Skip to main content
SpringerLink
Log in

Detecting sociosemantic communities by applying social network analysis in tweets

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Virtual social networks have led to a new way of communication that is different from the oral one, where the restriction of time and space generates new linguistic practices. Twitter, a medium for political and social discussion, can be analyzed to understand new ways of communication and to explore sociosemiotics aspects that come with the use of the hashtags and their relationship with other elements. This paper presents a quantitative study of tweets, around a fixed hashtag, in relation with other contents that users bring to connection. By calculating the frequency of terms, a table of nodes and edges is created to visualize tweets like graphs. Our study applies social network analysis that, going beyond mere topology, reveals relevant sociosemantic communities providing insights for the comparison of social and political movements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.dailymail.co.uk/news/article-2522868/Mexican-congressman-takes-clothes-angry-protest-historic-energy-privatization-scuffles-break-doors-barricaded.html.

  2. http://pri.org.mx/masconmenospluris/ consulted 10 October 2014.

References

  • Abascal-Mena R, López-Ornelas E, Zepeda-Hernández JS (2013) User generated content: an analysis of user behavior by mining political tweets. In: Ant Ozok A, Zaphiris P (eds) Online communities and social computing. Lecture Notes in Computer Science Volume 8029, Springer, Heidelberg, pp 3–12

    Google Scholar 

  • Arquilla J, Ronfeldt D (2003) Networks and netwars: the future of terror, crime, and militancy. RAND, Santa Monica, p 380

  • Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. International AAAI conference on weblogs and social media

  • Bastos MT, Puschmann C, Travitzki R (2012) Tweeting political dissent: retweets as pamphlets in #FreeIran, #FreeVenezuela, #Jan25, #SpanishRevolution and #OccupyWallSt. Internet, politics, policy 2012: big data, big challenges? Oxford. Panel 6A: the Arab spring and political dissent, vol 2. Oxford Internet Institute, Oxford, pp 1–20

  • Bertrand KZ, Bialik M, Virdee K, Gros A, Bar-Yam Y (2013) Sentiment in New York City: a high resolution spatial and temporal view. arXiv:1308.5010

  • Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177

    Article  MATH  Google Scholar 

  • Bruns A, Burgess JE (2011) The use of Twitter hashtags in the formation of ad hoc publics. 6th European Consortium for political research general conference, University of Iceland, Reykjavik

  • Castells M (2008) The new public sphere: Global civil society, communication networks, and global governance. Ann Am Acad Political Social Sci 616(1):78–93

    Article  Google Scholar 

  • Chew C, Eysenbach G (2010) Pandemics in the age of Twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS One 5(11):e14118. doi:10.1371/journal.pone.0014118

    Article  Google Scholar 

  • Chung JE, Mustafaraj E (2011) Can collective sentiment expressed on Twitter predict political elections?. In: Burgard W, Roth D (eds) Proceedings of the 25th AAAI conference on artificial intelligence (AAAI 2011). AAAI Press, Menlo Park, pp 1768–1769

  • Conover M, Ratkiewicz J et al (2011) Political polarization on twitter. In: Proceedings 5th International AAAI conference on weblogs and social media (ICWSM)

  • Crismore A, Markannen R, Steffensen M (1993) Metadiscourse in persuasive writing: a study of texts written by American and Finnish University students. Writ Commun 10(1):39–71

    Article  Google Scholar 

  • Cunha E, Magno G, Comarela G, Almeida V, Gonçalves MA, Benevenuto F (2011) Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In: Proceedings of the workshop on language in social media (LSM 2011), pp 58–65

  • Cunha E, Magno G, Almeida V, Gonçalves MA, Benevenuto F (2012) A gender based study of tagging behavior in twitter. In: Proceedings of the 23rd ACM conference on hypertext and social media. ACM, pp 323–324

  • Dodds PS, Danforth CM (2010) Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J Happiness Stud 11(4):441–456

    Article  Google Scholar 

  • Freeman LC (1979) Centrality in social networks: conceptual clarifications. Soc Netw 1(3):215–239

    Article  Google Scholar 

  • Georgakopoulou A (2013) Narrative analysis and computer-mediated communication. In: Herring S, Stein D, Virtanen T (eds) Pragmatics of computer-mediated communication. Mouton, Berlín, pp 695–715

    Google Scholar 

  • Gitlin T (1980) The whole world is watching: mass media in the making and unmaking of the new left.  University of California Press, Berkeley

    Google Scholar 

  • Gruzd A, Doiron S, Mai P (2011) Is happiness contagious online? A case of Twitter and the 2010 Winter Olympics. In: Proceedings of the 44th Hawaii international conference on system sciences. IEEE Computer Society, Washington, DC

  • Habert B, Nazarenko A, Salem A (1997) Les linguistiques de corpus. Armand colin, Paris

    Google Scholar 

  • Herrero R (2000) La terminología del análisis de redes: problemas de definición y de traducción. Política y sociedad 33:199–206

    MathSciNet  Google Scholar 

  • Heverin T, Zach L (2012) Use of microblogging for collective sense making during violent crises: a study of three campus shootings. J Am Soc Inform Sci Technol 63(1):34–47

    Article  Google Scholar 

  • Honeycutt C, Herring SC (2009) Beyond microblogging: conversation and collaboration via Twitter. 42nd Hawaii international conference on system sciences, Hawaii, pp 1–10

  • Hotho A, Nürnberger A, Paaß G (2005) A brief survey of text mining. Ldv Forum 20(1):19–62

    Google Scholar 

  • Hyland K, Tse P (2004) Metadiscourse in academic writing. Appl Linguist 25(2):156–177

    Article  Google Scholar 

  • Kramer ADI (2010) An unobtrusive behavioral model of “Gross National Happiness”. In: Proceedings of CHI 2010. ACM Press, New York, pp 287–290

  • Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on World Wide Web. ACM, pp 591–600

  • Lampos V, Lansdall-Welfare T, Araya R, Cristianini N (2013) Analysing mood patterns in the United Kingdom through Twitter content. arXiv preprint arXiv:1304.5507

  • Martínez V, González VM (2013) Sentiment characterization of an urban environment via Twitter. In: Urzaiz G, Ochoa SF, Bravo J, Chen LL, Oliveira J (eds) Ubiquitous computing and ambient intelligence. Context-awareness and context-driven interaction. Springer International Publishing, Cham, pp 394–397

    Chapter  Google Scholar 

  • Orihuela JL (2006) La revolución de los blogs. Cuando las bitácoras se convirtieron en el medio de comunicación de la gente. La Esfera de los Libros, Madrid, 20fbb06, ISBN: 84-9734-498-7, p 283

  • Peirce CS (1931) Collected writings (8 Vols.). Ed. Charles Hartshorne, Paul Weiss & Arthur W Burks. Harvard University Press, Cambridge

  • Poell T, de Kloet J, Zeng G (2014) Will the real Weibo please stand up? Chinese online contention and actor-network theory. Chin J Commun 7(1):1–18

    Article  Google Scholar 

  • Rodrigues BGA, Silva IS, Zaki M, Meira Jr W, Prates RO, Veloso A (2012) Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment. In: CHI'12 Extended Abstracts on Human Factors in Computing Systems, ACM, pp 2621–2626

  • Romero D, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. International world wide web conference (WWW 2011). Hyderabad, India

  • Russell A (2011) The Arab spring extra-national information flows, social media and the 2011 Egyptian uprising. Int J Commun 5:10

    Google Scholar 

  • Singh MP (2000) A social semantics for agent communication languages. In: Dignum F, Chaib-draa B, Weigand H (eds) Issues in agent communication.  Proceedings of the IJCAI-99 Workshop on Agent Communication Languages, Springer, Berlin, pp 31–45

    Google Scholar 

  • Wijaya V, Erwin A, Galinium M, Muliady W (2013) Automatic mood classification of Indonesian tweets using linguistic approach. 2013 international conference on information technology and electrical engineering (ICITEE), IEEE, pp 41–46

  • Zappavigna M (2011) Ambient affiliation: a linguistic perspective on Twitter. New Media Soc 13(5):788–806

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Tecnhology Department, Universidad Autónoma Metropolitana-Cuajimalpa, Mexico City, Mexico

    Rocío Abascal-Mena & Rose Lema

  2. Institut de Recherche en Informatique de Toulouse, Toulouse, France

    Florence Sèdes

Authors
  1. Rocío Abascal-Mena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rose Lema
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Florence Sèdes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rocío Abascal-Mena.

Appendix

Appendix

In this appendix is shown in detail the percentage of words obtained daily by each community. For example, for December 11 we have 20 communities where from the total number of concepts extracted for this day we got that the smallest community has 0.27 % of concepts while the biggest has 15.85 %.

Dec 6

Dec 10

Dec 11

Dec 12

Dec 13

Dec16

0.22

0.6

0.27

0.45

0.34

1.34

0.44

0.6

0.82

0.45

0.68

3.36

0.66

2.99

0.82

0.45

1.71

4.03

1.31

2.99

1.09

0.45

2.05

4.7

2.62

2.99

1.09

0.9

2.4

4.7

4.15

3.59

1.64

0.9

3.08

6.71

4.15

4.19

2.19

1.8

4.79

8.05

4.8

4.79

2.46

2.25

4.79

8.05

5.02

7.78

3.28

2.25

5.48

10.07

5.02

9.58

3.83

3.15

5.48

11.41

5.24

9.58

4.1

5.41

5.48

12.75

5.46

10.18

4.64

6.76

6.51

24.83

6.11

11.98

6.28

6.76

6.51

 

8.52

13.77

6.83

7.21

7.19

 

9.39

14.37

7.38

7.66

7.19

 

9.61

 

8.2

7.66

8.9

 

11.79

 

8.74

9.46

10.27

 

15.5

 

8.74

11.26

17.12

 
  

11.75

11.71

  
  

15.85

13.06

  
  1. Bold values show the communities that contain the hashtag #noalospluris

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abascal-Mena, R., Lema, R. & Sèdes, F. Detecting sociosemantic communities by applying social network analysis in tweets. Soc. Netw. Anal. Min. 5, 38 (2015). https://doi.org/10.1007/s13278-015-0280-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-015-0280-2

Keywords

Search

Navigation