Health and political misinformation in Italy. The case of mandatory vaccine law

Political and health misinformation are huge problems today. Both can have nefarious consequences on individuals, citizens, institutions and society. As I and co-authors have recently observed in our paper “Blurred Shots: Investigating the Information Crisis Around Vaccination in Italy”, politicization can increase the circulation of misinformation on vaccines “both directly and by opening the door to pseudoscientific and conspiratorial content (…) published by problematic news sources”. It is therefore interesting to further analyze the link between politicization and misinformation.

Italy is an excellent case for studying the intertwining of politics and health misinformation, since it has been at the center of both political and health information crisis during the election (on March 2018) and the debate on the law on mandatory child vaccinations (around July 2017).

I analyzed about 500,000 tweets published between 2016-02-01 and 2019-01-31 (three years),18 months before and after July 31, 2017, when the Italian law on mandatory vaccinations came into force (you can read here the working paper).

The peak of discussions during the political debate was very clear and a structural break analysis confirmed that the usual flow of conversation dramatically changed around that time.

Annotated time series of the Italian tweets on vaccines. The red box indicates the boundaries of the structural break, exactly during the debate on the mandatory vaccinations law.

I categorized over 1,000 information sources shared by Twitter’s users and used a combination of network analysis, correspondence analysis and clustering to identify the groups of information source categories frequently shared together.

The resulting map was unequivocal: the Twitter information environment related to vaccines was polarized and characterized by information homophily. The information sources openly promoting anti-vax perspectives were clustered with blacklisted domains (included in the blacklists of the main Italian debunkers), alternative therapy sites, and conspiracy blogs. On the opposite polarity of the Correspondence Analysis axis (the dimension account for over the 70% of the total variance) we find scientific information sources, those of health organizations, as well as the official sources. Political information sources (such as websites of political parties and advocacy organizations) lie in between these two polarities.

Clusters of information sources

I measured the spread of health misinformation by using the tweets that shared sources included in the cluster of problematic information sources and found that the time series of tweets sharing misinformation was characterized by a structural break during the political debate as the whole series of tweets. It seemed, therefore, that the politicization of the topic fostered not only the conversations on the topic in general, but also the spread of health misinformation.

Since the spread of misinformation can be clearly associated with the general attention and more specifically with the media attention to the topic (misinformation sources can try to “ride the wave” of this interest), I used a Vector Autoregressive Model to further test the hypothesis, and found that politicization, operationalized through a proxy variable indicating the structural break, was associated with an increase in quantity of misinformation also keeping constant the quantity of news media coverage of the previous days, which I measured through Media Cloud (but I found roughly the same results also using the time series of tweets sharing information sources categorized as news media).

VAR model with the time series of tweets sharing problematic information sources as the dependent variable.

Moreover, it seems that while misinformation, on average, grew a lot during the political debate (of about 6 times the period before the political debate) and also after that time (about two times the period before the political debate), the number of tweets sharing scientific and health information sources increased much less during the central period of the debate (only about two times) and even decreased after that time.

This study is only a small step toward understanding the intertwining of health and political misinformation. There is still much work to be done, other case studies are needed and more sophisticated measures are needed. In the meanwhile you can read here the full paper of the study briefly described above.

Total number of tweets that shared information sources included in each cluster (news media were considered separately) and, in parentheses, the average number of tweets by day (M) and the index numbers to compare differences (I).