Preprint / Version 2

Understanding Influential Accounts on Twitter


  • Aarush Shintre Millenium National School



Twitter, Covid-19, Vaccinations, LDA, Social media analytics, Social Network Research, Sentiment-Analysis, Influence, Python, Pro-vaxx, Anti-vaxx, graph-based, NLP


Twitter is a popular microblogging application that has become increasingly popular for its ability to provide a communication platform for people worldwide, on various topics. Data from twitter is incredibly useful in understanding everyday people’s opinions and sentiments in various contexts, including global issues, like the Covid-19 Pandemic, and the subsequent vaccination efforts. Looking at certain trends in sentiments, topics discussed and what popular influential accounts on twitter talk about are all great ways to understand what is relevant and important to people worldwide. 

We tried to understand accounts’ influence, by analyzing tweets spawned on twitter. First, we tried to understand when were covid-vaccine related tweets most popular in the time span of the data, and what factors might have affected popularity of these tweets. We categorized tweets into pro, anti, and neutral sentiments towards vaccinations, and further analysis was conducted on these categories. 

Interestingly, we observed that about 42% of all favorites and retweets received throughout the time span were attributed to pro-vaccine tweets, which made up 30% of all tweets spawned. To understand what topics were discussed through these popular pro-vaccine tweets that helped them gain popularity, we conducted LDA Analysis on them. The pro-vaccine accounts provided information about covid vaccines, including discussions about vaccine efficacy and vaccine administration, which might have helped their tweets gain traction  in the covid-19 vaccine discussion.


Suh, Bongwon, et al. "Want to be retweeted? large scale analytics on factors impacting retweet in twitter network." 2010 IEEE second international conference on social computing. IEEE, 2010.

Weng, Jianshu, et al. "Twitterrank: finding topic-sensitive influential twitterers." Proceedings of the third ACM international conference on Web search and data mining. 2010.

Cha, Meeyoung, Hamed Haddadi, and Fabricio Benevenuto. "Gummad. KP Measuring user influence on Twitter: The million follower fallacy." Proceedings of the fourth international aaai conference on weblogs and social media. 2010.

Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.

Lim, Dongwoo, Fujio Toriumi, and Mitsuo Yoshida. "Do you trust experts on Twitter? Successful correction of COVID-19-related misinformation." IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 2021.

Pujari, Rajkumar, and Dan Goldwasser. "Understanding politics via contextualized discourse processing." arXiv preprint arXiv:2012.15784 (2020)

Jang, Hyeju, et al. "Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis." Journal of Medical Internet Research 24.3 (2022): e35016.

Huangfu, Luwen, et al. "COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment–Based Topic Modeling." Journal of medical Internet research 24.2 (2022): e31726.

Nezhad, Zahra Bokaee, and Mohammad Ali Deihimi. "Twitter sentiment analysis from Iran about COVID 19 vaccine." Diabetes & Metabolic Syndrome: Clinical Research & Reviews 16.1 (2022): 102367.

Rogers, E. M. “Diffusion of Innovations” Free Press, 1962

Katz E, and Lazarsfeld P “Personal Influence: The Part Played by People in the Flow of Mass Communications” New York: The Free Press. 1955

Gladwell, M “The Tipping Point: How Little Things Can Make a Big Difference” Back Bay Books, 2002

Khan, Yusra Habib, et al. "Threat of COVID-19 vaccine hesitancy in Pakistan: the need for measures to neutralize misleading narratives." The American journal of tropical medicine and hygiene 103.2 (2020): 603.

Loomba, Sahil, et al. "Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA." Nature human behaviour 5.3 (2021): 337-348.

Thelwall, Mike, Kayvan Kousha, and Saheeda Thelwall. "Covid-19 vaccine hesitancy on English-language Twitter." Profesional de la información (EPI) 30.2 (2021).



2022-11-02 — Updated on 2022-12-20