Sosyal Medyanın Gönüllü Coğrafi Veri Olarak Kullanımı ve Sosyal Medya Verilerinden Coğrafya Sözlüğü Üretimi
Öz
Gelişen web ve akıllı mobil teknolojileri ile sosyal medya platformları yaygınlaşmıştır. Son 10 yılda bu platformlardaki aktif kullanıcı sayısının artması veri üretimine de yansımıştır. Sosyal medya platformları aracılığı ile üretilen mekansal veri doğrudan ya da dolaylı kullanımlarla afet yönetimi, pazarlama, politika gibi geniş çerçevede katkılar sunmaktadır. Bu veri geleneksek gönüllü coğrafi bilgi projelerinde üretilen verinin aksine yapılandırılmamış ve çoğunlukla belirli bir amaç için projelendirilmeden üretilen karma bir veridir. Bu nedenle veri üzerinde çalışılacak konuya göre metin analizleri ile filtreleme yapmanın yanında verinin mekansal tarafını ele almak için coğrafi etiketleme ve referanslama konusunda ön işleme yapmayı gerektirmektedir. Bu makalenin amacı, gönüllü coğrafi bilginin bir alt başlığı olan sosyal medya verilerinin mekansal veri olarak kullanımını değerlendirerek, metinlerden coğrafi bilgi çıkarımı yaklaşımlarını tanıtmaktadır. Coğrafi ayrıştırmada ihtiyaç duyulan coğrafya sözlüğü üretimi için bir metodoloji sunmaktadır. Sunulan metodoloji İstanbul ve Londra için üretilen tweetlerde test edilmiş ve ilgi noktalarının tespitinde özellikle bina bazında temsil edilen alanlar için başarı sağlamıştır. Bu çalışma, doğal dilden bağımsız ve coğrafi tekrarlılığa dayalı coğrafi veri elde etme metodolojisi ile literatüre katkı sağlamaktadır.
Anahtar Kelimeler
Sosyal Medya Mekansal Veri Madenciliği Gönüllü Coğrafi Bilgi Coğrafi Ayrıştırma Coğrafya Sözlüğü
Destekleyen Kurum
İstanbul Teknik Üniversitesi Bilimsel Araştırma Projeleri
Proje Numarası
MDK-2017-40569 40569
Teşekkür
Bu çalışma İstanbul Teknik Üniversitesi Bilimsel Araştırma Projeleri (BAP) programı kapsamında desteklenmiştir (Proje Kodu: MDK-2017-40569 40569).
Kaynakça
- Cribbin T., Barnett J., Brooker P., Basnayake H. 2015. The Chorus Project Tweet Catcher. TCD 1.3.1. http://chorusanalytics.co.uk/.
- D'Andrea E, Ducange P, Lazzerini B, Marcelloni F. 2015. Real-time detection of traffic from twitter stream analysis. Intelligent Transportation Systems, IEEE Transactions on.16:2269-2283.
- Gelernter J, Balaji S. 2013. An Algorithm for Local Geoparsing of Microtext. GeoInformatica., 17(4):635-67.
- Gentry J. 2016. R-Based Twitter Client. 1.1.9. https://cran.r-project.org/web/packages/twitteR/twitteR.pdf.
- Gong Y, Deng F, Sinnott RO. 2015. Identification of (near) Real-time Traffic Congestion in the Cities of Australia through Twitter.7-12.
- Goodchild MF. 2007. Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0. Proceedings of the International journal of spatial data infrastructures research. Citeseer.
- Gulnerman AG, Gengec NE, Karaman H. 2016. Review of Public Tweets Over Turkey Within a Pre-Determined Time. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.153-159.
- Hasby M, Khodra ML. Optimal path finding based on traffic information extraction from Twitter. Proceedings of the ICT for Smart Society (ICISS), 2013 International Conference on; 2013: IEEE.
- Hecht B, Hong L, Suh B, Chi EH. 2011. Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
- Hecht B, Stephens M. 2014. A tale of cities: Urban biases in volunteered geographic information. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media.
- Ishino A, Odawara S, Nanba H, Takezawa T. 2012. Extracting transportation information and traffic problems from tweets during a disaster. Proc IMMM.91-96.
- Keller M., Freifeld C.C., Brownstein J.S. 2009. Automated vocabulary discovery for geo-parsing online epidemic intelligence, BMC Bioinformatics, vol. 10, pp. 385.
- Kosala R, Adi E. 2012. Harvesting real time traffic information from Twitter. Procedia Engineering.50:1-11.
- Lin Y-R, Margolin D. 2014. The ripple of fear, sympathy and solidarity during the Boston bombings. EPJ Data Science.3:1-28.
- McCreadie R, Macdonald C, Ounis I, Osborne M, Petrovic S. Scalable distributed event detection for twitter. Proceedings of the Big Data, 2013 IEEE International Conference on; 2013: IEEE.
- Middleton S.E., Kordopatis-Zilos G., Papadopoulos S., and Kompatsiaris Y. 2018. Location Extraction from Social Media: Geoparsing, Location Disambiguation, and Geotagging. ACM Trans. Inf. Syst., vol. 36, no. 4, pp. 1-27.
- Moffitt J. Tweet Metadata Timeline 2017 [Available from: http://support.gnip.com/articles/tweet-timeline.html.
- Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes Twitter users: real-time event detection by social sensors. Proceedings of the Proceedings of the 19th international conference on World wide web; 2010: ACM.
- Schroeder P. 1996. Criteria for the Design of a GIS/2. Specialists' meeting for NCGIA Initiative 19. GIS and Society, Summer.
- Sieber R. 2006. Public participation geographic information systems: A literature review and framework. Annals of the Association of American Geographers.96:491-507.
- Signorini A, Segre AM, Polgreen PM. 2011. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS one.6:e19467.
- Tufekci Z, Wilson C. 2012. Social Media and the Decision to Participate in Political Protest: Observations From Tahrir Square. Journal of Communication.62:363-379.
- Turner A. 2006. Introduction to neogeography: O'Reilly.
- Unver A. 2017. What Twitter Can Tell Us about the Jerusalem Protests. The Washington Post, August.28:2017.
- Yavuz D.D., Abul O. 2016. Implicit Location Sharing Detection in Social Media Turkish Text Messaging. In International Workshop on Machine Learning, Optimization, and Big Data. Springer, Cham.pp. 341-352.
- İnternet kaynakları
- 1-https://www.statista.com/statistics/617136/digital-population-worldwide/, (30.08.2019)
- 2-https://developer.twitter.com/en/products/products-overview, (15.09.2019)
- 3-https://www.knime.com/blog/knime-twitter-nodes, (06.10.2018)
- 4-https://carto.com/connectors/twitter-maps/, (03.06.2017)
- 5-https://www.smrfoundation.org/nodexl/,(05.09.2019)
- 6-https://github.com/nagellette/geo-tweet-downloader, (10.05.2017)
- 7-https://socialmediadata.org/social-media-research-toolkit/, (10.10.2017)
- 8-https://www.podargos.com/,(10.10.2017)
- 9-https://support.gnip.com/sources/twitter/, (10.11.2019)
- 10-https://geoparser.io/, (10.12.2018)
Use of Social Media as a Volunteered Geographic Data and the Gazetteer Production from Social Media Data
Öz
Social media platforms became widespread thanks to the developments in web and smart mobile technologies. Produced data volume has tremendously increased with the growing number of active users in these platforms in the last decade. Spatial data generated through social media platforms, that is in-/directly produced, contribute to diverse topics such as disaster management, marketing, and policy. This data, unlike the general voluntary geographic information, is unstructured and undirected for a project or for a specific purpose. Therefore, it requires pre-processing and filtering for text analysis according to the subject to be studied, and evaluation for direct or indirect spatial data for geospatial analysis. The aim of this article is to introduce and discuss the use of social media data as a subtitle of voluntary geographic information over geo-parsing approaches. This article also presents a methodology for the production of a gazetteer, which is required for geo-parsing techniques. The proposed methodology in this study is tested with the tweets generated within Istanbul and London areas and it is succeeded especially in the detection of point of interest that is representing the buildings. This study contributes to the literature of geographic data retrieval with the methodology, which is independent of natural language and based on the geographic data repetitiveness.
Anahtar Kelimeler
Social Media Spatial Data Mining Volunteered Geographic Information Geo-parsing Gazetteer
Proje Numarası
MDK-2017-40569 40569
Kaynakça
- Cribbin T., Barnett J., Brooker P., Basnayake H. 2015. The Chorus Project Tweet Catcher. TCD 1.3.1. http://chorusanalytics.co.uk/.
- D'Andrea E, Ducange P, Lazzerini B, Marcelloni F. 2015. Real-time detection of traffic from twitter stream analysis. Intelligent Transportation Systems, IEEE Transactions on.16:2269-2283.
- Gelernter J, Balaji S. 2013. An Algorithm for Local Geoparsing of Microtext. GeoInformatica., 17(4):635-67.
- Gentry J. 2016. R-Based Twitter Client. 1.1.9. https://cran.r-project.org/web/packages/twitteR/twitteR.pdf.
- Gong Y, Deng F, Sinnott RO. 2015. Identification of (near) Real-time Traffic Congestion in the Cities of Australia through Twitter.7-12.
- Goodchild MF. 2007. Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0. Proceedings of the International journal of spatial data infrastructures research. Citeseer.
- Gulnerman AG, Gengec NE, Karaman H. 2016. Review of Public Tweets Over Turkey Within a Pre-Determined Time. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.153-159.
- Hasby M, Khodra ML. Optimal path finding based on traffic information extraction from Twitter. Proceedings of the ICT for Smart Society (ICISS), 2013 International Conference on; 2013: IEEE.
- Hecht B, Hong L, Suh B, Chi EH. 2011. Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
- Hecht B, Stephens M. 2014. A tale of cities: Urban biases in volunteered geographic information. Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media.
- Ishino A, Odawara S, Nanba H, Takezawa T. 2012. Extracting transportation information and traffic problems from tweets during a disaster. Proc IMMM.91-96.
- Keller M., Freifeld C.C., Brownstein J.S. 2009. Automated vocabulary discovery for geo-parsing online epidemic intelligence, BMC Bioinformatics, vol. 10, pp. 385.
- Kosala R, Adi E. 2012. Harvesting real time traffic information from Twitter. Procedia Engineering.50:1-11.
- Lin Y-R, Margolin D. 2014. The ripple of fear, sympathy and solidarity during the Boston bombings. EPJ Data Science.3:1-28.
- McCreadie R, Macdonald C, Ounis I, Osborne M, Petrovic S. Scalable distributed event detection for twitter. Proceedings of the Big Data, 2013 IEEE International Conference on; 2013: IEEE.
- Middleton S.E., Kordopatis-Zilos G., Papadopoulos S., and Kompatsiaris Y. 2018. Location Extraction from Social Media: Geoparsing, Location Disambiguation, and Geotagging. ACM Trans. Inf. Syst., vol. 36, no. 4, pp. 1-27.
- Moffitt J. Tweet Metadata Timeline 2017 [Available from: http://support.gnip.com/articles/tweet-timeline.html.
- Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes Twitter users: real-time event detection by social sensors. Proceedings of the Proceedings of the 19th international conference on World wide web; 2010: ACM.
- Schroeder P. 1996. Criteria for the Design of a GIS/2. Specialists' meeting for NCGIA Initiative 19. GIS and Society, Summer.
- Sieber R. 2006. Public participation geographic information systems: A literature review and framework. Annals of the Association of American Geographers.96:491-507.
- Signorini A, Segre AM, Polgreen PM. 2011. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS one.6:e19467.
- Tufekci Z, Wilson C. 2012. Social Media and the Decision to Participate in Political Protest: Observations From Tahrir Square. Journal of Communication.62:363-379.
- Turner A. 2006. Introduction to neogeography: O'Reilly.
- Unver A. 2017. What Twitter Can Tell Us about the Jerusalem Protests. The Washington Post, August.28:2017.
- Yavuz D.D., Abul O. 2016. Implicit Location Sharing Detection in Social Media Turkish Text Messaging. In International Workshop on Machine Learning, Optimization, and Big Data. Springer, Cham.pp. 341-352.
- İnternet kaynakları
- 1-https://www.statista.com/statistics/617136/digital-population-worldwide/, (30.08.2019)
- 2-https://developer.twitter.com/en/products/products-overview, (15.09.2019)
- 3-https://www.knime.com/blog/knime-twitter-nodes, (06.10.2018)
- 4-https://carto.com/connectors/twitter-maps/, (03.06.2017)
- 5-https://www.smrfoundation.org/nodexl/,(05.09.2019)
- 6-https://github.com/nagellette/geo-tweet-downloader, (10.05.2017)
- 7-https://socialmediadata.org/social-media-research-toolkit/, (10.10.2017)
- 8-https://www.podargos.com/,(10.10.2017)
- 9-https://support.gnip.com/sources/twitter/, (10.11.2019)
- 10-https://geoparser.io/, (10.12.2018)
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | MDK-2017-40569 40569 |
| Yayımlanma Tarihi | 20 Mayıs 2020 |
| Gönderilme Tarihi | 30 Aralık 2019 |
| Yayımlandığı Sayı | Yıl 2020 Cilt: 20 Sayı: 2 |
