fbpx Twitter, tweets and influencers | Science in the net

Twitter, tweets and influencers

Primary tabs

Read time: 2 mins

Article published on the ASSET website

How many ways are there to tell a story and who will do it?

In these months we tried to answer those questions by running an analysis of the most relevant tweets and accounts about some key words, chosen by the editorial board, focused on Zika virus and vaccines.

We then developed an application to identify the most influential Twitter users on specific topics, according to a list of hashtag we have provided. Being based on mentions and retweets, such an approach is also effective in discovering influential users on the short period.

Every day, the app extrapolates the most popular accounts according to our key words. A daily analysis of the firsts 20 accounts allowed us to identify some main categories:

•             Institutions

•             Media

•             Firms

•             Researchers

•             University, organizations, and charities. 

We analysed over 500 accounts: 13 belong to public institutions (i.e. United Nations or House Foreign Affairs Committee), 94 to public health institutions (i.e. CDC and WHO) and 66 to employees of public institutions (i.e. Gregory Härtl – Head of Public Relations/Social Media for the World Health Organization – or Tom Frieden – CDC Director). Six accounts belong to politicians (mostly in US).

This study underlined a strong prevalence of media related accounts. Among 100 accounts, we found that 16 belong to medical or scientific journals (as The Lancet or PLoS), 80 belong to newspapers (as Forbes) and 120 to journalists.

18 of the most popular accounts belong to researchers; universities, charities and organizations were included in a single group of 40 accounts. 

#Zika and #vaccine have been the most used hashtags. In particular, #Zika has been used by 455 accounts, while #vaccine was often used in association with other terms or some related concepts, like #vaccineworks, #immunisation and #autism.

Finally, we found that 63 accounts were “unknown people” that, in most cases, only produced a few tweets. Our app recognised them as influencers because of their interactions with some relevant accounts (most of the times CDC, which often replies when cited).

The absence of European accounts may be due on the one hand to the strong presence of #zika (whose spread is mainly focused in South America), and on the other hand to a smaller social presence of European institutions and media.  


Scienza in rete è un giornale senza pubblicità e aperto a tutti per garantire l’indipendenza dell’informazione e il diritto universale alla cittadinanza scientifica. Contribuisci a dar voce alla ricerca sostenendo Scienza in rete. In questo modo, potrai entrare a far parte della nostra comunità e condividere il nostro percorso. Clicca sul pulsante e scegli liberamente quanto donare! Anche una piccola somma è importante. Se vuoi fare una donazione ricorrente, ci consenti di programmare meglio il nostro lavoro e resti comunque libero di interromperla quando credi.


prossimo articolo

Why have neural networks won the Nobel Prizes in Physics and Chemistry?

This year, Artificial Intelligence played a leading role in the Nobel Prizes for Physics and Chemistry. More specifically, it would be better to say machine learning and neural networks, thanks to whose development we now have systems ranging from image recognition to generative AI like Chat-GPT. In this article, Chiara Sabelli tells the story of the research that led physicist and biologist John J. Hopfield and computer scientist and neuroscientist Geoffrey Hinton to lay the foundations of current machine learning.

Image modified from the article "Biohybrid and Bioinspired Magnetic Microswimmers" https://onlinelibrary.wiley.com/doi/epdf/10.1002/smll.201704374

The 2024 Nobel Prize in Physics was awarded to John J. Hopfield, an American physicist and biologist from Princeton University, and to Geoffrey Hinton, a British computer scientist and neuroscientist from the University of Toronto, for utilizing tools from statistical physics in the development of methods underlying today's powerful machine learning technologies.