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Twitter (X)'da Alana Özgü Kanaat Önderlerinin Belirlenmesi: İyileştirilmiş Bir Yaklaşım

Year 2024, Volume: 8 Issue: 1, 65 - 81, 31.05.2024
https://doi.org/10.33709/ictimaiyat.1404626

Abstract

Bilginin yayılımını analiz etmek ve anlamak için Twitter'daki kanaat önderlerinin rolü göz ardı edilemez. Bilgi çağının gelişi ve sosyal ağ platformlarının ortaya çıkışı kanaat önderliğini ortadan kaldırmamış, aksine yeni biçimlerinin ortaya çıkmasına neden olmuştur. Bu doğrultuda, bu makale Twitter'daki kanaat önderlerini tespit etmeye yönelik bir yöntem önermektedir. Bu yöntem temel olarak Twitter listelerine eklenmiş kullanıcıların uzmanlıklarına tahmin etmeye yönelik anlamsal ipuçları içeren meta verilerine dayanmaktadır. Bu çalışma halihazırda kullanılan bu yöntemi temel alarak yöntemin herhangi bir uzmanlık alandaki yüksek etkili Twitter kullanıcılarını belirlemek için nasıl daha esnek bir şekilde kullanılabileceğini ayrıntılı olarak göstermektedir. Bu bağlamda, çalışmadan önerilen yöntem farklı araştırma sorularına uyarlanarak, araştırmacıların kendi amaçlarına uyacak şekilde uygulanmasına olanak tanıyacaktır. Bu çalışma ayrıca, Twitter listeleri aracılığıyla belirlenen kanaat önderlerinin nasıl sıralanabileceğine ilişkin yeni bir yaklaşım önermektedir. Bu bağlamda geliştirilen sıralama endeksi, bilgi yayılımının hem dikey (kamuoyu algısı ve etkileşimi) hem de yatay (alandaki itibarı) boyutlarına dikkate almaktadır.

References

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Identifying Domain-specific Opinion Leaders in Twitter (X): An Optimized Approach

Year 2024, Volume: 8 Issue: 1, 65 - 81, 31.05.2024
https://doi.org/10.33709/ictimaiyat.1404626

Abstract

The role of opinion leaders on Twitter to analyze and understand the diffusion of information cannot be overlooked. The coming of the information age and the advent of social networking platforms have not eliminated opinion leadership but rather led to the emergence of its new forms. In this line, this paper deals with organically identifying opinion leaders on Twitter, based on the list feature. The method relies on the meta-data of Twitter lists, containing semantic cues to infer the topical expertise of its members. Based on the studies that have already shown the effectiveness of this method, this paper further illustrates in detail how the method can be employed flexibly to identify highly influential Twitter users in any specific domain. In this regard, the method can be adapted to different research questions, allowing researchers to apply it to suit their specific objectives and data. This paper also presents a novel approach as to how influential Twitter users identified through Twitter lists can be ranked. The ranking index proposed is attentive to both vertical (public perception and engagement) and horizontal (peer perception) dimensions of information diffusion.

References

  • Documenting the Now Project. (2021). DocNow/twarc (2.8.0) [Python]. Documenting the Now. https://doi.org/10.5281/zenodo.593575. (Original work published 2013)
  • Dongwoo, K., Yohan, J., Il-Chul, M., & Oh, A. H. (2010, April 10). Analysis of Twitter Lists as a Potential Source for Discovering Latent Characteristics of Users. Workshop on Microblogging. CHI ’10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, CA, USA.
  • Dorsey, J. [@jack]. (2018, September 5). In any public space, you’ll find inspired ideas, and you’ll find lies and deception. People who want to help others and unify, and people who want to hurt others and themselves and divide. What separates a physical and digital public space is greater accessibility and velocity. [Post]. X. https://twitter.com/jack/status/1037339409862025216
  • Harmon, R., J. & other contributors. (2009). Tweepy Documentation [Computer software]. https://docs.tweepy.org/en/stable/api.html#tweepy.API.get_list_members
  • Harmon, R., J. & other contributors. (2021). Tweepy (4.1.0) [Python]. https://doi.org/10.5281/zenodo.7860636 Heumann, S. (2018, October 12). Why Social Media Platforms Should Be Treated as Critical Infrastructures. Election Interference in the Digital Age. https://medium.com/election-interference-in-the-digital-age/why-social-media-platforms-should-be-treated-as-critical-infrastructures-6a437a127ff7
  • Hey, T., Tansley, S., & Tolle, K. (Eds.). (2009). Jim Gray on eScience: A transformed scientific method. In The fourth paradigm: Data-intensive scientific discovery. Microsoft Research.
  • Ke, Q., Ahn, Y.-Y., & Sugimoto, C. R. (2017). A systematic identification and analysis of scientists on Twitter. PLOS ONE, 12(4), e0175368. https://doi.org/10.1371/journal.pone.0175368
  • Kitchin, R. (2017). Big Data – Hype or Revolution? In A. Quan-Haase & L. Sloan (Eds.), The SAGE handbook of social media research methods (pp. 27–40). SAGE.
  • Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? Proceedings of the 19th International Conference on World Wide Web, 591–600. https://doi.org/10.1145/1772690.1772751
  • Lazarsfeld, P. F., Berelson, B., & Gaudet, H. (1960). The people’s choice: How the voter makes up his mind in a presidential campaign (2nd ed). Columbia University Press.
  • McClain, C., Widjaya, R., Rivero, G., & Smith, A. (2021, November 15). The behaviors and attitudes of U.S. adults on twitter. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2021/11/15/the-behaviors-and-attitudes-of-u-s-adults-on-twitter/
  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415–444. https://doi.org/10.1146/annurev.soc.27.1.415
  • Musk, E. [@elonmusk]. (2022, October 27). Dear Twitter Advertisers https://t.co/GMwHmInPAS [Post]. X. https://twitter.com/elonmusk/status/1585619322239561728
  • Myers, S. A., Sharma, A., Gupta, P., & Lin, J. (2014). Information network or social network?: The structure of the Twitter follow graph. Proceedings of the 23rd International Conference on World Wide Web, 493–498. https://doi.org/10.1145/2567948.2576939
  • Park, C. S. (2013). Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), 1641–1648. https://doi.org/10.1016/j.chb.2013.01.044 Python Software Foundation. (2021). Python Language Reference (3.9.5) [Python]. https://www.python.org
  • Rogers, E. M. (2003). Diffusion of innovations (5th ed). Free Press.
  • Sharma, N. K., Ghosh, S., Benevenuto, F., Ganguly, N., & Gummadi, K. (2012). Inferring who-is-who in the Twitter social network. ACM SIGCOMM Computer Communication Review, 42(4), 533–538. https://doi.org/10.1145/2377677.2377782
  • Sharpe, A. (2020, October 22). Is Social Media Critical Infrastructure? LinkedIn. https://www.linkedin.com/pulse/social-media-critical-infrastructure-alex-sharpe
  • Sloan, L., & Quan-Haase, A. (2017). Introduction to the Handbook of Social Media Research Methods: Goals, Challenges and Innovations. In The SAGE handbook of social media research methods (pp. 1–10). SAGE reference.
  • The pandas development team. (2021). pandas-dev/pandas: Pandas (1.3.3) [Python]. https://doi.org/10.5281/zenodo.3509134 (Original work published 2010).
  • Törnberg, P. (2022). How digital media drive affective polarization through partisan sorting. Proceedings of the National Academy of Sciences, 119(42), e2207159119. https://doi.org/10.1073/pnas.2207159119 Twitter Engagement Calculator. (n.d.). Mention. Retrieved December 13, 2023, from https://mention.com/en/twitter-engagement-calculator/
  • Twitter Engagement Metric. (n.d.). Klipfolio. Retrieved December 13, 2023, from https://www.klipfolio.com/resources/kpi-examples/social-media/twitter-engagement-metrics
  • We Are Social. (2023). Digital 2023: Global Overview Report. https://wearesocial.com/uk/blog/2023/01/digital-2023/
  • Wu, S., Hofman, J., Mason, W., & Watts, D. (2011). Who says what to whom on Twitter. 705–714. https://doi.org/10.1145/1963405.1963504
  • X Developers. (n.d.-a). API Reference (1.1) [Computer software]. X Corp. Retrieved December 13, 2023, from https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/create-manage-lists/api-reference/get-lists-memberships
  • X Developers. (n.d.-b). GET lists/members (1.1) [Computer software]. X Corp. Retrieved December 13, 2023, from https://developer.twitter.com/en/docs/twitter-api/v1/accounts-and-users/create-manage-lists/api-reference/get-lists-members
  • X Developers. (n.d.-c). Twitter API Documentation [Computer software]. Retrieved December 13, 2023, from https://developer.twitter.com/en/docs/twitter-api
  • X (Twitter) Engagement Rate Benchmark. (n.d.). Social Status. Retrieved December 13, 2023, from https://www.socialstatus.io/insights/social-media-benchmarks/twitter-engagement-rate-benchmark/
  • Yamaguchi, Y., Amagasa, T., & Kitagawa, H. (2011). Tag-based User Topic Discovery Using Twitter Lists. 2011 International Conference on Advances in Social Networks Analysis and Mining, 13–20. https://doi.org/10.1109/ASONAM.2011.58
There are 29 citations in total.

Details

Primary Language English
Subjects Sociology and Social Studies of Science and Technology, Communication Sociology, Quantitative Methods in Sociology
Journal Section Orjinal Makale
Authors

Nurullah Karaca 0000-0001-5218-6095

Onur Ayas 0000-0001-5317-271X

Early Pub Date May 21, 2024
Publication Date May 31, 2024
Submission Date December 13, 2023
Acceptance Date January 29, 2024
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

APA Karaca, N., & Ayas, O. (2024). Identifying Domain-specific Opinion Leaders in Twitter (X): An Optimized Approach. İçtimaiyat, 8(1), 65-81. https://doi.org/10.33709/ictimaiyat.1404626