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Covid-19 ile İlişkili Türkçe Sosyal Medya Paylaşımlarının İçerik Analizi

Year 2021, Volume: 11 Issue: 3, 113 - 137, 15.09.2021

M. Fevzi Esen

Abstract

Günümüzde Covid-19 sebebiyle sosyal medya platformları aracılığıyla gerçekleştirilen paylaşımlarda dikkat çekici bir artış yaşanmaktadır. Kullanıcılar, sosyal medyada birçok konu hakkında kısa süreler içerisinde ciddi sayıda paylaşımlarda bulunmakta ve eş zamanlı olarak çok sayıda kullanıcıyla etkileşim sağlamaktadır. Bu çalışmada, Covid-19 konusunda sosyal medya aracılığıyla yayılan bilgi içeriğinin tespit edilmesi ve Covid-19 ile ilgili yapılan paylaşımlarda ön plana çıkan konuların keşfedilmesi amaçlanmıştır. Bu doğrultuda 01.03.2020-01.12.2020 tarihleri arasında Türkçe atılan 17.542 tweet ele alınmıştır. Sosyal medya içeriğinin analizinde saklı anlamsal dizinleme yöntemi, tweetler içerisindeki ilişkilerin ve etkileşimlerin tespit edilmesinde ise ağ analizi gerçekleştirilmiştir. Çalışma sonucunda, “yasak, tedbir, karantina, vaka” konularında sıkça paylaşım yapıldığı, söz konusu kelimelerle gerçekleştirilen iletişimin sık olduğu ve bilgi alışverişinin bu ağlarda hızlı olduğu sonucuna ulaşılmıştır. Ayrıca “temizlik, maske, mesafe” ile ilişkili paylaşımların “tedbir, kural, vaka, yasak” ifade eden paylaşımlara oranla daha az gerçekleştirilmiş olduğu tespit edilmiştir. Paylaşımlarda ayrıca “#evdekal, #evdehayatvar, #birliktebaşaracağız” gibi sosyal propaganda içeriklerine ilişkin beğeni ve retweet sayısının düşük olduğu, söz konusu terimlerin anlamsal bir dizede bulunmadığı görülmüştür. Bu durum, salgın yönetiminde sosyal propagandanın etkisinin sınırlı kaldığına işaret etmektedir. Sonuç olarak sosyal medya paylaşımlarında ön plana çıkan konuların ve paylaşıldıkları sosyal ağların karakteristiklerinin tespit edilmesi, pandeminin kontrolü ve yayılımının önlenmesine yönelik olarak karar vericilerin uygun politikalar belirlemelerine yardımcı olacaktır.

Keywords

Covid-19 sosyal medya analizi metin madenciliği sosyal propaganda içerik analizi.

References

  • Abd-Alrazaq, A., Alhuwail, D., Househ, M. ve Hamdi, Z. S. (2020). Top concerns of Tweeters during the COVID-19 pandemic: Infoveillance study. J Med Internet Res, 22(4), e19016.
  • Aiello, A. E., Renson, A. ve Zivich, P. N. (2020). Social media-and internet-based disease surveillance for public health. Annu Rev Public Health, 41, 101-118.
  • Ateş, N. B. ve Baran, S. (2020). Kriz iletişiminde sosyal medyanın etkin kullanımı: Covid-19 (Koronavirüs) salgınına yönelik Twitter analizi. Kocaeli Üniversitesi İletişim Fakültesi Araştırma Dergisi, 16, 66-99.
  • Bardus, M., El Rassi, R., Chahrour, M., Akl, E. W., Raslan, A. S. … Akl, E. A. (2020). The use of social media to increase the impact of health research: Systematic review. J Med Internet Res., 22(7), 1-21.
  • Basch, C. H., Hillyer, G. C., Meleo-Erwin, Z. C., Jaime, C., Mohlman, J. ve Basch, C. E. (2020). Preventive behaviors conveyed on YouTube to mitigate transmission of COVID-19: Cross-sectional study. JMIR Public Health Surveill, 6(2), e18807.
  • Belt, T., Berben, S., Samsom, M., Engelen, L. ve Schoonhoven, L. (2012). Use of social media by Western European hospitals: Longitudinal study. Journal of Medical Internet Research, 14, 1-9.
  • Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A. ve Porcelli, F. (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci, 117(27): 15530–15535.
  • BTK. (2021). Elektronik haberleşme sektöründe teknolojik gelişmeler ve eğilimler. Bilgi Teknolojileri ve İletişim Kurumu Araştırma Raporu. https://www.btk.gov.tr/arastirma-raporlari adresinden erişilmiştir.
  • Centers for Disease Control and Prevention. (2020). Health communication strategies and resources. https:// npin.cdc.gov/pages/health-communication-strategies adresinden erişilmiştir.
  • Chan, A. K. M., Nickson, C. P., Rudolph, J. W., Lee, A. ve Joynt, G. M. (2020). Social media for rapid knowledge dissemination: early experience from the COVID‐19 pandemic. Anaesthesia, 75: 1579-1582.
  • Chandrasekaran, R., Mehta, V., Valkunde, T. ve Moustakas, E. (2020). Topics, trends, and sentiments of tweets about the COVID-19 Pandemic: Temporal infoveillance study. J Med Internet Res, 22(10), e22624.
  • Chatfield, A. T., Scholl, H. J. J. ve Brajawidagda, U. (2013). Tsunami early warnings via twitter in government: Net-savvy citizens’ co-production of time-critical public information services. Govern Inf Quart, 30(4), 377-386.
  • Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L. … Scala, A. (2020). The COVID-19 social media infodemic. Sci Rep, 10, 16598.
  • Datare. (2021). Global social media overview. https://datareportal.com/social-media-users adresinden erişilmiştir.
  • Desai, T., Shariff, A., Shariff, A., Kats, M., Fang, X., Christiano, C. ve Ferris, M. (2012). Tweeting the meeting: An in-depth analysis of Twitter activity at Kidney Week 2011. PLoS One, 7(7), e40253.
  • DSÖ. (2020). Health promotion. Erişim adresi http://www.who.int/topics/ health_promotion/en/
  • El-Awaisi, A., O’Carroll, V., Koraysh, S., Koummich, S. ve Huber, M. (2020). Perceptions of who is in the healthcare team? A content analysis of social media posts during COVID-19 pandemic. Journal of Interprofessional Care, 34(5), 622-632.
  • Gao, X. (2018). Networked co-production of 311 services: investigating the use of Twitter in five US cities. Int. J. Public Adm. 41(9), 712-724.
  • García, G.M., Haboud, M., Howard, R., Manresa, A. ve Zurita, J. (2020). Miscommunication in the COVID‐19
  • Garcia, K. ve Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057.
  • Gefen, D., Endicott, J. E., Fresneda, J. E., Miller, J. ve Larsen, K. R. (2017). A guide to text analysis with latent semantic analysis in R with annotated code: Studying online reviews and the stack exchange community. Communications of the Association for Information Systems, 41, 450-496.
  • Gemlik, N., Pektaş, A. ve Arslanoğlu, A. (2021). COVİD-19 salgını sürecinde Twitter haberciliği üzerine nitel bir araştırma. Sağlık Profesyonelleri Araştırma Dergisi, 3(1), 15-24.
  • Goel, A. ve Gupta, L. (2020). Social media in the times of COVID-19. Journal of Clinical Rheumatology: practical Reports on Rheumatic & Musculoskeletal Diseases, 26(6), 220–223.
  • Han, X., Wang, J., Zhang, M. ve Wang, X. (2020). Using social media to mine and analyze public opinion related to COVID-19 in China. Int J Environ Res Public Health, 17(8), 2788.
  • Heldman, A., Schindelar, J. ve Weaver, J. (2013). Social media engagement and public health communication: Implications for public health organizations being truly “social”. Public Health Reviews, 35(13), 1-18.
  • Hocberg, I., Allon, R. ve Yom-Tov, E. (2020). Assessment of the frequency of online searches for symptoms before diagnosis: analysis of archival data. J Med Internet Res, 22(3), 1-7.
  • Imran, M., Ofli, F., Caragea, D. ve Torralba, A. (2020). Using AI and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Inf Process Manag, 57(5), 102261.
  • Islam, M. S., Sarkar, T. ve Khan, S. H. (2020). COVID-19-related infodemic and its impact on public health: A global social media analysis. Am J Trop Med Hyg., 103(4), 1621-1629.
  • Keskin, E. ve Mendeş, M. (2021). COVID-19 pandemisi ile ilgili Twitter mesajlarının metin madenciliği tekniği ile değerlendirilmesi. Turkiye Klinikleri J Biostat, 13(1), 82-90.
  • Kotsenas, A. L., Arce, M., Aase, L., Farris, K., Timimi, C. Y. ve Wald, J. T. (2018). The strategic imperative for the use of social media in health care. Journal of the American College of Radiology, 15(1B), 155-161.
  • Kouzy, R., Abi, Jaoude J. ve Kraitem, A. (2020). Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter. Cureus, 12(3), e7255.
  • Lamsal, R. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Appl Intell, 51, 2790-2804.
  • Landwehr, P. M., Wei, W., Kowalchuck, M. ve Carley, K. M. (2016). Using tweets to support disaster planning, warning and response. Saf Sci, 90, 33-47.
  • Landauer, T. K., McNamara, D. S., Dennis, S. ve Kintsch, W. (2007). Handbook of latent semantic analysis. Lawrence Erlbaum Associates Publishers.
  • Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P. ve Chen, H. (2020). Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data. Euro Surveill., 25, 2000199.
  • Lu, Y. ve Zhang, L. (2020). Social media WeChat infers the development trend of COVID-19. J Infect., 81: e82-e83.
  • Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R. ve Yang, Y. (2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends. JMIR Public Health and Surveillance, 6(2), e19447.
  • Malecki, K., Keating, J. A. ve Safdar, N. (2021). Crisis Communication and public perception of COVID-19 risk in the era of social media. Clinical Infectious Diseases, 72(4), 697-702.
  • Muniz-Rodriguez, K., Ofori, S. K., Bayliss, L. C., Schwind, J. S., Diallo, K., Liu, M., … Fung, I. C.-H. (2020). Social media use in emergency response to natural disasters: A systematic review with a public health perspective. Disaster Medicine and Public Health Preparedness, 14(1), 139-149.
  • Niknam, F., Samadbeik, M., Fatehi, F., Shirdel, M., Rezazadeh, M. ve Bastani, P. (2020). COVID-19 on Instagram: A content analysis of selected accounts. Health Policy and Technology, 10(1): 165-173.
  • Oberlo. (2021). Twitter statistics. https://www.oberlo.com/blog/twitter-statistics adresinden erişilmiştir.
  • Padilla, D. A. ve Tortolero, L. (2020). Social media influence in the COVID-19 Pandemic. Int. Braz Jurol, 46(1), 120-124.
  • Park, H. W., Park, S. ve Chong, M. (2020). Conversations and medical news frames on Twitter: Infodemiological study on COVID-19 in South Korea. J Med Internet Res, 22(5), e18897.
  • Perez, S. (2020). Twitter has a record-breaking week as users looked for news of protests and COVID-19. TechCrunch. https://techcrunch.com/2020/06/04/twitter-has-a-record-breaking-week-as-users looked-for-news-ofprotests-and-covid-19/ adresinden erişilmiştir.
  • Petersen, K. ve Gerken, J. M. (2021). #Covid-19: An exploratory investigation of hashtag usage on Twitter. Health Policy, 125(4), 541-547.
  • Riquelme, F. ve González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Journal of Information Processing and Management, 52, 1-33.
  • Shannon, S. ve Kent, N. (2020). 8 charts on internet use around the world as countries grapple with COVID-19 Internet. Pew Research Center. https://www.pewresearch.org/fact-tank/2020/04/02/8-charts-on-internetuse-around-the-world-as-countries-grapple-with-covid-19/ adresinden erişilmiştir.
  • Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L. ve Merchant, R. M. (2017). Twitter as a tool for health research: A systematic review. Am J Public Health, 107(1), e1-e8.
  • Wasserman, S. ve Faust, K. (2005). Social network analysis: Methods and applications. New York, Cambridge University Press.
  • Wright, K. B., Sparks, L. ve O’Hair, H. D. (2013). Health communication in the 21st century. UK: Wiley-Blackwell, UK.
  • Zahra, K., Imran, M. ve Ostermann, F. O. (2020). Automatic identification of eyewitness messages on twitter during disasters. Inf Process Manag, 57(1), 102107.
  • Zhu, H., Wu, H., Cao, J., Fu, G. ve Li, H. (2018). Information dissemination model for social media with constant updates. Physica A: Statistical Mechanics and its Applications, 502, 469-482.

The Content Analysis of Social Media Shares in Turkish Related to COVID-19

Year 2021, Volume: 11 Issue: 3, 113 - 137, 15.09.2021

M. Fevzi Esen

Abstract

A remarkable increase has currently been happening in social media platform content related to COVID-19. Users have created large volumes of content on various topics over a short time, interacting with people in real-time. This also has transformed social media into an indispensable information source for any crisis. This study aims to explore the information content on COVID-19 disseminated through social media and to discover prominent topics in shares on COVID-19. In this regard, we have retrieved 17,542 tweets shared in Turkish. A content analysis of social media shares has been carried out, with latent semantic indexing and network analyses being performed to detect the relationships and interactions among shares. As a result, the most shared topics have been concluded to be on yasak [lockdown], tedbir [precaution], karantina [quarantine], and vaka [case], with communication being frequently passed using this semantic string and information exchanges being faster within the network. In addition, shares related to hygiene, masks, and distancing were determined to have occurred less than shares related to precautions, rules, cases, and lockdowns. The number of likes and retweets for content with social propaganda such as #evdekal [stayathome], #evdehayatvar [lifeathome], and #birliktebaşaracağız [togetherwesucceed] were low and not found in a semantic string. This suggests social propaganda through social media to have had a limited impact on epidemic management. In conclusion, identifying the prominent issues in social media posts and the characteristics of social media networks will help decision-makers determine appropriate policies for controlling and preventing the pandemic’s spread.

Keywords

COVID-19 social media analysis text mining social propaganda content analysis

References

  • Abd-Alrazaq, A., Alhuwail, D., Househ, M. ve Hamdi, Z. S. (2020). Top concerns of Tweeters during the COVID-19 pandemic: Infoveillance study. J Med Internet Res, 22(4), e19016.
  • Aiello, A. E., Renson, A. ve Zivich, P. N. (2020). Social media-and internet-based disease surveillance for public health. Annu Rev Public Health, 41, 101-118.
  • Ateş, N. B. ve Baran, S. (2020). Kriz iletişiminde sosyal medyanın etkin kullanımı: Covid-19 (Koronavirüs) salgınına yönelik Twitter analizi. Kocaeli Üniversitesi İletişim Fakültesi Araştırma Dergisi, 16, 66-99.
  • Bardus, M., El Rassi, R., Chahrour, M., Akl, E. W., Raslan, A. S. … Akl, E. A. (2020). The use of social media to increase the impact of health research: Systematic review. J Med Internet Res., 22(7), 1-21.
  • Basch, C. H., Hillyer, G. C., Meleo-Erwin, Z. C., Jaime, C., Mohlman, J. ve Basch, C. E. (2020). Preventive behaviors conveyed on YouTube to mitigate transmission of COVID-19: Cross-sectional study. JMIR Public Health Surveill, 6(2), e18807.
  • Belt, T., Berben, S., Samsom, M., Engelen, L. ve Schoonhoven, L. (2012). Use of social media by Western European hospitals: Longitudinal study. Journal of Medical Internet Research, 14, 1-9.
  • Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A. ve Porcelli, F. (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci, 117(27): 15530–15535.
  • BTK. (2021). Elektronik haberleşme sektöründe teknolojik gelişmeler ve eğilimler. Bilgi Teknolojileri ve İletişim Kurumu Araştırma Raporu. https://www.btk.gov.tr/arastirma-raporlari adresinden erişilmiştir.
  • Centers for Disease Control and Prevention. (2020). Health communication strategies and resources. https:// npin.cdc.gov/pages/health-communication-strategies adresinden erişilmiştir.
  • Chan, A. K. M., Nickson, C. P., Rudolph, J. W., Lee, A. ve Joynt, G. M. (2020). Social media for rapid knowledge dissemination: early experience from the COVID‐19 pandemic. Anaesthesia, 75: 1579-1582.
  • Chandrasekaran, R., Mehta, V., Valkunde, T. ve Moustakas, E. (2020). Topics, trends, and sentiments of tweets about the COVID-19 Pandemic: Temporal infoveillance study. J Med Internet Res, 22(10), e22624.
  • Chatfield, A. T., Scholl, H. J. J. ve Brajawidagda, U. (2013). Tsunami early warnings via twitter in government: Net-savvy citizens’ co-production of time-critical public information services. Govern Inf Quart, 30(4), 377-386.
  • Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L. … Scala, A. (2020). The COVID-19 social media infodemic. Sci Rep, 10, 16598.
  • Datare. (2021). Global social media overview. https://datareportal.com/social-media-users adresinden erişilmiştir.
  • Desai, T., Shariff, A., Shariff, A., Kats, M., Fang, X., Christiano, C. ve Ferris, M. (2012). Tweeting the meeting: An in-depth analysis of Twitter activity at Kidney Week 2011. PLoS One, 7(7), e40253.
  • DSÖ. (2020). Health promotion. Erişim adresi http://www.who.int/topics/ health_promotion/en/
  • El-Awaisi, A., O’Carroll, V., Koraysh, S., Koummich, S. ve Huber, M. (2020). Perceptions of who is in the healthcare team? A content analysis of social media posts during COVID-19 pandemic. Journal of Interprofessional Care, 34(5), 622-632.
  • Gao, X. (2018). Networked co-production of 311 services: investigating the use of Twitter in five US cities. Int. J. Public Adm. 41(9), 712-724.
  • García, G.M., Haboud, M., Howard, R., Manresa, A. ve Zurita, J. (2020). Miscommunication in the COVID‐19
  • Garcia, K. ve Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057.
  • Gefen, D., Endicott, J. E., Fresneda, J. E., Miller, J. ve Larsen, K. R. (2017). A guide to text analysis with latent semantic analysis in R with annotated code: Studying online reviews and the stack exchange community. Communications of the Association for Information Systems, 41, 450-496.
  • Gemlik, N., Pektaş, A. ve Arslanoğlu, A. (2021). COVİD-19 salgını sürecinde Twitter haberciliği üzerine nitel bir araştırma. Sağlık Profesyonelleri Araştırma Dergisi, 3(1), 15-24.
  • Goel, A. ve Gupta, L. (2020). Social media in the times of COVID-19. Journal of Clinical Rheumatology: practical Reports on Rheumatic & Musculoskeletal Diseases, 26(6), 220–223.
  • Han, X., Wang, J., Zhang, M. ve Wang, X. (2020). Using social media to mine and analyze public opinion related to COVID-19 in China. Int J Environ Res Public Health, 17(8), 2788.
  • Heldman, A., Schindelar, J. ve Weaver, J. (2013). Social media engagement and public health communication: Implications for public health organizations being truly “social”. Public Health Reviews, 35(13), 1-18.
  • Hocberg, I., Allon, R. ve Yom-Tov, E. (2020). Assessment of the frequency of online searches for symptoms before diagnosis: analysis of archival data. J Med Internet Res, 22(3), 1-7.
  • Imran, M., Ofli, F., Caragea, D. ve Torralba, A. (2020). Using AI and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Inf Process Manag, 57(5), 102261.
  • Islam, M. S., Sarkar, T. ve Khan, S. H. (2020). COVID-19-related infodemic and its impact on public health: A global social media analysis. Am J Trop Med Hyg., 103(4), 1621-1629.
  • Keskin, E. ve Mendeş, M. (2021). COVID-19 pandemisi ile ilgili Twitter mesajlarının metin madenciliği tekniği ile değerlendirilmesi. Turkiye Klinikleri J Biostat, 13(1), 82-90.
  • Kotsenas, A. L., Arce, M., Aase, L., Farris, K., Timimi, C. Y. ve Wald, J. T. (2018). The strategic imperative for the use of social media in health care. Journal of the American College of Radiology, 15(1B), 155-161.
  • Kouzy, R., Abi, Jaoude J. ve Kraitem, A. (2020). Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on Twitter. Cureus, 12(3), e7255.
  • Lamsal, R. (2021). Design and analysis of a large-scale COVID-19 tweets dataset. Appl Intell, 51, 2790-2804.
  • Landwehr, P. M., Wei, W., Kowalchuck, M. ve Carley, K. M. (2016). Using tweets to support disaster planning, warning and response. Saf Sci, 90, 33-47.
  • Landauer, T. K., McNamara, D. S., Dennis, S. ve Kintsch, W. (2007). Handbook of latent semantic analysis. Lawrence Erlbaum Associates Publishers.
  • Li, C., Chen, L. J., Chen, X., Zhang, M., Pang, C. P. ve Chen, H. (2020). Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data. Euro Surveill., 25, 2000199.
  • Lu, Y. ve Zhang, L. (2020). Social media WeChat infers the development trend of COVID-19. J Infect., 81: e82-e83.
  • Lwin, M. O., Lu, J., Sheldenkar, A., Schulz, P. J., Shin, W., Gupta, R. ve Yang, Y. (2020). Global sentiments surrounding the COVID-19 pandemic on Twitter: Analysis of Twitter trends. JMIR Public Health and Surveillance, 6(2), e19447.
  • Malecki, K., Keating, J. A. ve Safdar, N. (2021). Crisis Communication and public perception of COVID-19 risk in the era of social media. Clinical Infectious Diseases, 72(4), 697-702.
  • Muniz-Rodriguez, K., Ofori, S. K., Bayliss, L. C., Schwind, J. S., Diallo, K., Liu, M., … Fung, I. C.-H. (2020). Social media use in emergency response to natural disasters: A systematic review with a public health perspective. Disaster Medicine and Public Health Preparedness, 14(1), 139-149.
  • Niknam, F., Samadbeik, M., Fatehi, F., Shirdel, M., Rezazadeh, M. ve Bastani, P. (2020). COVID-19 on Instagram: A content analysis of selected accounts. Health Policy and Technology, 10(1): 165-173.
  • Oberlo. (2021). Twitter statistics. https://www.oberlo.com/blog/twitter-statistics adresinden erişilmiştir.
  • Padilla, D. A. ve Tortolero, L. (2020). Social media influence in the COVID-19 Pandemic. Int. Braz Jurol, 46(1), 120-124.
  • Park, H. W., Park, S. ve Chong, M. (2020). Conversations and medical news frames on Twitter: Infodemiological study on COVID-19 in South Korea. J Med Internet Res, 22(5), e18897.
  • Perez, S. (2020). Twitter has a record-breaking week as users looked for news of protests and COVID-19. TechCrunch. https://techcrunch.com/2020/06/04/twitter-has-a-record-breaking-week-as-users looked-for-news-ofprotests-and-covid-19/ adresinden erişilmiştir.
  • Petersen, K. ve Gerken, J. M. (2021). #Covid-19: An exploratory investigation of hashtag usage on Twitter. Health Policy, 125(4), 541-547.
  • Riquelme, F. ve González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Journal of Information Processing and Management, 52, 1-33.
  • Shannon, S. ve Kent, N. (2020). 8 charts on internet use around the world as countries grapple with COVID-19 Internet. Pew Research Center. https://www.pewresearch.org/fact-tank/2020/04/02/8-charts-on-internetuse-around-the-world-as-countries-grapple-with-covid-19/ adresinden erişilmiştir.
  • Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L. ve Merchant, R. M. (2017). Twitter as a tool for health research: A systematic review. Am J Public Health, 107(1), e1-e8.
  • Wasserman, S. ve Faust, K. (2005). Social network analysis: Methods and applications. New York, Cambridge University Press.
  • Wright, K. B., Sparks, L. ve O’Hair, H. D. (2013). Health communication in the 21st century. UK: Wiley-Blackwell, UK.
  • Zahra, K., Imran, M. ve Ostermann, F. O. (2020). Automatic identification of eyewitness messages on twitter during disasters. Inf Process Manag, 57(1), 102107.
  • Zhu, H., Wu, H., Cao, J., Fu, G. ve Li, H. (2018). Information dissemination model for social media with constant updates. Physica A: Statistical Mechanics and its Applications, 502, 469-482.
There are 52 citations in total.

Details

Primary Language Turkish
Subjects Sociology
Journal Section Research Articles
Authors

M. Fevzi Esen

Publication Date September 15, 2021
Published in Issue Year 2021 Volume: 11 Issue: 3

Cite

APA Esen, M. F. (2021). Covid-19 ile İlişkili Türkçe Sosyal Medya Paylaşımlarının İçerik Analizi. İnsan Ve Toplum, 11(3), 113-137.
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