International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.13(4)(2022)

  • Current Trend of Virology in the Post COVID-19 Era with Related Topic-Based Tweet Analysis

    Fahim K Sufi (Federal Government, Melbourne, AUSTRALIA),
    Musleh AlSulami (Umm Al-Qura University, Makkah, Makkah, SAUDI ARABIA).

    Disciplinary: Sociology, Information Technology (Social Media and Networking).

    ➤ FullText

    doi: 10.14456/ITJEMAST.2022.83

    Keywords: Social media analysis; Tweet analysis; Covid tweet; Tweeter hashtag; Covid hashtag; number of posts; number of users; Engagement metric; Influence metric; Word cloud on Virology; Virological Science; Preventive vaccines; Therapeutic vaccines; Viral pathogenesis; Viral immunology; Antiviral strategies; Virus structure; Virus expression; Viral resistance; Emerging viruses; Interspecies transmission; Viruses and cancer; Viral diseases.

    Abstract
    In light of current trends in virology, we performed social media analysis of 13 main topics in the area of virology and ranked these topics with metrics such as users, posts, engagement, and influence. These metrics were monitored against the 13 keywords on Twitter for the same period (i.e., from 27 November to 6 December 2021) for benchmarking purposes. The 13 main topics were "virological Science", "preventive vaccines", "therapeutic vaccines", "viral pathogenesis", "viral immunology", "antiviral strategies", "virus structure", "virus expression", "viral resistance", "emerging viruses", "interspecies transmission", "viruses and cancer" and "viral diseases". "viral diseases" recorded the highest number of users (i.e., 905 users) and the highest number of post (i.e., about 1K posts). The second-highest number of posts were monitored to be on "therapeutic vaccines" with 729 posts from 691 users. In terms of engagement, "viral diseases" (3.4 K) were found to be on the top followed by "viruses and cancer" (3.1K). Lastly, in terms of influence, "viral diseases" recorded 9.0 million influences followed by 6.6 million influences on "emerging viruses". In summary, "viral diseases" was found to be the most engaging and influential topic highest with the highest number of posts from most of the tweet users. In relation to trending hashtags in virology, #COVID19 recorded the highest number of hashtags, followed by #omicron, #sarscov2, #publichealth, #omicronvarient, #wuhan, #originofcovid, #fauci and #epidemiology. Word clouds showing the main area of discussion were also generated for these 13 main topics.

    Paper ID: 13A4T

    Cite this article:

    Sufi, F. K., AlSulami, M. (2022). Current Trend of Virology in the Post COVID-19 Era with Related Topic-Based Tweet Analysis. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 13(4), 13A4T, 1-10. http://TUENGR.COM/V13/13A4T.pdf DOI: 10.14456/ITJEMAST.2022.83

References

  1. F. K. Sufi and M. Alsulami, "Automated Multidimensional Analysis of Global Events With Entity Detection, Sentiment Analysis and Anomaly Detection," IEEE Access, vol. 9, pp. 152449-152460, 2021.
  2. F. K. Sufi, "AI-GlobalEvents: A Software for analyzing, identifying and explaining global events with Artificial Intelligence," Software Impacts, vol. Accepted in Press, no. DOI:10.1016/j.simpa.2022.100218, 2022.
  3. F. K. Sufi and M. Alsulami, "Knowledge Discovery of Global Landslides Using Automated Machine Learning Algorithms," IEEE Access, vol. 9, pp. 131400-131419, 2021.
  4. F. K. Sufi, "AI-Landslide: Software for acquiring hidden insights from global landslide data using Artificial Intelligence," Software Impacts, vol. 10, no. 100177, 2021.
  5. F. Sufi and I. Khalil, "Diagnosis of cardiovascular abnormalities from compressed ECG: a data mining-based approach," IEEE transactions on information technology in biomedicine, vol. 15, no. 1, pp.33-39, 2010.
  6. F. Sufi and I. Khalil, "A clustering based system for instant detection of cardiac abnormalities from compressed ECG," Expert Systems with Applications, vol. 38, no. 5, pp. 4705-4713, 2011.
  7. F. Sufi and I. Khalil, "Faster person identification using compressed ECG in time critical wireless telecardiology applications," Journal of Network and Computer Applications, vol. 34, no. 1, pp. 282-293, 2011.
  8. F. Sufi, Q. Fang, I. Khalil and S. S. Mahmoud, "Novel methods of faster cardiovascular diagnosis in wireless telecardiology," IEEE Journal on Selected Areas in Communications, vol. 27, no. 4, pp. 537-552, 2009.


Other issues:
Vol.13(3)(2021)
Vol.13(2)(2021)
Vol.13(1)(2021)
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