Google Blog, May

[13] S. StieglitzL. Dang-Xuan 2013 Social media and political communication: a social media analytics framework. Social Network Analysis and Mining 3 4 1277 1291

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