An Analysis of the Characteristics of Verified Twitter Users
Twitter, the most popular microblog, contains a large variety of users as a result of its huge popularity. Twitter manually verifies the accounts which are deemed worthy of public interest. As a natural consequence of being verified, users trust these verified accounts since they represent legitimate users, and are managed by authorized users. To the best of our knowledge, Twitter has never revealed the requirements of being verified. In this study, in order to shed light on the characteristics of verified Twitter users, a software, which is based on Python programming language that utilizes a recent dataset, which consists of 297,798 verified Twitter users, was implemented within the scope of this study. The characteristics of verified Twitter users such as being public, and having a customized profile were revealed as a result of the analysis of the utilized dataset.
Doğrulanmış Twitter Hesaplarının Karakteristiklerinin Analizi
En popüler mikroblog olan Twitter, sahip olduğu devasa popüleritenin sonucu olarak çok çeşitli kullanıcı kitlesine sahiptir. Twitter kamu yararına olacağı inanılan hesapları elle doğrulamaktadır. Doğrulanmanın doğal bir sonucu olarak kullanıcılar, bu hesapların meşru kullanıcıları temsil etmesinden ve yetkili kullanıcılar tarafından yönetilmesinden dolayı bu hesaplara güven duymaktadır. Elde ettiğimiz en iyi verilere göre, Twitter doğrulanmanın gereksinimlerini hiçbir zaman açıklamamıştır. Bu çalışmada, doğrulanmış kullanıcıların karakteristiklerine ışık tutmak amacıyla bu çalışma kapsamında Python programlam dili tabanlı 297.798 doğrulanmış Twitter kullanıcısı içeren güncel bir verisetini kullanan bir yazılım geliştirilmiştir. Bu veriseti üzerinde yapılan analizler sonucunda doğrulanmış kullanıcıların kamuya açık olma, kişiselleştirilmiş bir profile sahip olma gibi ortak karakteristikleri açığa çıkartılmıştır.
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[1] “Q1 2019 Earnings Report,” Twitter, 2019. [Online]. Available: https://s22.q4cdn.com/826641620/files/doc_financials/2019/q1/Q1-2019-Slide- Presentation.pdf. [Accessed: 15-Nov-2019].[2] “Agency Playbook,” Twitter, 2019. [Online]. Available: https://cdn.cmstwdigitalassets. com/content/dam/businesstwitter/ resources/Twitter_Agency_Playbook_2019.pdf. [Accessed: 15-Nov-2019].
[3] “2018 Twitter Report,” Mention, 2018. [Online]. Available: https://info.mention.com/hubfs/Twitter Engagement Report 2018 %7C Mention.pdf. [Accessed: 15-Nov-2019].
[4] A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining,” in Proceedings of LREC’10, the Seventh International Conference on Language Resources and Evaluation, 2010, pp. 1320–1326.
[5] S. Waisbord and A. Amado, “Populist communication by digital means: presidential Twitter in Latin America,” Inf. Commun. Soc., vol. 20, no. 9, pp. 1330–1346, 2017.
[6] H. Kwak, C. Lee, H. Park, and S. Moon, “What is Twitter, a social network or a news media?,” in Proceedings of the 19th International Conference on World Wide Web (WWW ’10), 2010, pp. 591–600.
[7] R. Li, K. H. Lei, R. Khadiwala, and K. C. C. Chang, “TEDAS: A twitter-based event detection and analysis system,” in Proceedings of the 2012 IEEE 28th International Conference on Data Engineering, 2012, pp. 1273–1276.
[8] I. Paul, A. Khattar, P. Kumaraguru, M. Gupta, and S. Chopra, “Elites Tweet? Characterizing the Twitter Verified User Network,” in Proceedings of the 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019.
[9] M. Hentschel, O. Alonso, S. Counts, and V. Kandylas, “Finding Users We Trust: Scaling Up Verified Twitter Users Using Their Communication Patterns,” in Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM 2014), 2014, pp. 591– 594.
[10] O. Varol, E. Ferrara, C. A. Davis, F. Menczer, and A. Flammini, “Online Human-Bot Interactions: Detection, Estimation, and Characterization,” in Proceedings of the 11th International Conference on Web and Social Media (ICWSM 2017), 2017.
[11] C. De Micheli and A. Stroppa, “Twitter and the underground market,” 2013. [Online]. Available: https://nexa.polito.it/nexacenterfiles/lunch-11-de_micheli-stroppa.pdf. [Accessed: 16-Nov- 2019].
[12] Y. Roth and D. Harvey, “How Twitter is fighting spam and malicious automation,” Twitter, 2018. [Online]. Available: https://blog.twitter.com/en_us/topics/company/2018/how-twitter-isfighting- spam-and-malicious-automation.html. [Accessed: 16-Nov-2019].
[13] H. L. LaMarre and Y. Suzuki-Lambrecht, “Tweeting democracy? Examining Twitter as an online public relations strategy for congressional campaigns’,” Public Relat. Rev., vol. 39, no. 4, pp. 360–368, 2013.
[14] Jason Baumgartner, “All Verified Twitter Users (100% complete) in ndjson format : datasets,” pushshift.io, 2018. [Online]. Available: https://www.reddit.com/r/datasets/comments/8s6nqz/all_verified_twitter_users_100_complete_ in_ndjson/. [Accessed: 15-Nov-2019].
[15] “API reference index — Twitter Developers,” Twitter, 2019. [Online]. Available: https://developer.twitter.com/en/docs/api-reference-index. [Accessed: 15-Nov-2019].
[16] B. Erşahin, Ö. Aktaş, D. Kilmç, and C. Akyol, “Twitter fake account detection,” in 2nd International Conference on Computer Science and Engineering (UBMK 2017), 2017, pp. 388– 392.
[17] “pandas: Python Data Analysis Library,” 2019. [Online]. Available: https://pandas.pydata.org. [Accessed: 16-Nov-2019].
[18] “Matplotlib: Python plotting — Matplotlib 3.1.1 documentation,” 2019. [Online]. Available: https://matplotlib.org. [Accessed: 16-Nov-2019].
[19] T. Velayutham and P. K. Tiwari, “Bot Identification: Helping Analysts for Right Data in Twitter,” in Proceedings - 2017 3rd International Conference on Advances in Computing, Communication and Automation (Fall) (ICACCA 2017), 2018, pp. 1–5.
[20] S. Yardi, D. Romero, G. Schoenebeck, and D. Boyd, “Detecting spam in a Twitter network,” First Monday, vol. 15, no. 1, 2010.
- ISSN: 2636-8129
- Yayın Aralığı: Yılda 3 Sayı
- Başlangıç: 2018
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An Analysis of the Characteristics of Verified Twitter Users