Fake News in the 2018’s Brazilian Presidential Election: An analysis of its diffusion on Twitter

by Pieter Attema Zalis
Thesis supervisor: dr. Damian Trilling

Research Design
This thesis had the following research question: To what extent, during the second round of the 2018’s Brazilian elections, is fake news from one ideology more present than the other, and do they, together, have different patterns of diffusion and reach compared to traditional news on Twitter?

The answer came from a dataset of 19,649,008 messages streamed through Twitter’s Application Programming Interface (API) between October 8, 2018, and October 27, 2018. The query for the data collection contained the names of the two candidates, Haddad and Bolsonaro, and the abbreviations of their respective parties, PT and PSL.

As the interest of this research lies on only understanding patterns of fake and traditional news, we started by filtering messages which contain URLs of news domains that fall into the concepts of fake news and traditional news. Importantly, as Lazer et al. (2018) suggest, we focused the “fakeness” not at the level of the story but of the publisher.

Regarding the research units, in most cases, the analysis was performed at the level of articles. In some case, we aggregate the articles into the level of news domains. Content analysis, a collection of techniques used to analyze media content, is the main research method of this study.

The study had four main hypotheses, which were all confirmed:

  • Right-wing fake news was more common than left-wing fake news.
  • When comparing fake to traditional news, we showed that fake news articles had more emotional content than traditional news articles.
  • Fake news also traveled faster and had more retweets than traditional news articles.
  • When we considered news domains, however, traditional news outlets had more unique users retweeting its content than fake news outlets.

Main contributions
As studies on fake news are only at the earliest stage, this paper brings a valuable contribution to the field as it extends to Brazil, the fourth largest democracy in the world, some of the findings already identified in the US and Europe. We provided, for instance, further evidence that theories, such as motivated reasoning and the significant role of emotions in information diffusion, can partially explain the virality of fake news on social media.

We can add other two main contributions. First, similar to what was discovered in other countries (Fletcher et al., 2018; Grinberg et al., 2019; Nelson & Taneja, 2018), we showed that fake news still is not capable of reaching the same amount of unique users as traditional news. Second, from all findings here, the one with higher effect size was the difference between right-wing and left-wing fake news. Even if it is early to claim that this trend of right-wing predominance is an establish global pattern, this study provides further evidence that it is fundamental to investigate this further in other democracies. As suggested by Bennett and Livingston (2018), there appears to be a correlation between fake news and the emergence of radical right parties.

Methods Innovativeness

There are two main innovations in this research. As far as the author could research, this thesis is the first to use Language Inquiry Word Count (LIWC) – a text analysis software that calculates the degree of usage of different psychologically meaningful categories in text files – to analyze emotions, such as anger, anxiety and sadness, in Brazilian political texts.

Second, the scale of the data set impedes the usage of traditional forms of content analysis. Thus, this study relies mainly on automated content analysis, a collection of techniques used to automatically analyze media content (Trilling & Jonkman, 2018). Specific Python scripts were developed to extract relevant information from the dataset. In other words, this paper brings a mixed of communication science and computer science methods.


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Fletcher, R., Cornia, A., Graves, L., & Nielsen, R. K. (2018). Measuring the reach of fake news and online disinformation in europe. Oxford, UK: Reuters Institute for the Study of Journalism.

Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on twitter during the 2016 U.S. presidential election. Science, 363(6425), 374-378. doi: 0.1126/science.aau2706

Lazer, D., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 36–44. doi: 10.1126/science.aao2998

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Trilling, D., & Jonkman, J. (2018). Scaling up content analysis. Communication Methods and Measures, 12(3), 158–174. doi: 10.1080/19312458.2018.1447655