Skip to main content
SpringerLink
Log in

Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions

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

Abstract

The information published by the millions of public social network users is an important source of knowledge that can be used in academic, socioeconomic or demographic studies (distribution of male and female population, age, marital status, birth), lifestyle analysis (interests, hobbies, social habits) or be used to study online behavior (time spent online, interaction with friends or discussion about brands, products or politics). This work uses a database of about 27 million Portuguese geolocated tweets, produced in Portugal by 97.8 K users during a 1-year period, to extract information about the behavior of the geolocated Portuguese Twitter community and show that with this information it is possible to extract overall indicators such as: the daily periods of increased activity per region; prediction of regions where the concentration of the population is higher or lower in certain periods of the year; how do regional habitants feel about life; or what is talked about in each region. We also analyze the behavior of the geolocated Portuguese Twitter users based on the tweeted contents, and find indications that their behavior differs in certain relevant aspect from other Twitter communities, hypothesizing that this is in part due to the abnormal high percentage of young teenagers in the community. Finally, we present a small case study on Portuguese tourism in the Algarve region. To the best of our knowledge, this work is the first study that shows geolocated Portuguese users’ behavior in Twitter focusing on geographic regional use.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. https://about.twitter.com/company, last accessed, February 5th, 2016.

  2. http://www.internetlivestats.com/twitter-statistics/, last accessed, February 5th, 2016.

  3. http://www.ctt.pt/feapl_2/app/restricted/postalCodeSearch/postalCodeDownloadFiles.jspx, last accessed November 15th, 2015.

  4. https://dre.pt/application/dir/pdf2sdip/2014/07/126000000/1728617289.pdf, last accessed November 15th, 2015.

  5. http://www.jn.pt/PaginaInicial/Nacional/Media/Interior.aspx?content_id=4730582. Last accessed November 15th, 2015.

  6. http://www.businessinsider.com/update-a-breakdown-of-the-demographics-for-each-of-the-different-social-networks-2015-6, last accessed February 6th, 2015.

  7. http://www.asourceofinspiration.com/2014/02/18/social-media-statistics-for-portugal/, last accessed February 6th, 2015.

  8. http://travelbi.turismodeportugal.pt/pt–pt/Documents/An%C3%A1lises/Alojamento/Turismo%20em%20N%C3%BAmeros%20-%202015.pdf, last accessed June 2106.

References

  • Blanford J, Huang Z, Savelyev A, MacEachren A (2015) Geo-located tweets. Enhancing mobility maps and capturing crossborder movement. PLoS ONE. doi:10.1371/journal.pone.0129202

    Google Scholar 

  • Bora S, Singh H, Sen A, Bagchi A, Singla P (2015) On the role of conductance, geography and topology in predicting hashtag virality. J Soc Netw Anal Min 5(1):1–15

    Article  Google Scholar 

  • Boyd D, Golder S, Lotan G (2010) Tweet, tweet, retweet: conversational aspects of retweeting on Twitter. In: Proceedings in system sciences (HICSS), pp 1–10

  • Brogueira G, Batista F, Carvalho JP, H. Moniz H (2014a) Portuguese geolocated tweets: an overview. In: Proceedings of the international conference on information systems and design of communication, ISDOC, pp 178–179. ACM

  • Brogueira G, Batista F, Carvalho JP, H. Moniz H (2014b) Expanding a database of Portuguese tweets. In: SLATE’14 3rd symposium on languages, applications and technologies, Jun. 2014, pp 275–282, Schloss Dagstuhl

  • Brogueira G, Batista F, Carvalho JP (2015) Sistema Inteligente de Recolha, Armazenamento e Visualização de Informação proveniente do Twitter. In: 15th Conferência da Associação Portuguesa de Sistemas de Informação, CAPSI’2015, Lisboa

  • Brogueira G, Batista F, Carvalho JP (2016) A smart system for Twitter corpus collection, management and visualization. Int J Technol Hum Interact. Accepted for publication, IGI Global, 2016

  • Burnap P, Williams M, Sloan L, Rana O, Housley W, Edwards A, Knight V, Procter R, Voss A (2014) Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. J Soc Netw Anal Min 4(1):1–14

    Google Scholar 

  • Chandra S, Khan L, Muhaya FB (2011) Estimating Twitter user location using social interactions—a content based approach. In: Proceedings—2011 IEEE international conference on privacy, security, risk and trust and IEEE international conference on social computing, PASSAT/SocialCom 2011, pp 838–843

  • Chang J, Sun E (2011) Location 3: how users share and respond to location-based data on social networking sites. In: Proceedings of the 5th international conference on weblogs and social media (ICWSM’11). AAAI Press, pp 74–80

  • Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM ‘10). ACM, pp 759–768

  • Culotta A (2014) Estimating county health statistics with Twitter. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 1335–1344

  • Culotta A, Ravi N, Cutler J (2015) Predicting the demographics of Twitter users from social evidence using website traffic data. In: 29th AAAI conference on artificial intelligence (AAAI-15)

  • Dodds P, Harris K, Kloumann I, Bliss C, Danforth C (2011) Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter. PLoS ONE. doi:10.1371/journal.pone.0026752

    Google Scholar 

  • Edwards J (2014) These maps show that android is for people with less money. http://www.businessinsider.com/android-is-for-poor-people-maps-2014-4. Accessed 12 Nov 2015

  • Eisenstein J, O’Connor B, Smith N, Xing E (2010) A latent variable model for geographic lexical variation. In: Proceedings of the 2010 conference on empirical methods in natural language processing (EMNLP’10). Association for Computational Linguistics, pp 1277–1287

  • Hecht B, Hong L, Suh B, Chi E (2011) Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI ‘11). ACM, pp 237–246

  • Honeycutt C, Herring S (2009) Beyond microblogging: conversation and collaboration via Twitter. In: Proceedings of the forty-second Hawaii international conference on system sciences (HICSS-42). IEEE Press, Los Alamitos, pp 1–10

  • Hong L, Convertino G, Chi EH (2011) Language matters in Twitter: a large scale study. In: Fifth international AAAI conference on weblogs and social media

  • Housley W, Procter R, Edwards A, Burnap P, Williams M, Sloan L, Rana O, Morgan J, Voss A, Greenhill A (2014) Big and broad social data and the sociological imagination: a collaborative response. Big Data Soc 1(2):1–15

    Article  Google Scholar 

  • Instituto Nacional de Estatística (2011) Censos 2011 Resultados Definitivos—Portugal

  • Java A, Song X, Finn T, Tseng B (2006) Why we Twitter: understanding microblogging usage and communities. Joint 9th WEBKDD and 1st SNA-KDD Workshop ‘07, San Jose, CA

  • Kalarikkal S, Remya PC (2015) Sentiment analysis and dataset collection: a comparative study. In: Advance computing conference (IACC), IEEE international, pp 519–524

  • Kim T, Huerta-Canepa G, Park J, Hyun SJ, Lee D (2011) What’s happening: finding spontaneous user clusters nearby using Twitter. In: IEEE international conference on privacy, security, risk, and trust, and IEEE international conference on social computing

  • Kim H, Lee S, Kyeong S (2013) Discovering hot topics using Twitter streaming data. In: IEEE/ACM international conference on advances in social networks analysis and mining

  • Kumar S, Morstatter F, Liu H (2014) Twitter data analytics. Springer, New York

    Book  Google Scholar 

  • Mahmud J, Nichols J, Drews C (2014) Home location identification of Twitter users. ACM Trans Intell Syst Technol 5(3):47

    Article  Google Scholar 

  • Malik M, Lamba H, Nakos C, Pfeffer J (2015) Population bias in geotagged tweets. In: 9th International AAAI conference on web and social media research. The AAAI Press, pp 18–27

  • Manfredini F, Tagliolato P, Rosa CD (2011) Monitoring temporary populations through cellular core network data. In: Computational science and its applications—ICCSA 2011. Lecture notes in computer science, vol 6783. Springer, Berlin Heidelberg, pp 151–161

  • Mittal A, Goel A (2011) Stock prediction using Twitter sentiment analysis. Standford University, CS229 (http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.4517&rep=rep1&type=pdf). Accessed 16 Nov 2015

  • Morstatter F, Pfeffer J, Liu H, Carley K (2013) Is the sample good enough? comparing data from twitter’s streaming API with Twitter’s firehose. In: International AAAI conference on weblogs and social media

  • Rill S, Reinel D, Scheidt J, Zicari RV (2014) PoliTwi: early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowl-Based Syst 69:24–33

    Article  Google Scholar 

  • Santos JC, Matos S (2013) Predicting flu incidence from Portuguese tweets. In: International work-conference on bioinformatics and biomedical engineering—IWBBIO, pp 11–18

  • Saravanan M, Sundar D, Kumaresh VS (2013) Probing of geospatial stream data to report disorientation. In: IEEE recent advances in intelligent computational systems (RAICS)

  • Widener MJ, Li W (2014) Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Appl Geogr 54:189–197

    Article  Google Scholar 

  • Zagheni E, Garimella K, State B, Weber I (2014) Inferring international and internal migration patterns from Twitter data. In: Proceedings of the 23rd international conference on WWW’14 companion, Seoul, Korea

Download references

Acknowledgments

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) under project PTDC/IVC-ESCT/4919/2012 (MISNIS) and funds with reference UID/CEC/50021/2013.

Author information

Authors and Affiliations

  1. INESC-ID, Lisbon, Portugal

    Gaspar Brogueira, Fernando Batista & Joao Paulo Carvalho

  2. ISCTE-IUL – Instituto Universitário de Lisboa, Lisbon, Portugal

    Gaspar Brogueira & Fernando Batista

  3. Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Joao Paulo Carvalho

Authors
  1. Gaspar Brogueira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Batista
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joao Paulo Carvalho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joao Paulo Carvalho.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brogueira, G., Batista, F. & Carvalho, J.P. Using geolocated tweets for characterization of Twitter in Portugal and the Portuguese administrative regions. Soc. Netw. Anal. Min. 6, 37 (2016). https://doi.org/10.1007/s13278-016-0347-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-016-0347-8

Keywords

Search

Navigation