The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing

TL;DR

It is found that a minority of accounts is responsible for the majority of the misinformation circulating online, and two categories of users are identified: a few active ones, playing the role of ‘creators’, and a majority playing the roles of ’consumers’.

Abstract

Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network - barely considered in the literature - which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analyzing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of "creators", and a majority playing the role of "consumers". The relative proportion of these groups ($\approx$14% creators - 86% consumers) appears stable over time: Consumers are mostly exposed to the opinions of a vocal minority of creators, that could be mistakenly understood as of representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.

Figures (5)

• Figure 1: Characterizing the share of contents with respect to the share of users who produce them. The $x$-axis indicates the share of users appearing more frequently in our data set, while the $y$-axis displays the share of the overall content (tweets and retweets) that those users are responsible for. Different content types are encoded by distinct colours: note the red one, corresponding to content identified as fake news. Black dashed lines correspond to the distribution one would observe if all users were responsible for the same fraction of total content, to highlight the highly heterogeneous activity of content production from different users, regardless of content type.
• Figure 2: Schematic illustration of the separation between creators and consumers. The arrows represent the endorsements (i.e., retweets in Twitter) and go from the retweeting to the retweeted individuals. Values indicates the ratio between the observed number of links and the number one would expect if the links were randomly assigned. Note that the illustration is not at scale with numbers.
• Figure 3: Fluid transitions between creator and consumer groups. Let us consider first-return times: $a)$ schematic example of the behaviour of a user (the circle), who might change his/her group at every time step (e.g., 1 day). Red, blue and black circles represent creators, consumers and non-spreaders, respectively. The return times are the number of black circles that separate colored circles from one another, so in this example they are 3, 0, 1 and 0 days, in chronological order from left-hand to right-hand side. We also report the probability of returning to a skeptic (i.e., non-spreader) group for a user that has just left the creators $b)$ or the consumers $c)$.
• Figure 4: Unraveling causal relationships between group dynamics and fake news volume.$a)$ Comparison between time series of the fraction of fake retweets (black line), the fraction of consumers (blue line) and the fraction of creators (red line). The latters were rescaled to ease the comparison between trends. The time step is of one day while the lines are obtained through a ten days moving average. $b)$ Cross map signal computed for different time-delays with the Convergent Cross Mapping algorithm (see Methods). For the null hypothesis we used surrogates obtained by randomly reshuffling empirical observations. The null hypothesis is rejected at 95% CL, equivalent to an a priori test size of 5%, only at time delay equal to 0 and 1 day, with the strongest signal at the former.
• Figure 5: Analysis of the separation between creators and consumers for different definitions of these groups. The height of the bars indicate the ratio between the observed number of links between two groups and the number we would expect if the links were randomly distributed among the network. The dashed horizontal line corresponds to the case where the number of observed links is compatible with those of a random network ($y=1$). The red and blue colors indicate if the tweet was originally from the creators or from the consumers, respectively. The figures differ because of the threshold in the percentage of most active fake news spreaders used to define creators and consumers. However, it can be seen that the densities of the connections between these groups do not depend strongly on such a threshold.