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

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 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, analysing 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 (which are the origin of 82% of fake content in our data), that could be mistakenly understood as 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.

Modelling and predicting online vaccination views using bow-tie decomposition

“…For instance, we can use bow-tie structure to investigate potential distinctions between misinformation and scientific information dissemination. A recent paper [ 16 ] highlights that a minority of accounts are responsible for the majority of the misinformation circulating on Twitter, a pattern highly pertinent to our bow-tie structure analysis. Bow-tie structure may also help explain the phenomenon of infodemic in misinformation circulation, which describes the situation where exposure to an abundance of information undermines people’s ability to discern disinformation, thus facilitating its dissemination [ 17 , 54 ].…”

“…Given the significant impact of online social media platforms as sources of information, a number of studies have emphasized their effect on vaccination views in public opinion [7][8][9][10][11][12]. Recent studies have highlighted the significance of the information 'creator-receiver' dynamics in online vaccination campaigns: some researchers have found that vaccination opponents tend to produce a higher volume of information than vaccination supporters [12][13][14][15]; several studies have observed that most (mis)information is created by a minority of users (which should not be assumed to be representative of a majority) and that information roles tend to remain relatively stable over time [15,16].…”

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

“…Social networks that are particular to a region can provide user behavior data that can inform early warning detection systems specific to that region; for example, using Baidu search data, Qin et al [ 56 ] were able to predict the number of new COVID-19 cases such as fever, coronavirus , and pneumonia . Their study, along with similar social media–based early warning detection efforts [ 37 - 40 ], shows potential for the creation of a more effective yet affordable model to forecast new cases.…”

“…Furthermore, forecasting models have been created to track demand for ICU capacity planning in countries such as Chile [ 32 , 33 ], Brazil [ 34 ], Colombia [ 35 ], the United States [ 36 ], India [ 37 ], and China [ 38 ]. Previous studies have applied convergent cross-mapping (CCM) analysis to explore possible relationships involving antiepidemic measure–related tweets [ 39 ], the dynamics of misleading news on Twitter [ 40 ], and the identification of the global drivers of influenza [ 41 ]. However, to our knowledge, there are limited studies examining the potential of social media, particularly Twitter, to better understand hospital bed demand.…”

The intrinsic and extrinsic factors predicting fake news sharing among social media users: the moderating role of fake news awareness

“…Fake news, in the form of misinformation and disinformation, has received much attention and scrutiny among scholars and policy makers in recent years. This is because the spread of fake news is considered as an information disorder (Castioni et al, 2022 ; Wang et al, 2019 ) that has social, political, and economic implications. Likewise, policy makers have been looking for the solutions using various strategies such as developing news verification and fake news detection tools (Tandoc et al, 2018 ), enforcing strict laws and regulations (Roozenbeek & van der Linden, 2019 ) and promoting digital literacy or fake news awareness in society to counter fake news spread (Torres et al, 2018 ).…”