Published February 16, 2025 | Version Review Article
Journal article Open

Application of Lotka's Law to Study the Retweets on #Research

Authors/Creators

  • 1. Department of Library and Information Science, University of Madras
  • 2. Department of Library and Information Science, University of Madras, Chennai,

Contributors

  • 1. Department of Library and Information Science, University of Madras, Chennai,

Description

Twitter is an extraordinary microblogging tool used by millions of users regularly. Through its primary purpose is socially connecting users and helping them share their thoughts, it has become an excellent tool for aggregating research by researchers and academicians globally. The present study collates data from Twitter using #research shared on a particular date using data mining techniques. The collected data is analysed based on retweets for the country of origin, accompanying hashtags, and most popular tweets, and the content of the tweets is also analysed. It is astonishing that though there are 1584 unique tweets, only 425 are retweeted. There are about 274 retweets that are retweeted at least once, while there are only 4 retweets that are repeatedly shared by other users at least more than 10 times. The study investigates the usage of #research on Twitter to glean insight into the content of the majorly shared tweets.

Files

Jenita.pdf

Files (1.2 MB)

Name Size Download all
md5:8c9bf8d04deef2bb171ba7dc94006247
1.2 MB Preview Download

Additional details

References

  • 1. Abbasi-Perez, A., Alvarez-Mon, M. A., Donat-Vargas, C., Ortega, M. A., Monserrat, J., Perez-Gomez, A., Sanz, I., & Alvarez-Mon, M. (2021). Analysis of tweets containing information related to rheumatological diseases on Twitter. International Journal of Environmental Research and Public Health, 18(17), 18179094. https://doi.org/10.3390/ijerph18179094 2. Antoniadis, K., Zafiropoulos, K., & Vrana, V. (2015). Communities of Followers in Tourism Twitter Accounts of EuropeanCountries. European Journal of Tourism Hospitality and Recreation, 6(1), 11–26. 3. Berkovic, D., Ackerman, I. N., Briggs, A. M., & Ayton, D. (2020). Tweets by people with arthritis during the COVID-19 pandemic: Content and sentiment analysis. Journal of Medical Internet Research, 22(12), 1–17. https://doi.org/10.2196/24550 4. Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. 2010 43rd Hawaii International Conference on System Sciences, 1–10. https://doi.org/10.1109/HICSS.2010.412 5. Cavazos-Rehg, P. A., Sowles, S. J., Krauss, M. J. ., Agbonavbare, V., Grucza, R., & Bierut, L. (2016). A content analysis of tweets about high-potency marijuana. Drug and Alcohol Dependence, 166, 100–108. https://doi.org/https://doi.org/10.1016/j.drugalcdep.2016.06.034 6. Cavazos-Rehg, P. A., Krauss, M. J., Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., & Bierut, L. J. (2016). A content analysis of depression-related tweets. Computers in Human Behavior, 54, 351–357. https://doi.org/10.1016/j.chb.2015.08.023 7. Chew, C., & Eysenbach, G. (2010). Pandemics in the age of Twitter: Content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE, 5(11), 1–13. https://doi.org/10.1371/journal.pone.0014118 8. Curlin, T., Jaković, B., & Miloloža, I. (2019). Twitter usage in Tourism: Literature Review. Business Systems Research, 10(1), 102–119. https://doi.org/10.2478/bsrj-2019-0008 9. Java, A., Song, X., Finin, T., & Tseng, B. (2007). Why We Twitter: Understanding Microblogging Usage and Communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, 56–65. https://doi.org/10.1145/1348549.1348556 10. 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, WWW '10, 591–600. https://doi.org/10.1145/1772690.1772751 11. Lee, J. L., DeCamp, M., Dredze, M., Chisolm, M. S., & Berger, Z. D. (2014). What are health-related users tweeting? A qualitative content analysis of health-related users and their messages on Twitter. Journal of Medical Internet Research, 16(10), e237. https://doi.org/10.2196/jmir.3765 12. Liu, Sam, Zhu, M., & Young, S. D. (2018). Monitoring freshman college experience through content analysis of tweets: Observational study. JMIR Public Health and Surveillance, 4(1), 1–8. https://doi.org/10.2196/publichealth.7444 13. Murthy, D. (2013). Twitter: Social Communication in the Twitter Age. Polity press. 14. Ovadia, S. (2009). Exploring the Potential of Twitter as a Research Tool. Behavioral & Social Sciences Librarian, 28(4), 202–205. https://doi.org/10.1080/01639260903280888 15. Rosenberg, H., Syed, S., & Rezaie, S. (2020). The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic. Canadian Journal of Emergency Medicine, 22(4), 418–421. https://doi.org/10.1017/cem.2020.361 16. Sayce, D. (2022). The Number of tweets per day in 2022. https://www.dsayce.com/social-media/tweets-day/ 17. Skaza, J., & Blais, B. (2017). Modeling the infectiousness of Twitter hashtags. Physica A: Statistical Mechanics and Its Applications, 465, 289–296. https://doi.org/https://doi.org/10.1016/j.physa.2016.08.038