Social media applications, which developed rapidly with the Web 2.0 era, provided users with the opportunity to produce and share their own content in an easy and accessible way and to interact with the content produced by others. Twitter, a popular location based social network, has attracted the attention of various disciplines with user-generated content. When these contents are obtained together with their spatial information, they provide significant potential for geography research. A retrospective approach has been adopted in the method of this study, which aims to examine the types of spatial data on the Twitter network, how to access data and the importance of these data in geography research. Document review and use of open source code were also used within the method in accordance with the purpose. It has been determined that there are a total of three different geospatial metadata on Twitter and two different JSON objects where this data is made available for users. It has been observed that there are three different methods generally preferred to access relevant data and various research and projects using these methods. In general, the data available on the social network Twitter for thirteen different research topics associated with the science of geography provides significant data potential. In addition, it is obvious that the use of user-generated contents on social network Twitter and the application of related methods in geography research will contribute to data collection, analysis and visualization processes for research and diversifying the subjects present in researches.
Aghaei, S., Nematbakhsh, M. A. & Farsani, H. K. (2012). Evolution of the world wide web: from web 1.0 to
web 4.0. International Journal of Web & Semantic Technology, 3(1), 1-10.
Aljohani, N. R., Alahmari, S. A. & Aseere, A. M. (2011). An organized collaborative work using Twitter in flood
disaster. Presented to ACM Web Science Conference. Koblenz, Germany.
Ashktorab, Z., Brown, C., Nandi, M. & Culotta, A. (2014). Tweedr: mining Twitter to inform disaster response.
In S.R. Hiltz, M.S. Pfaff, L. Plotnick & A.C. Robinson, (Eds), 11th Proceedings of the international conference
on information systems for crisis response and management (pp. 359-363), Pennsylvania, USA: University
Park.
Bermingham, A. & Smeaton, A. (2011). On using Twitter to monitor political sentiment and predict election
results. In S. Bandyopadhyay & M. Okumura (Eds.), Proceedings of the workshop on sentiment analysis
where aI meets psychology (pp. 2-10). USA: Curran Associates. Inc.
Berners Lee, T. (1998). The World Wide Web: A very short personal history. Retrieved December 24, 2020,
from http://www.w3.org/People/Berners-Lee/ShortHistory.html
Bruns, A. & Liang, Y. E. (2012). Tools and methods for capturing Twitter data during natural disasters. First
Monday, 17(4), 1-8.
Campagna, M. (2016). Social media geographic information: why social is special when it goes spatial? In C.
Capineri, M. Haklay, H. Huang, V. Antoniou, J. Kettunen, F. Ostermann & R. Purves (Eds.), European
handbook of crowdsourced geographic information (pp. 45-54). London: Ubiquity Press.
Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z. & Soltani, K. (2015). A scalable framework for
spatiotemporal analysis of location-based social media data. Computers, Environment and Urban Systems,
51, 70-82.
Carr, C. T. & Hayes, R. A. (2015). Social media: defining, developing, and divining. Atlantic journal of
communication, 23(1), 46-65.
Chatfield, A. T. & Brajawidagda, U. (2013). Twitter early tsunami warning system: a case study in Indonesia's
natural disaster management. In H. Ralph & Jr. Sprague (Eds), 46th Hawaii international conference on
system sciences (pp. 2050-2060). Wailea: Maui, USA.
Cormode, G. & Krishnamurthy, B. (2008). Key differences between Web 1.0 and Web 2.0. First Monday,
13(6), 1-30.
De Longueville, B., Smith, R. & Luraschi, G. (2009). "OMG! From here, I can see the flames!": a case for
mining location based social networks to acquire spatio-temporal data on forrest fires. In X. Zou (Ed.),
Proceedings of the 2009 international workshop on location based social networks (pp.73-80). Seattle: ACM.
Earle, P. S., Bowden, D. C. & Guy, M. (2012). Twitter earthquake detection: earthquake monitoring in a social
world. Annals of Geophysics, 54(6), 708-715.
Flew, T., Bruns, A., Burgess, J., Crawford, K. & Shaw, F. (2013). Social media and its impact on crisis
communication: Case studies of Twitter use in emergency management in Australia and New Zealand.
Presented to Communication and Social Transformation, ICA Regional Conference. Shanghai, China.
Frias Martinez, V., Soto, V., Hohwald, H., & Frias Martinez, E. (2012). Characterizing urban landscapes using
geolocated tweets. In 2012 IEEE international conference on and IEEE international conference on social
computing (SocialCom) privacy, security, risk and trust (PASSAT) (pp. 239-248). IEEE Computer Society:
USA.
Geospatial metadata, (2021). Tweet geospatial metadata. Retrieved February 26, 2021, from
https://developer.twitter.com/en/docs/tutorials/tweet-geo-
metadata#:~:text=Twitter%20Places%20can%20be% 20thought,
the%20highest%20level%20of%20precision.
Ghosh, D. & Guha, R. (2013). What are we ‘tweeting’about obesity? Mapping tweets with topic modeling and
geographic information system. Cartography and geographic information science, 40(2), 90-102.
Gunawong, P. & Butakhieo, N. (2016). Social media in local administration: An empirical study of twitter use
in flood management. In N. Edelmann & P. Parycek (Eds.), Conference for e-democracy and open
government (CeDEM) (pp. 77-83). USA: IEEE Computer Society
Halliwell, J. (2020). Applying social media research methods in geography teaching: benefits and emerging
challenges? Journal of Geography, 119(3), 108-113.
Harrison, T. M. & Barthel, B. (2009). Wielding new media in Web 2.0: exploring the history of engagement
with the collaborative construction of media products. New media & society, 11(1-2), 155-178.
HealthMap, (2021). About healthmap, Retrieved February 21, 2021, from http://www.diseasedaily.org/about
Hundey, E. (2012). Social media as an educational tool in university level Geography. Teaching Innovation
Projects, 2(1), 1-11.
Imran, M., Castillo, C., Lucas, J., Meier, P. & Vieweg, S. (2014). AIDR: Artificial intelligence for disaster
response. In C. Chung (Ed.), Proceedings of the 23rd international conference on world wide web (pp. 159-
162). USA: Association for Computing Machinery.
Internetlivestats, (2021). Twitter usage statistics. Retrieved February 26, 2021, from
https://www.internetlivestats.com/twitter-statistics/
Jongman, B., Wagemaker, J., Romero, B. R. & De Perez, E. C. (2015). Early flood detection for rapid
humanitarian response: harnessing near real-time satellite and Twitter signals. ISPRS International Journal of
Geo-Information, 4(4), 2246-2266.
Kaigo, M. (2012). Social media usage during disasters and social capital: Twitter and the Great East Japan
earthquake. Keio Communication Review, 34(1), 19-35.
Kwon, H. Y. & Kang, Y. O. (2016). Risk analysis and visualization for detecting signs of flood disaster in
Twitter. Spatial information research, 24(2), 127-139.
Lassila, O. & Hendler, J. (2007). Embracing" Web 3.0". IEEE Internet Computing, 11(3), 90-93.
Lee, R. & Sumiya, K. (2010). Measuring geographical regularities of crowd behaiviors for Twitter-based geo-
social event detection. In X. Zhou & W. Chien-Lee (Eds.), GIS '10: 18th SIGSPATIAL international conference
on advances in geographic ınformation systems (pp. 1-10). USA: Association for Computing Machinery.
Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C. & Tapia, A. H. (2015). Twitter mining
for disaster response: a domain adaptation approach. In L. Palen, M. Büscher & T. Comes, A. Hughes (Eds.),
Proceedings of the ISCRAM 2015 conference. ISCRAM: Norway.
Location metadata, (2021). Data dictionary: standard v1.1 - Geo object. Retrieved February 05, 2021, from
https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/geo
Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S. & Danforth, C. M. (2013). The geography of happiness:
connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PloS one,
8(5), 1-15.
Murugesan, S. (2007). Understanding Web 2.0. IT professional, 9(4), 34-41.
O'Reilly, T. (2005). What is Web 2.0. Design patterns and business models for the next generation of
software. Retrieved January 02, 2021, from http://oreilly.com/web2/archive/what-is-web-20.html
Palen, L., Anderson, K. M., Gloria, M., Martin, J., Sicker, D. & Palmer, M. (2010b). A vision for technology-
mediated support for public participation & assistance in mass emergencies & disasters. In ACM-BCS '10:
proceedings of the 2010 ACM-BCS visions of computer science conference (pp. 1-12). UK: BCS Learning &
Development Ltd.
Palen, L., Starbird, K., Vieweg, S. & Hughes, A. (2010a). Twitter‐based information distribution during the
2009 Red River Valley flood threat. Bulletin of the American Society for Information Science and Technology,
36(5), 13-17.
Prakash, A. (2020). Top GitHub alternatives to host your open source projects. Retrieved March 06, 2021,
from https://itsfoss.com/github-alternatives/
Roick, O. & Heuser, S. (2013). Location based social networks-definition, current state of the art and
research agenda. Transactions in GIS, 17(5), 763-784.
Starbird, K. & Palen, L. (2010). Pass It On?: retweeting in mass emergency. In S. French, B. Tomaszewski &
C. Zobel (Eds.), 7th annual international ISCRAM conference, (pp. 1-10). Seattle, WA, USA: ISCRAM.
Takahashi, B., Tandoc Jr, E. C. & Carmichael, C. (2015). Communicating on Twitter during a disaster: an
analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior, 50, 392-398.
Tweet location, (2021a). Tweet location FAQs. Retrieved January 26, 2021, from
https://help.twitter.com/en/safety-and-security/tweet-location-settings
Tweet location, (2021b). How to add your location to a Tweet. Retrieved January 29, 2021, from
https://help.twitter.com/en/using-twitter/tweet-location
Tweet object, (2021). Data dictionary: standard v1.1 - Tweet object. Retrieved January 26, 2021, from
https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/tweet
Uchida, O., Kosugi, M., Endo, G., Funayama, T., Utsu, K., Tajima, S. & Yamamoto, Y. (2015). A real-time
disaster-related information sharing system based on the utilization of Twitter. In C. Merkle Westphall & D.
Roman (Eds.), The Fifth international conference on social media, technologies, communication, and
informatics (SOTICS 2015) (pp. 22-25). Spain: IARIA.
Vieweg, S., Hughes, A. L., Starbird, K. & Palen, L. (2010). Microblogging during two natural hazards events:
what twitter may contribute to situational awareness. In Proceedings of the SIGCHI conference on human
factors in computing systems (pp. 1079-1088). USA: Association for Computing Machinery.
Wahlster, W., Dengel, A., Telekom, D., Dengel, W., Dengler, C. D., Heckmann, D. & Sintek, M. (2006). Web
3.0: convergence of web 2.0 and the semantic web. In Technology radar feature paper edition (pp. 1-23).
Germany: Deutsche Telekom Laboratories.
Xu, Z., Liu, Y., Yen, N., Mei, L., Luo, X., Wei, X. & Hu, C. (2016). Crowdsourcing based description of urban
emergency events using social media big data. IEEE Transactions on Cloud Computing, 8(2), 387-397.
Zahra, K., Ostermann, F. O. & Purves, R. S. (2017). Geographic variability of Twitter usage characteristics
during disaster events. Geo-spatial information science, 20(3), 231-240.
Zheng, Y. (2011). Location-based social networks: Users. In Y. Zheng & X. Zhou (Eds.), Computing with
spatial trajectories (pp. 243-276). New York, USA: Springer.
Zheng, Y., Chen, Y., Xie, X., & Ma, W. Y. (2009). GeoLife2.0: a location-based social networking service. In
2009 tenth international conference on mobile data management: systems, services and middleware (pp.
357-358). USA: IEEE Computer Society.
METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER
Web 2.0 dönemi ile hızlı bir gelişim gösteren sosyal medya uygulamaları, kullanıcılarına kolay ve erişilebilir biçimde kendi içeriklerini üretme, paylaşma ve başkaları tarafından üretilen içeriklerle etkileşime geçebilme imkanı sağlamıştır. Popüler bir lokasyon bazlı sosyal ağ olan Twitter, içerisinde barındırdığı kullanıcı üretimli içerikler ile çeşitli disiplinlerin dikkatini çekmiştir. Bu içerikler mekânsal bilgileri ile birlikte elde edildiğinde, coğrafya bilimi araştırmaları için önemli bir potansiyel sağlamaktadır. İlgili ağ üzerinde yer alan mekânsal veri çeşitlerini, verilere nasıl erişilebileceğini ve bu verilerin coğrafya araştırmalarındaki önemini irdelemeyi amaçlayan bu araştırmanın yönteminde retrospektif bir yaklaşım benimsenmiştir. Doküman incelemesi ve açık kaynak kod kullanımı da amaca uygun olarak yöntem dâhilinde kullanılmıştır. Twitter üzerinde toplam üç farklı coğrafi-mekânsal meta veri ve bu verilerin kullanıcılara sunulduğu iki farklı JSON nesnesi olduğu belirlenmiştir. İlgili verilere ulaşabilmek için genel olarak tercih edilen üç farklı yöntem ve bu yöntemlerin kullanıldığı çeşitli araştırma ve projelerin var olduğu görülmüştür. Genel olarak, coğrafya bilimi ile ilişkili on üç farklı araştırma konusu için sosyal ağ Twitter üzerinde bulunan veriler önemli bir veri potansiyeli sağlamaktadır. Yine, Sosyal ağ Twitter üzerinde bulunan kullanıcı üretimli içeriklerin ve ilgili yöntemlerin coğrafya araştırmalarında kullanımının; araştırmalar için veri toplama, analiz etme ve görselleştirme süreçlerine katkı sağlayacağı, araştırmalarda ele alınan konuları çeşitlendireceği aşikârdır.
Aghaei, S., Nematbakhsh, M. A. & Farsani, H. K. (2012). Evolution of the world wide web: from web 1.0 to
web 4.0. International Journal of Web & Semantic Technology, 3(1), 1-10.
Aljohani, N. R., Alahmari, S. A. & Aseere, A. M. (2011). An organized collaborative work using Twitter in flood
disaster. Presented to ACM Web Science Conference. Koblenz, Germany.
Ashktorab, Z., Brown, C., Nandi, M. & Culotta, A. (2014). Tweedr: mining Twitter to inform disaster response.
In S.R. Hiltz, M.S. Pfaff, L. Plotnick & A.C. Robinson, (Eds), 11th Proceedings of the international conference
on information systems for crisis response and management (pp. 359-363), Pennsylvania, USA: University
Park.
Bermingham, A. & Smeaton, A. (2011). On using Twitter to monitor political sentiment and predict election
results. In S. Bandyopadhyay & M. Okumura (Eds.), Proceedings of the workshop on sentiment analysis
where aI meets psychology (pp. 2-10). USA: Curran Associates. Inc.
Berners Lee, T. (1998). The World Wide Web: A very short personal history. Retrieved December 24, 2020,
from http://www.w3.org/People/Berners-Lee/ShortHistory.html
Bruns, A. & Liang, Y. E. (2012). Tools and methods for capturing Twitter data during natural disasters. First
Monday, 17(4), 1-8.
Campagna, M. (2016). Social media geographic information: why social is special when it goes spatial? In C.
Capineri, M. Haklay, H. Huang, V. Antoniou, J. Kettunen, F. Ostermann & R. Purves (Eds.), European
handbook of crowdsourced geographic information (pp. 45-54). London: Ubiquity Press.
Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z. & Soltani, K. (2015). A scalable framework for
spatiotemporal analysis of location-based social media data. Computers, Environment and Urban Systems,
51, 70-82.
Carr, C. T. & Hayes, R. A. (2015). Social media: defining, developing, and divining. Atlantic journal of
communication, 23(1), 46-65.
Chatfield, A. T. & Brajawidagda, U. (2013). Twitter early tsunami warning system: a case study in Indonesia's
natural disaster management. In H. Ralph & Jr. Sprague (Eds), 46th Hawaii international conference on
system sciences (pp. 2050-2060). Wailea: Maui, USA.
Cormode, G. & Krishnamurthy, B. (2008). Key differences between Web 1.0 and Web 2.0. First Monday,
13(6), 1-30.
De Longueville, B., Smith, R. & Luraschi, G. (2009). "OMG! From here, I can see the flames!": a case for
mining location based social networks to acquire spatio-temporal data on forrest fires. In X. Zou (Ed.),
Proceedings of the 2009 international workshop on location based social networks (pp.73-80). Seattle: ACM.
Earle, P. S., Bowden, D. C. & Guy, M. (2012). Twitter earthquake detection: earthquake monitoring in a social
world. Annals of Geophysics, 54(6), 708-715.
Flew, T., Bruns, A., Burgess, J., Crawford, K. & Shaw, F. (2013). Social media and its impact on crisis
communication: Case studies of Twitter use in emergency management in Australia and New Zealand.
Presented to Communication and Social Transformation, ICA Regional Conference. Shanghai, China.
Frias Martinez, V., Soto, V., Hohwald, H., & Frias Martinez, E. (2012). Characterizing urban landscapes using
geolocated tweets. In 2012 IEEE international conference on and IEEE international conference on social
computing (SocialCom) privacy, security, risk and trust (PASSAT) (pp. 239-248). IEEE Computer Society:
USA.
Geospatial metadata, (2021). Tweet geospatial metadata. Retrieved February 26, 2021, from
https://developer.twitter.com/en/docs/tutorials/tweet-geo-
metadata#:~:text=Twitter%20Places%20can%20be% 20thought,
the%20highest%20level%20of%20precision.
Ghosh, D. & Guha, R. (2013). What are we ‘tweeting’about obesity? Mapping tweets with topic modeling and
geographic information system. Cartography and geographic information science, 40(2), 90-102.
Gunawong, P. & Butakhieo, N. (2016). Social media in local administration: An empirical study of twitter use
in flood management. In N. Edelmann & P. Parycek (Eds.), Conference for e-democracy and open
government (CeDEM) (pp. 77-83). USA: IEEE Computer Society
Halliwell, J. (2020). Applying social media research methods in geography teaching: benefits and emerging
challenges? Journal of Geography, 119(3), 108-113.
Harrison, T. M. & Barthel, B. (2009). Wielding new media in Web 2.0: exploring the history of engagement
with the collaborative construction of media products. New media & society, 11(1-2), 155-178.
HealthMap, (2021). About healthmap, Retrieved February 21, 2021, from http://www.diseasedaily.org/about
Hundey, E. (2012). Social media as an educational tool in university level Geography. Teaching Innovation
Projects, 2(1), 1-11.
Imran, M., Castillo, C., Lucas, J., Meier, P. & Vieweg, S. (2014). AIDR: Artificial intelligence for disaster
response. In C. Chung (Ed.), Proceedings of the 23rd international conference on world wide web (pp. 159-
162). USA: Association for Computing Machinery.
Internetlivestats, (2021). Twitter usage statistics. Retrieved February 26, 2021, from
https://www.internetlivestats.com/twitter-statistics/
Jongman, B., Wagemaker, J., Romero, B. R. & De Perez, E. C. (2015). Early flood detection for rapid
humanitarian response: harnessing near real-time satellite and Twitter signals. ISPRS International Journal of
Geo-Information, 4(4), 2246-2266.
Kaigo, M. (2012). Social media usage during disasters and social capital: Twitter and the Great East Japan
earthquake. Keio Communication Review, 34(1), 19-35.
Kwon, H. Y. & Kang, Y. O. (2016). Risk analysis and visualization for detecting signs of flood disaster in
Twitter. Spatial information research, 24(2), 127-139.
Lassila, O. & Hendler, J. (2007). Embracing" Web 3.0". IEEE Internet Computing, 11(3), 90-93.
Lee, R. & Sumiya, K. (2010). Measuring geographical regularities of crowd behaiviors for Twitter-based geo-
social event detection. In X. Zhou & W. Chien-Lee (Eds.), GIS '10: 18th SIGSPATIAL international conference
on advances in geographic ınformation systems (pp. 1-10). USA: Association for Computing Machinery.
Li, H., Guevara, N., Herndon, N., Caragea, D., Neppalli, K., Caragea, C. & Tapia, A. H. (2015). Twitter mining
for disaster response: a domain adaptation approach. In L. Palen, M. Büscher & T. Comes, A. Hughes (Eds.),
Proceedings of the ISCRAM 2015 conference. ISCRAM: Norway.
Location metadata, (2021). Data dictionary: standard v1.1 - Geo object. Retrieved February 05, 2021, from
https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/geo
Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S. & Danforth, C. M. (2013). The geography of happiness:
connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PloS one,
8(5), 1-15.
Murugesan, S. (2007). Understanding Web 2.0. IT professional, 9(4), 34-41.
O'Reilly, T. (2005). What is Web 2.0. Design patterns and business models for the next generation of
software. Retrieved January 02, 2021, from http://oreilly.com/web2/archive/what-is-web-20.html
Palen, L., Anderson, K. M., Gloria, M., Martin, J., Sicker, D. & Palmer, M. (2010b). A vision for technology-
mediated support for public participation & assistance in mass emergencies & disasters. In ACM-BCS '10:
proceedings of the 2010 ACM-BCS visions of computer science conference (pp. 1-12). UK: BCS Learning &
Development Ltd.
Palen, L., Starbird, K., Vieweg, S. & Hughes, A. (2010a). Twitter‐based information distribution during the
2009 Red River Valley flood threat. Bulletin of the American Society for Information Science and Technology,
36(5), 13-17.
Prakash, A. (2020). Top GitHub alternatives to host your open source projects. Retrieved March 06, 2021,
from https://itsfoss.com/github-alternatives/
Roick, O. & Heuser, S. (2013). Location based social networks-definition, current state of the art and
research agenda. Transactions in GIS, 17(5), 763-784.
Starbird, K. & Palen, L. (2010). Pass It On?: retweeting in mass emergency. In S. French, B. Tomaszewski &
C. Zobel (Eds.), 7th annual international ISCRAM conference, (pp. 1-10). Seattle, WA, USA: ISCRAM.
Takahashi, B., Tandoc Jr, E. C. & Carmichael, C. (2015). Communicating on Twitter during a disaster: an
analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior, 50, 392-398.
Tweet location, (2021a). Tweet location FAQs. Retrieved January 26, 2021, from
https://help.twitter.com/en/safety-and-security/tweet-location-settings
Tweet location, (2021b). How to add your location to a Tweet. Retrieved January 29, 2021, from
https://help.twitter.com/en/using-twitter/tweet-location
Tweet object, (2021). Data dictionary: standard v1.1 - Tweet object. Retrieved January 26, 2021, from
https://developer.twitter.com/en/docs/twitter-api/v1/data-dictionary/object-model/tweet
Uchida, O., Kosugi, M., Endo, G., Funayama, T., Utsu, K., Tajima, S. & Yamamoto, Y. (2015). A real-time
disaster-related information sharing system based on the utilization of Twitter. In C. Merkle Westphall & D.
Roman (Eds.), The Fifth international conference on social media, technologies, communication, and
informatics (SOTICS 2015) (pp. 22-25). Spain: IARIA.
Vieweg, S., Hughes, A. L., Starbird, K. & Palen, L. (2010). Microblogging during two natural hazards events:
what twitter may contribute to situational awareness. In Proceedings of the SIGCHI conference on human
factors in computing systems (pp. 1079-1088). USA: Association for Computing Machinery.
Wahlster, W., Dengel, A., Telekom, D., Dengel, W., Dengler, C. D., Heckmann, D. & Sintek, M. (2006). Web
3.0: convergence of web 2.0 and the semantic web. In Technology radar feature paper edition (pp. 1-23).
Germany: Deutsche Telekom Laboratories.
Xu, Z., Liu, Y., Yen, N., Mei, L., Luo, X., Wei, X. & Hu, C. (2016). Crowdsourcing based description of urban
emergency events using social media big data. IEEE Transactions on Cloud Computing, 8(2), 387-397.
Zahra, K., Ostermann, F. O. & Purves, R. S. (2017). Geographic variability of Twitter usage characteristics
during disaster events. Geo-spatial information science, 20(3), 231-240.
Zheng, Y. (2011). Location-based social networks: Users. In Y. Zheng & X. Zhou (Eds.), Computing with
spatial trajectories (pp. 243-276). New York, USA: Springer.
Zheng, Y., Chen, Y., Xie, X., & Ma, W. Y. (2009). GeoLife2.0: a location-based social networking service. In
2009 tenth international conference on mobile data management: systems, services and middleware (pp.
357-358). USA: IEEE Computer Society.
Oğlakcı, B., & Uzun, A. (2021). METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER. International Journal of Geography and Geography Education(44), 176-191. https://doi.org/10.32003/igge.927907
AMA
Oğlakcı B, Uzun A. METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER. IGGE. Temmuz 2021;(44):176-191. doi:10.32003/igge.927907
Chicago
Oğlakcı, Burak, ve Alper Uzun. “METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER”. International Journal of Geography and Geography Education, sy. 44 (Temmuz 2021): 176-91. https://doi.org/10.32003/igge.927907.
EndNote
Oğlakcı B, Uzun A (01 Temmuz 2021) METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER. International Journal of Geography and Geography Education 44 176–191.
IEEE
B. Oğlakcı ve A. Uzun, “METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER”, IGGE, sy. 44, ss. 176–191, Temmuz 2021, doi: 10.32003/igge.927907.
ISNAD
Oğlakcı, Burak - Uzun, Alper. “METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER”. International Journal of Geography and Geography Education 44 (Temmuz 2021), 176-191. https://doi.org/10.32003/igge.927907.
JAMA
Oğlakcı B, Uzun A. METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER. IGGE. 2021;:176–191.
MLA
Oğlakcı, Burak ve Alper Uzun. “METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER”. International Journal of Geography and Geography Education, sy. 44, 2021, ss. 176-91, doi:10.32003/igge.927907.
Vancouver
Oğlakcı B, Uzun A. METİNSEL MESAJDAN MEKÂNSAL BİLGİYE: COĞRAFYA ARAŞTIRMALARINDA VERİ KAYNAĞI OLARAK SOSYAL AĞ TWİTTER. IGGE. 2021(44):176-91.