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R İLE TWITTER VERİSİ ANALİZİ: VERİ TOPLAMA, SOSYAL AĞ ANALİZİ VE METİN ANALİZİ AŞAMALARI

Yıl 2023, Cilt: 13 Sayı: 1, 193 - 224, 01.01.2023

Naim Çınar

https://doi.org/10.7456//11301100/014
Cited By: 1

Öz

Enformasyon ve iletişim teknolojilerindeki hızlı gelişmeler çevrim içi davranışları anlamak için büyük veri setlerine erişme imkanını da beraberinde getirdi. İnternetin yaygınlaşmasıyla birlikte çok daha fazla sayıda birey, topluluk ve kurum sosyal medya platformlarında dijital sosyal etkileşimler kurmaya başladı. Bu dönüşüm sayesinde, yapılandırılmamış ya da yarı-yapılandırılmış yapıdaki ve çok zengin bir içerik çeşitliliğine sahip olan sosyal büyük veri (Big Social Data) her an birikerek artıyor. Dijital sosyal ağların, büyük oranda internet kullanıcıları tarafından oluşturulan içerik yığınını doğal ortamında gözlemleme imkanı sağlaması araştırmacılara çok çeşitli konularda çalışma gerçekleştirmek için ideal bir ortam sağlıyor. Bruns(2020: 65)’un da belirttiği gibi büyük sosyal veri üzerine yapılan çalışmalar aynı zamanda iletişim, kültürel çalışmalar, sosyal bilimler ve bilgisayar bilimi gibi çalışma alanlarının arasında yeni bağlantılar kuruyor. Büyük sosyal veri üzerine yapılan çalışmalarda, içeriğin yapısı, çeşitliliği, erişim imkanları ve karşılıklılık şartı aramayan kullanıcılar arası ilişki yapısı nedeniyle Twitter araştırma yapmak için ideal bir platform olarak ön plana çıkıyor. Bu çalışmada R programlama dili kullanılarak Twitter verisinin toplanması, verinin analize hazır hale getirilmesi, temizlenen veriye otomatik metin analizi ve sosyal ağ analizi yapılması adımlarını örnekler ile açıklayan bir rehber oluşturulması amaçlanmıştır.

Anahtar Kelimeler

Programlamalı Sosyal Bilimler Programlamalı İletişim Araştırmaları R Twitter Sosyal Ağ analizi Metin Analizi Computational Social Science Computational Communication Research Social Network Analysis Text Analysis.

Destekleyen Kurum

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Proje Numarası

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Teşekkür

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Kaynakça

  • Anber, H., Salah, A., ve Abd El-Aziz, A. A. (2016). A literature review on Twitter data analysis. International Journal of Computer and Electrical Engineering, 8(3), 241. https://doi.org/10.17706/ijcee.2016.8.3.241-249
  • Ashtiani, M., Mirzaie, M. ve Jafari, M. (2019). CINNA: an R/CRAN package to decipher central informative nodes in network analysis. Bioinformatics, 35(8), 1436-1437. https://doi.org/10.1093/bioinformatics/bty819
  • Barrie C, Ho J (2021). academictwitteR: an R package to access the Twitter Academic Research Product Track v2 API endpoint. Journal of Open Source Software, 6(62), 3272. https://github.com/cjbarrie/academictwitteR.
  • Bruns, A. (2020). Big social data approaches in Internet studies: The case of Twitter. Second international handbook of Internet research, 65-81. https://doi.org/10.1007/978-94-024-1555-1_3
  • Comeforo, K. ve Görgülü, B. (2022). Democratic possibilities of digital feminism. Democratic Frontiers: Algorithms and Society, (63-82), Routledge Focus. https://doi.org/10.4324/9781003173427-4
  • Chambers, J. M. (2020). S, R, and data science. Proceedings of the ACM on Programming Languages, 4(HOPL), 1-17. https://doi.org/10.1145/3386334
  • Çamurcu, M. H. (2022). İstanbul Sözleşmesi: Türkiye’de iç hukuka etkisi ve toplumun tepkisi. Ankara Barosu Dergisi. 79(4), 63-106. https://doi.org/10.30915/abd.1090725
  • de Nooy, W. (2009). Social network analysis, graph theoretical approaches to. Encyclopedia of complexity and system science, 8231-8245. https://doi.org/10.1007/978-0-387-30440-3_488
  • Fernández, J., Gómez, J. M. ve Martínez-Barco, P. (2014, October). A supervised approach for sentiment analysis using skipgrams. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC), 30-36.
  • Francechet, M. (2022). Network science. Universita Degli Studi di Udine. http://users.dimi.uniud.it/~massimo.franceschet/teaching/datascience/network
  • Grandjean, M. (2016). A social network analysis of Twitter: Mapping the digital humanities community. Cogent Arts & Humanities, 3(1), 1171458. https://doi.org/10.1080/23311983.2016.1171458
  • Hoffman, M. (2021). Methods for network analysis. Stanford University. https://bookdown.org/markhoff/social_network_analysis/
  • İstanbul sözleşmesi kadınları şiddetten koruyor. (2021, 22 Mart). İstanbul Barosu. https://www.istanbulbarosu.org.tr/HaberDetay.aspx?ID=16265
  • Maheswaran, R., Craigs, C., Read, S., Bath, P.A. ve Willett, P. (2009). A graph-theory method for pattern identification in geographical epidemiology-a preliminary application to deprivation and mortality. International Journal of Health Geographics, 8(1), 1-8. http://www.ij-healthgeographics.com/content/8/1/28 Nature. (t.y.). Computational social science. https://www.nature.com/collections/cadaddgige/
  • Olshannikova, E., Olsson, T., Huhtamäki, J. ve Kärkkäinen, H. (2017). Conceptualizing big social data. Journal of Big Data, 4(1), 1-19. https://doi.org/10.1186/s40537-017-0063-x
  • Orduz, J. C. (2018, 20 Aralık). Text mining, networks and visualization: Plebiscito tweets. Juanitorduz. https://juanitorduz.github.io/text-mining-networks-and-visualization-plebiscito-tweets/
  • Özbaş Anbarlı Z. ve Çınar, N. (2019). Sosyal ağlarda neler oluyor? Sosyal ağ analizi ile pazarlama iletişimi araştırmaları. İletişim Araştırmalarında Farklı Bakış Açıları, (37-55), Detay Yayıncılık.
  • Pelechrinis, K. (2015). TELCOM2125: Network science and analysis. [Powerpoint sunumu]. School of Information Sciences, University of Pittsburgh. https://sites.pitt.edu/~kpele/Materials15/module2.pdf
  • Phua, J., Jin, S. V., ve Kim, J. J. (2017). Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Computers in Human Behavior, 72, 115-122. https://doi.org/10.1016/j.chb.2017.02.041
  • Qiu, D., Li, B., ve Leung, H. (2016). Understanding the API usage in Java. Information and Software Technology, 73, 81-100. https://doi.org/10.1016/j.infsof.2016.01.011 Sagepub. (t.y.). Methods map: Computational Social Science. https://methods.sagepub.com/methods-map/computational-social-science
  • Segev, E. (Ed.). (2021). Semantic Network Analysis in Social Sciences. Routledge. https://doi.org/10.4324/9781003120100
  • Stackoverflow. (2020, Ocak 29). How do I clean Twitter data in R. https://stackoverflow.com/questions/31348453/ Steinert-Threlkeld ve Zachary C. (2018). Twitter as data. Cambridge University Press. https://doi.org/10.1017/9781108529327
  • Stylos, J., & Myers, B. (2007, September). Mapping the space of API design decisions. In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2007) (pp. 50-60). IEEE.
  • Twitter. (t.y.). Twitter API. https://developer.twitter.com/en/docs/twitter-api
  • Valente, T. W., Coronges, K., Lakon, C., ve Costenbader, E. (2008). How correlated are network centrality measures? Connect(Tor). 28(1), 16-26. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2875682/
  • van Gompel, M. ve van den Bosch, A. (2016). Efficient n-gram, skipgram and flexgram modelling with colibri core. Journal of Open Research Software, 4(1), e30. https://doi.org/10.5334/jors.105
  • Wasim, A. (2021, 18 Mayıs). Using Twitter as a data source an overview of social media research tools. The London School of Economics and Political Science (LSE), Impact of Social Sciences Blog. http://eprints.lse.ac.uk/111332/
  • Woods, C. (2021). Text and sentiment analysis in R. Chryswoods.com. https://chryswoods.com/text_analysis_r
  • Yu, J., 2021. Discovering Twitter through computational social science methods. Yayınlanmamış Doktora Tezi. Universitat Autonoma de Barcelona.

ANALYSIS OF TWITTER DATA WITH R: DATA COLLECTION, SOCIAL NETWORK ANALYSIS, AND TEXT ANALYSIS STAGES

Yıl 2023, Cilt: 13 Sayı: 1, 193 - 224, 01.01.2023

Naim Çınar

https://doi.org/10.7456//11301100/014
Cited By: 1

Öz

The constant development in information and communication technologies has enabled the opportunity to access and analyze large datasets to understand human behavior in the digital era. Following the widespread use of the internet, social media platforms have become the most popular environments where individuals, communities, and institutions interact. It has led to the emergence of extensive amounts of unstructured or semi-structured big social data that is very rich in content variety. Digital social networks provide an opportunity to observe the online behavior of users in a natural environment which makes it an ideal place for researchers to study a wide variety of topics. Bruns (2020:65) states that big social data approaches connect core disciplines that use big data methods -media, communication and cultural studies, the social sciences, and computer science. Twitter stands out as an ideal platform for research on big social data because of the structure and diversity of the content, data access opportunities, and the structure of the relations between users that does not require reciprocity. This study aims to provide a guideline for data collection from Twitter, data cleaning, social network analysis, and automated text analysis with R programming language.

Anahtar Kelimeler

Computational Social Science Computational Communication Research Social Network Analysis Text Analysis.

Proje Numarası

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Kaynakça

  • Anber, H., Salah, A., ve Abd El-Aziz, A. A. (2016). A literature review on Twitter data analysis. International Journal of Computer and Electrical Engineering, 8(3), 241. https://doi.org/10.17706/ijcee.2016.8.3.241-249
  • Ashtiani, M., Mirzaie, M. ve Jafari, M. (2019). CINNA: an R/CRAN package to decipher central informative nodes in network analysis. Bioinformatics, 35(8), 1436-1437. https://doi.org/10.1093/bioinformatics/bty819
  • Barrie C, Ho J (2021). academictwitteR: an R package to access the Twitter Academic Research Product Track v2 API endpoint. Journal of Open Source Software, 6(62), 3272. https://github.com/cjbarrie/academictwitteR.
  • Bruns, A. (2020). Big social data approaches in Internet studies: The case of Twitter. Second international handbook of Internet research, 65-81. https://doi.org/10.1007/978-94-024-1555-1_3
  • Comeforo, K. ve Görgülü, B. (2022). Democratic possibilities of digital feminism. Democratic Frontiers: Algorithms and Society, (63-82), Routledge Focus. https://doi.org/10.4324/9781003173427-4
  • Chambers, J. M. (2020). S, R, and data science. Proceedings of the ACM on Programming Languages, 4(HOPL), 1-17. https://doi.org/10.1145/3386334
  • Çamurcu, M. H. (2022). İstanbul Sözleşmesi: Türkiye’de iç hukuka etkisi ve toplumun tepkisi. Ankara Barosu Dergisi. 79(4), 63-106. https://doi.org/10.30915/abd.1090725
  • de Nooy, W. (2009). Social network analysis, graph theoretical approaches to. Encyclopedia of complexity and system science, 8231-8245. https://doi.org/10.1007/978-0-387-30440-3_488
  • Fernández, J., Gómez, J. M. ve Martínez-Barco, P. (2014, October). A supervised approach for sentiment analysis using skipgrams. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC), 30-36.
  • Francechet, M. (2022). Network science. Universita Degli Studi di Udine. http://users.dimi.uniud.it/~massimo.franceschet/teaching/datascience/network
  • Grandjean, M. (2016). A social network analysis of Twitter: Mapping the digital humanities community. Cogent Arts & Humanities, 3(1), 1171458. https://doi.org/10.1080/23311983.2016.1171458
  • Hoffman, M. (2021). Methods for network analysis. Stanford University. https://bookdown.org/markhoff/social_network_analysis/
  • İstanbul sözleşmesi kadınları şiddetten koruyor. (2021, 22 Mart). İstanbul Barosu. https://www.istanbulbarosu.org.tr/HaberDetay.aspx?ID=16265
  • Maheswaran, R., Craigs, C., Read, S., Bath, P.A. ve Willett, P. (2009). A graph-theory method for pattern identification in geographical epidemiology-a preliminary application to deprivation and mortality. International Journal of Health Geographics, 8(1), 1-8. http://www.ij-healthgeographics.com/content/8/1/28 Nature. (t.y.). Computational social science. https://www.nature.com/collections/cadaddgige/
  • Olshannikova, E., Olsson, T., Huhtamäki, J. ve Kärkkäinen, H. (2017). Conceptualizing big social data. Journal of Big Data, 4(1), 1-19. https://doi.org/10.1186/s40537-017-0063-x
  • Orduz, J. C. (2018, 20 Aralık). Text mining, networks and visualization: Plebiscito tweets. Juanitorduz. https://juanitorduz.github.io/text-mining-networks-and-visualization-plebiscito-tweets/
  • Özbaş Anbarlı Z. ve Çınar, N. (2019). Sosyal ağlarda neler oluyor? Sosyal ağ analizi ile pazarlama iletişimi araştırmaları. İletişim Araştırmalarında Farklı Bakış Açıları, (37-55), Detay Yayıncılık.
  • Pelechrinis, K. (2015). TELCOM2125: Network science and analysis. [Powerpoint sunumu]. School of Information Sciences, University of Pittsburgh. https://sites.pitt.edu/~kpele/Materials15/module2.pdf
  • Phua, J., Jin, S. V., ve Kim, J. J. (2017). Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Computers in Human Behavior, 72, 115-122. https://doi.org/10.1016/j.chb.2017.02.041
  • Qiu, D., Li, B., ve Leung, H. (2016). Understanding the API usage in Java. Information and Software Technology, 73, 81-100. https://doi.org/10.1016/j.infsof.2016.01.011 Sagepub. (t.y.). Methods map: Computational Social Science. https://methods.sagepub.com/methods-map/computational-social-science
  • Segev, E. (Ed.). (2021). Semantic Network Analysis in Social Sciences. Routledge. https://doi.org/10.4324/9781003120100
  • Stackoverflow. (2020, Ocak 29). How do I clean Twitter data in R. https://stackoverflow.com/questions/31348453/ Steinert-Threlkeld ve Zachary C. (2018). Twitter as data. Cambridge University Press. https://doi.org/10.1017/9781108529327
  • Stylos, J., & Myers, B. (2007, September). Mapping the space of API design decisions. In IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2007) (pp. 50-60). IEEE.
  • Twitter. (t.y.). Twitter API. https://developer.twitter.com/en/docs/twitter-api
  • Valente, T. W., Coronges, K., Lakon, C., ve Costenbader, E. (2008). How correlated are network centrality measures? Connect(Tor). 28(1), 16-26. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2875682/
  • van Gompel, M. ve van den Bosch, A. (2016). Efficient n-gram, skipgram and flexgram modelling with colibri core. Journal of Open Research Software, 4(1), e30. https://doi.org/10.5334/jors.105
  • Wasim, A. (2021, 18 Mayıs). Using Twitter as a data source an overview of social media research tools. The London School of Economics and Political Science (LSE), Impact of Social Sciences Blog. http://eprints.lse.ac.uk/111332/
  • Woods, C. (2021). Text and sentiment analysis in R. Chryswoods.com. https://chryswoods.com/text_analysis_r
  • Yu, J., 2021. Discovering Twitter through computational social science methods. Yayınlanmamış Doktora Tezi. Universitat Autonoma de Barcelona.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İletişim ve Medya Çalışmaları
Bölüm Makaleler
Yazarlar

Naim Çınar 0000-0002-1824-4076

Proje Numarası -
Yayımlanma Tarihi 1 Ocak 2023
Gönderilme Tarihi 21 Kasım 2022
Kabul Tarihi 22 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 1

Kaynak Göster

APA Çınar, N. (2023). R İLE TWITTER VERİSİ ANALİZİ: VERİ TOPLAMA, SOSYAL AĞ ANALİZİ VE METİN ANALİZİ AŞAMALARI. Turkish Online Journal of Design Art and Communication, 13(1), 193-224. https://doi.org/10.7456//11301100/014

Cited By

Metin ve konum bazlı sosyal medya analizleriyle Antarktika’yı haritalama yaklaşımı
Geomatik
https://doi.org/10.29128/geomatik.1417673
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