The DOI matches its Crossref record; 54 of the 61 references it cites were found in a registry or an index; none of the 18 sources its claims cite is checked for support yet.
Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics
Open accessPublished version of a preprint
Abstract
During COVID-19, governments and the public are fighting not only a pandemic but also a co-evolving infodemic—the rapid and far-reaching spread of information of questionable quality. We analysed more than 100 million Twitter messages posted worldwide during the early stages of epidemic spread across countries (from 22 January to 10 March 2020) and classified the reliability of the news being circulated. We developed an Infodemic Risk Index to capture the magnitude of exposure to unreliable news across countries. We found that measurable waves of potentially unreliable information preceded the rise of COVID-19 infections, exposing entire countries to falsehoods that pose a serious threat to public health. As infections started to rise, reliable information quickly became more dominant, and Twitter content shifted towards more credible informational sources. Infodemic early-warning signals provide important cues for misinformation mitigation by means of adequate communication strategies. An analysis of news shared on Twitter estimates the level of infodemic risk associated with COVID-19 across countries. Epidemic spread and infodemic risk co-evolve, with reliable information becoming more dominant as infection rates rise locally.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Analyzing more than 112 million Twitter messages in 64 languages from 21 January to 10 March 2020, the authors find that waves of unreliable and low-quality COVID-19 information often anticipate epidemic waves, while reliable information spreads after outbreaks hit. They introduce an Infodemic Risk Index and report that global infodemic risk tends to decline as epidemics expand, though country patterns vary substantially.
Key findings
- An estimated 40.4% of online messages during the period were due to automated agents (bots), doubling activity relative to estimates from only four years earlier.
- The authors extracted about 20.7 million links (3.3 million unique); about 50% of URLs were fact-checked by screening almost 4,000 expert-curated web domains, yielding a final database of 3,892 entries: 1,434 reliable, 2,264 unreliable, and 194 unknown.
- Infodemic waves of unreliable/low-quality information anticipate epidemic waves; when epidemics hit an area, reliable information is “inoculated” and focus shifts toward certified sources. The global Infodemic Risk Index tends to decrease as epidemics spread, with average risk on days with no confirmed cases at 0.276, a drop at first reported case, a rebound at 3 confirmed cases, then a decrease as cases grow.
- Country profiles differ: South Korea remains low-risk apart from an early spike; Canada is slightly higher; Venezuela is high-risk throughout; Russia shows erratic, pronounced spikes; Italy’s misinformation collapses after outbreak; and in the USA misinformation is mainly driven by verified sources and remains basically constant. The paper also finds G9 countries with remarkable infodemic risk and developing countries with far lower risk levels.
Methods
- Twitter messages were collected via the Filter API (24 Jan–10 Mar 2020) and Search API (21–24 Jan 2020) using hashtags/keywords including coronavirus, ncov, #Wuhan, covid19, covid-19, sarscov2, and covid. The authors estimate 161.2 million tweets were posted until 10 Mar 2020 and collected 112.6 million; more than 60 million geolocated messages containing more than 9 million news items were analyzed.
- Users were classified as human or non-human (bots) using a deep-learning algorithm with >90% accuracy and >95% precision for bots, while keeping verified/unverified distinctions. URLs were extracted and assigned reliability through fact-checking databases and a Harm Score scheme.
- The authors defined exposure, reliability/unreliability, and an Infodemic Risk Index (IRI) ranging from 0 to 1, aggregated daily and by country. Figure 3 considers 178 countries; Supplementary Fig. 5 uses 177 countries excluding China.
Limitations
- Paper-stated: only about 50% of URLs were fact-checked, and unassessable links were discarded; exposure is a lower bound because higher-order transmission pathways and users who read without resharing are not tracked; China was removed as a major outlier; the relationship with biological contagion patterns cannot be checked in some cases due to lack of reliable data; and whether the USA will show a similar readjustment remains to be checked.
- Paper-stated/evident: Twitter API restrictions after 25 Feb 2020 allowed no more than 4.5 million messages per day on average, so an increasing fraction of tweets was missed (161.2 million estimated posted vs. 112.6 million collected); geolocated analysis relies on about 56% of the total observed volume after geocoding profiles.
- Evident: source reliability inherits labels and Harm Scores from external databases and expert classification; the paper also notes that countries with high infodemic risk might be more unreliable in reporting epidemic data, which could alter perceptions and indirectly mislead people.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
Statistics
Verification
Claims the paper makes
38 Own result · 20 Own interpretation · 11 Own method · 10 Not checked yet · 3 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that waves of unreliable and low-quality information anticipate the epidemic ones, exposing entire countries to irrational social behavior and serious threats for public health.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“By using state-of-the-art machine learning techniques to analyze the online behavior of users (see Methods for details), we have discovered an extraordinary activity of automated agents, referred to as social bots 14,15,35,39 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [14] De Domenico, M., Granell, C., Porter, M. A. & Arenas, A. The physics of spreading processes in multilayer networks. Nat. Phys. 12, 901–906 (2016). source
- Not checked yetcites [15] Brockmann, D. & Helbing, D. The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013). source
- Not checked yetcites [35] Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002). source
- Not checked yetcites [39] Hébert-Dufresne, L., Scarpino, S. V. & Young, J.-G. Macroscopic patterns of interacting contagions are indistinguishable from social reinforcement. Nat. Phys. https://doi.org/10.1038/s41567-020-0791-2 (2020). source
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In the paper
“Contrary to what it could be expected in principle, what this natural experiment reveals is that, on the verge of a threatening global pandemic emergency due to SARS-CoV-2 [23][24][25] , human communication activity is to a significant extent characterized by the intentional production of informational noise and even of misleading or false information 26 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [26] Weekly Epidemiological Record Vol. 95, 16 (WHO, 2020); https://www.who.int/wer/2020/wer9516/en/
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We first compute the average value for days with no confirmed cases, which is 0.276”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Overall, the global level of infodemic risk tends to decrease as the epidemics spread globally”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the evolution of infodemic risk among countries with both high message volume and significant epidemic contagion tends to be very asymmetric”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, also the dynamic profiles of infodemic risk in countries with similar risk levels may be very different.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unverified users, both humans and bots, share a larger fraction of unreliable news than verified users.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This analysis demonstrates that reliable sources are more represented than unreliable ones”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, profiles identified as bots have a larger number of followers.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 36
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When the epidemics hit the same area, reliable information is quickly inoculated, like antibodies, and the system shifts focus towards certified informational sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Specifically, we estimate that 40.4% of online messages during this period were due to such automated agents, doubling the activity with respect to estimates of only four years ago 35 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [35] Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002). source
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The average values are: 660 for Unverified Humans (circles), 1400 for Unverified Bots (squares, 51k for Verified Humans (stars) and 240k for Verified Bots (diamonds).”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have found a total of 4,988 domains, reduced to 4,417 after removing hard duplicates across databases.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the contagion spreads to significant levels, the infodemic risk further decreases, signalling an increasing focus of the public opinion toward reliable news sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgeddirectSingle case- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All distributions display a fat-tail, but different categories of users have a different outreach.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“About 0.84% of collected posts were geo-tagged by the user, providing highly accurate information about their geographic location.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Canada presents a slightly higher level of infodemic risk, and unlike South Korea, we see that the risk level increases as the epidemics spread, but stays at low levels.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The difference, possibly caused by the correlation of this kind of behaviour with the choice of how customising the field”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the opposite, in a high-risk country such as Venezuela, the infodemics is in full swing throughout the period of observation, and in addition to the expected activity from unverified sources one notices that even verified ones contribute to a large extent to the infodemics.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Fig. 4 shows, however, that the relationship between reduction of infodemic risk and expansion of the epidemics seems to be a rather general trend, as the relationship between number of confirmed cases and infodemic risk is (nonlinearly) negative, confirming the result shown in Fig. 3.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“with major roles played not only by countries such as Iran, but also United States, Germany, the Netherlands, Austria and Norway maintaining their relative levels, and other countries like Italy, South Korea and Japan significantly reducing it with the progression of the epidemics.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“This phase of processing reduced the overall database to unique 3,920 domains.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Type Count RELIABLE 1434 UNRELIABLE 2264 UNKNOWN 194 Total 3892”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For the USA, misinformation is mainly driven by verified sources, and it remains basically constant even after the epidemics outbreak.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe that reporting of the first case is associated with a drop in the risk index, followed by an increase back to the”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelationaln = 3- Asserted: passes — The authors put it forward as true.
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In the paper
“We can observe a first increase in collective attention after the outbreak in Wuhan, China (between 24 Jan and 02 Feb 2020) and a second strong rise after the epidemics began to spread in northern Italy (20 Feb 2020 onwards).”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
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In the paper
“In the case of Italy the risk is mostly due to the activity of unverified sources, but we notice that with the outbreak of the epidemics, the production of misinformation literally collapses and there is a sudden shift to reliable sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In fact, we find that there are G9 countries with remarkable infodemic risk and developing countries with far lower risk levels.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Here, we have shown that in the context of the COVID-19 crisis, complex infodemic effects are indeed at work, with significant variations across countries, where level of socio-economic development is not the key discriminant to separate countries with high vs. low infodemic risk.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
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In the paper
“The fraction of Geoolocated (messages with shared locations, or geonamed, indicated in green) is constantly about 56% of the total volume recorded”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
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In the paper
“Finally, in a relatively high-risk country such as Russia we notice that infodemic risk is erratic with sudden, very pronounced spikes, and again also verified sources play a major role.Here too, information about the epidemics is fragmented and mostly unreliable.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This action reduced the database to the final number of 3,892 entries”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“About 50% of URLs have been fact-checked by screening almost 4,000 expert-curated web domains, whereas the remaining corpus was pointing to disappeared web pages or to content not classifiable automatically (eg, videos on YouTube) and unpopular sources.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The reliability of a URL can be inherited from the reliability of its source domain.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As users are repeatedly hit by a given message from different sources, this works as an indirect validation of its reliability and relevance, leading the user to spread it in turn, and to become an informationally infectious agent.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Contrary to mainstream beliefs, we show that human response to falsehood exhibits early-warning signals that might be mitigated with adequate communication strategies.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Recently, it has been suggested that this challenging phenomenon might be due to the fact that, at population level, the dynamics of multiple interacting contagions are indistinguishable from social reinforcement 38 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [38] Stella, M., Cristoforetti, M. & De Domenico, M. Influence of augmented humans in online interactions during voting events. PLoS ONE 14, e0214210 (2019). source
hedged- Asserted: fails — The authors may not put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13,14 or inflammatory 15 content, a phenomenon named infodemics [16][17][18][19] , sharing similarities with more traditional epidemics and spreading phenomena [20][21][22] .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [12] Zarocostas, J. How to fight an infodemic. Lancet 395, 676 (2020). source
- Not checked yetcites [13] Pastor-Satorras, R., Castellano, C., Van Mieghem, P. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925–979 (2015). source
- Not checked yetcites [14] De Domenico, M., Granell, C., Porter, M. A. & Arenas, A. The physics of spreading processes in multilayer networks. Nat. Phys. 12, 901–906 (2016). source
- Not checked yetcites [15] Brockmann, D. & Helbing, D. The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013). source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Fig. 1, an infodemics is the result of the simultaneous action of multiple human and non-human sources of fake or unreliable news.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In fact, the dangerousness of infodemics can compare and sum up to a large extent to that of the epidemics itself 29 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [29] Ferrara, E., Varol, O., Davis, C., Menczer, F. & Flammini, A. The rise of social bots. Commun. ACM 59, 96–104 (2016). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Perceived and actual biological and social risks feed upon one another, and may co-evolve in complex ways.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Human and non-human accounts forge unreliable content -such as fake or untrustworthy news -about the COVID19, a topic attracting the collective attention of the whole world.Their followers are exposed to such content, and reshare it, becoming infectious agents:”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“More in detail, our method has the highest accuracy (>90%) and precision in identifying bots (>95%) when compared with state-of-the-art methods.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Human vs non-human classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“suggesting that evidence of the expansion of the contagion leads people to look for relatively more reliable sources, and that verified influencers with many followers started inoculating the system with more reliable news (see Supplementary Figures 3 and 4), playing a role that presents interesting analogies to that of antibodies in the treatment of an infectious disease.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Especially in situations where effective therapies to contrast the diffusion of the pandemic are not readily available, coordination of behaviors and diffusion of pro-social orientations driven by reliable information at all scales are the key resources for the mitigation of adverse effects.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We estimate that until 24 Feb 2020 we lost about 60,000 tweets out of millions, capturing more than 99.5% of all messages posted (see Supplementary Fig. 1).”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is also interesting to observe though that countries with high infodemic risk might also be more unreliable in terms of reporting of epidemic data, thus altering the perceptions of people and indirectly misleading them in their search for reliable information.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The escalation of the epidemics leads people to progressively pay attention to more reliable sources thus potentially limiting the impact of the infodemics, but the actual speed of adjustment may make a major difference in determining the social outcome, and in particular between a controlled epidemics and a global pandemics.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“IRI is robust to user classification, making it an indicator not sensitive to performances of bot detection algorithms.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have collected manually-checked web domains from multiple publicly available databases, including scientific and journalistic ones.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Data availability
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Note that this choice is the safest as to date: in fact, it has been recently shown that biases affecting Sample API (which samples data based on rate limits), for instance, are not found in REST and Filter APIs 40 .”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
What the sources say
- Not checked yetcites [40] Eysenbach, G. How to fight an infodemic: the four pillars of infodemic management. J. Med. Internet Res. 22, e21820 (2020). source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This casts new light on the social mechanics of the infodemics-epidemics interaction, and may be of help to policy makers to design a more integrated strategic approach, by suitably embedding communication and information management into a comprehensive, extended public health perspective.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We have estimated a total of 161.2 millions tweets posted until 10 Mar 2020: we have successfully collected 112.6 millions of them, providing an unprecedented opportunity for infodemics analysis.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“however, they circulate in different ways and reach different targets, a feature that is perfectly captured by the infodemic risk index introduced in this study.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The effect is particularly pronounced with the escalation of the epidemics, suggesting that this effect could be mediated by levels of perceived social alarm.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“When two or more domains are soft duplicates, we keep the classification with the highest Harm Score, as a conservative choice.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
Verified users are more authentic than unverified users because Twitter applies strict verification criteria.
“In fact, verified users should be considered as more authentic than unverified ones, because Twitter makes use of strict criteria for verification.”
The paper relies on this without support.
Twitter messages are a valid proxy for global human communication activity during the pandemic.
“We focus our attention on the analysis of messages posted on a popular microblogging platform 30 , an online social network characterized by heterogeneous connectivity 31 and topological shortcuts typical of small-world systems 32 .”
The paper relies on this without support.
The reliability of a URL can be inherited from the reliability of its source domain.
“Each URL is therefore used for fact-checking, inheriting the reliability of its source (see Methods).”
The paper relies on this without support.
The machine learning classifier used to distinguish human from bot accounts is accurate.
“we have used an accurate machine learning approach to classify the author as human or non-human (i.e., bot)”
The authors state this as an assumption.
Exposure can be approximated by the number of followers of the posting user at first order.
“The number of followers K u of a single user u defines the exposure, in terms of potential visualizations at first-order approximation, of a single message m posted by user u at time t .”
The paper relies on this without support.
The geolocated subset of tweets is representative enough of the full Twitter sample for estimating infodemic risk.
“The fraction of Geoolocated (messages with shared locations, or geonamed, indicated in green) is constantly about 56% of the total volume recorded”
The authors state this as an assumption.
Message reliability can be adequately represented as a binary value after discarding links that cannot be easily assessed.
“The reliability r m of a single message m is either 0 or 1, because we discard all web links that can not be easily assessed”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Similar results were obtained about the presence of bots in online cryptocurrency discussions [24] and as part of the 'infodemics' about the COVID-19 pandemic [14].”
From A decade of social bot detection (2020) · cites this paper as [14]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Additional information
“Reliable news includes MSM and SCIENCE, whereas unreliable news includes the remaining categories.”
§ Assessing the risks of "infodemics" in response to COVID-19 epidemics > Category
“OTHER 5 UNKNOWN Domains pointing to general content that can not be easily classified, such as videos on YouTube.SHADOW 6 UNKNOWN Domains related to URL shortening, that can be classified a priori .”
Citations
Cites
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Collective dynamics of ‘small-world’ networks
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“We focus our attention on the analysis of messages posted on a popular microblogging platform ( 25 ) , an online social network characterized by heterogeneous connectivity ( 26 ) and topological shortcuts typical of small-world systems ( 27 ) .”
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The spread of true and false news online
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“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13, 14 or inflammatory 15 content, a phenomenon named infodemics [16] [17] [18] [19] , sharing similarities with more traditio…”
BackgroundWhat is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Catastrophic cascade of failures in interdependent networks
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“(1) Note that different users of the same class might have overlapping social neighborhoods: those neighbors might be reached multiple times by the messages coming from distinct users of the same class, therefore our measure of exposure accounts for this effect.”
BackgroundLazer, D. M. J., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., Metzger, M. J., Nyhan, B., Pennycook, G., Rothschild, D., et al. (2018) · Science · doi:10.1126/science.aao2998 · Cited by 4,160 · cited 1×
“However, contrary to what it could be expected in principle, what this natural experiment reveals is that, on the verge of a threatening global pandemic, human communication activity is to a significant extent characterized by the intentional production of informational noise and even of misleading or false informatio…”
ResultBenkler, Y. R. (2017) · Yale University Press eBooks · doi:10.12987/9780300127232 · Cited by 3,728
Epidemic processes in complex networks
Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015) · Reviews of Modern Physics · doi:10.1103/revmodphys.87.925 · Cited by 3,697 · cited 1×
“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13, 14 or inflammatory 15 content, a phenomenon named infodemics [16] [17] [18] [19] , sharing similarities with more traditio…”
Background
Cited by
Sorted by citations
Items 201–300 of 587
Infodemic: Challenges and solutions in topic discovery and data process
Zhang, J., Pan, Y., Lin, H., Sun, Z., Wu, P., & Tu, J. (2023) · Archives of Public Health · doi:10.1186/s13690-023-01179-z · Cited by 11
“However, it can be extended to other events by searching with keywords such as “COVD19, Coronavirus Pandemic, COVID-19, 2019 ncov Corona Outbreak, coronavirus, Wuhan Virus " and so forth [32].”
BackgroundMethodEmotions and Reputation Learning by Audience Networks: A Research Agenda in Bureaucratic Politics
Maor, M., Rimkutė, D., & Capelos, T. (2025) · Public Administration Review · doi:10.1111/puar.70004 · Cited by 16
“This misalignment damaged credibility, fueled public confusion, and contributed to the “infodemic”—an epidemic of information (Gallotti et al. 2020).”
Political polarization on Twitter during the COVID-19 pandemic: a case study in Brazil
Brum, P. P. V., Teixeira, M. C., Vimieiro, R., Araújo, E. F. D. M., Meira, W., & Pappa, G. L. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00949-x · Cited by 15
“The vast majority of the literature in social media and the COVID-19 pandemic analyze how socioeconomic and social media data relates to public health and the political scenario of different countries worldwide (Gallotti et al. 2020; Graham et al. 2020; Charron et al. 2020).”
Background(Des)infodemia: lecciones de la crisis de la covid-19
Quian, A. (2023) · Revista de Ciencias de la Comunicación e Información · doi:10.35742/rcci.2023.28.e274 · Cited by 12
Measuring the impacts of quantity and trustworthiness of information on COVID ‐19 vaccination intent
Park, M. S., Park, J., Kim, H., Lee, J. H., & Park, H. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24760 · Cited by 12
“In fact, the mainstream literature regarding the infodemic has confirmed that exposure to misleading, inaccurate, or false information negatively influences people's COVID-19 vaccination intent by generating mistrust about vaccines (Bheekhun et al., 2021; Dror et al., 2020; Gallotti et al., 2020; Moore et al., 2021).”
BackgroundSocial status and novelty drove the spread of online information during the early stages of COVID-19
Photiou, A., Nicolaides, C., & Dhillon, P. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-99060-y · Cited by 12
Topic Modelling of Croatian News During COVID-19 Pandemic
Bogović, P. K., Meštrović, A., Beliga, S., & Martinčić-Ipšić, S. (2021) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro52101.2021.9597125 · Cited by 12
Si, M., Su, X., Jiang, Y., Wang, W., Gu, X., Ma, L., Li, J., Zhang, S., Ren, Z., Liu, Y., et al. (2021) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580211059953 · Cited by 17
Of seekers and nonseekers: Characteristics of Covid‐19‐related information‐seeking behaviors
Link, E., Baumann, E., Czerwinski, F., Rosset, M., & Suhr, R. (2022) · World Medical & Health Policy · doi:10.1002/wmh3.509 · Cited by 13
Ability to detect fake news predicts sub-national variation in COVID-19 vaccine uptake across the UKPreprint
Loomba, S., Maertens, R., Roozenbeek, J., Götz, F. M., van der Linden, S., & de Figueiredo, A. (2023) · medRxiv · doi:10.1101/2023.05.10.23289764 · Cited by 11
“Second, since information is often transmitted between socially connected individuals, misinformation susceptibility is likely mediated by networked information ecosystems that individuals are a part of [10, 17], which requires a methodology that can account for any network effects.”
BackgroundGioia, F., Imperato, C., Boursier, V., Franceschini, C., Schimmenti, A., & Musetti, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-30531-0 · Cited by 14
Mechanisms Driving Online Vaccine Debate During the COVID-19 Pandemic
Seckin, O. C., Atalay, A., Otenen, E., Duygu, U., & Varol, O. (2024) · Social Media + Society · doi:10.1177/20563051241229657 · Cited by 19
“…anti-vaccine propaganda was mostly supported with false narratives and conspiracies, and the prevalence of anti-vaccine misinformation was conspicuous on social media platforms (Cha et al., 2021; Ferrara, 2020a; Gallotti et al., 2020; Gruzd et al., 2021; S. K. Lee et al., 2022; Singh et al., 2022).”
BackgroundSentiment analysis of the COVID-19 vaccine perception
Park, B., Jang, I. S., & Kwak, D. (2024) · Health Informatics Journal · doi:10.1177/14604582241236131 · Cited by 11
Xu, R. H., & Chen, C.-S. (2024) · Journal of Medical Internet Research · doi:10.2196/53417 · Cited by 11
“An Italian study suggested developing early warning signals for an infodemic, which can provide important cues for implementing effective communication strategies to mitigate misinformation [13].”
Kuo, H.-Y., & Chen, S. (2025) · Journal of Medical Internet Research · doi:10.2196/65631 · Cited by 13
Unmasking deception: detection of fake profiles in online social ecosystems
Sarfraz, A., Ahmad, A., Zeshan, F., Hamid, M., & Alshalali, T. A. N. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01254-y · Cited by 13
Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19
Roitero, K., Soprano, M., Portelli, B., Luise, M. D., Spina, D., Mea, V. D., Serra, G., Mizzaro, S., & Demartini, G. (2021) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01604-6 · Cited by 23
“There is significant effort on analyzing COVID-19 information on social media, and linking to data from external fact-checking organizations to quantify the spread of misinformation [9, 19, 69].”
BackgroundRemoving AI’s sentiment manipulation of personalized news delivery
Wu, C., Wu, F., Qi, T., Zhang, W.-Q., Xie, X., & Huang, Y. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01473-1 · Cited by 12
Watch ’n’ Check: Towards a Social Media Monitoring Tool to Assist Fact-Checking Experts
Cerone, A., Naghizade, E., Scholer, F., Mallal, D., Skelton, R., & Spina, D. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00085 · Cited by 12
“from external fact-checking organizations to quantify the spread of misinformation [2], [28]–[31].”
Libotte, G. B., dos Anjos, L., Almeida, R. C. C., et al. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0275 · Cited by 11
“Political polarization and the spread of fake news also hamper the fight against COVID-19 and the adoption of non-pharmaceutical interventions (NPI) [16,17].”
BackgroundReferences to unbiased sources increase the helpfulness of community fact-checks
Solovev, K., & Pröllochs, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-09372-6 · Cited by 19
“Among other instances, negative effects of misinformation have been repeatedly observed in the contexts of public safety [1, 2, 3, 4], public health [5,6,7], and elections [8,9,10].”
Cheng, Z., Ruktanonchai, N., Wesolowski, A., Pei, S., Wang, J., Cockings, S., Tatem, A. J., & Lai, S. (2025) · Infectious Diseases of Poverty · doi:10.1186/s40249-025-01378-6 · Cited by 15
“The COVID-19 pandemic has reaffirmed these findings, with scholars documenting a surge in fake news and rumours concurrent with pathogens spread, leading to the coinage of “inf-odemic” to describe the proliferation of misinformation [24, 25].”
Detecting coordinated and bot-like behavior in Twitter: the Jürgen Conings case
Clerck, B. D., Toledano, J. C. F., Utterbeeck, F. V., & Rocha, L. E. C. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00477-y · Cited by 12
“An Infodemic Risk Index was devised to quantify the extent to which nations are exposed to unreliable news [16].”
BackgroundEspinoza, B., Saad-Roy, C. M., Grenfell, B. T., Levin, S. A., & MARATHE, M. V. (2024) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2024.1772 · Cited by 10
He, P., He, C., Wu, F., Ou, Y., Luo, S., Zhang, Y., Chang, Y., Guo, Z., Tang, X., Zhao, Y., et al. (2025) · Journal of Controlled Release · doi:10.1016/j.jconrel.2025.01.069 · Cited by 13
Infodemics during era of COVID-19 pandemic: A review of literature
Sharma, L., Joshi, K. J., Acharya, T. A., Dwivedi, M. G., & Sethy, G. B. (2022) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_2446_21 · Cited by 13
“[15] monitored Twitter activity and collected more than 112 million messages using a selection of words commonly used about COVID‑19 during the pandemic.”
MethodVaccinEU: COVID-19 Vaccine Conversations on Twitter in French, German and Italian
Giovanni, M. D., Pierri, F., Torres-Lugo, C., & Brambilla, M. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19374 · Cited by 10
“Specifically to the COVID-19 pandemic, concern has recently risen around the ’infodemic’ (Zarocostas 2020; Yang et al. 2021; Gallotti et al. 2020) of misleading information about the virus spreading online, and it has been shown that online misinformation might negatively influence individuals’ opinion towards getting…”
BackgroundHeyerdahl, L. W., Marcis, F. L., Nguyen, T., Alenichev, A., Camara, B. S., & Grietens, K. P. (2023) · Critical Public Health · doi:10.1080/09581596.2023.2245964 · Cited by 10
“Additionally, language barriers, along with subjective keyword choices, may exclude certain population segments (Gallotti et al., 2020), further limiting the representativeness of online social listening efforts.”
BackgroundEmotion-based epidemic dynamics coupling information with diseases in multiplex networks
You, X., Zhang, M., & Ma, Y. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-08793-0 · Cited by 10
“Taking COVID-19 as an example, the Internet became an important channel for people to seek health-related information during the outbreak [51].”
BackgroundPublic Perception of the German COVID-19 Contact-Tracing App Corona-Warn-App
Beierle, F., Dhakal, U., Cohrdes, C., Eicher, S., & Pryss, R. C. (2021) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms52027.2021.00031 · Cited by 10
“There has been work analyzing the spread of misinformation [9] and conspiracy theories [10], [11] during the COVID-19 pandemic.”
BackgroundExperts vs. policymakers in the COVID-19 policy response
Antoci, A., Sabatini, F., Sacco, P. L., & Sodini, M. (2022) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2022.06.031 · Cited by 10
Self-citationCovering the Crisis: Evolution of Key Topics and Actors in COVID-19 News Coverage in Switzerland
Ort, A., Rohrbach, T., Diviani, N., & Rubinelli, S. (2023) · International Journal of Public Health · doi:10.3389/ijph.2022.1605240 · Cited by 10
“Moreover, the fact that the analysis yielded a daily average of roughly one thousand news articles related to the pandemic raises the question of at what point necessary reporting turns into an “infodemic” overwhelming the audience with too much information [14].”
BackgroundDoctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers
Ahamed, S. H. R., Shakil, S., Lyu, H., Zhang, X., & Luo, J. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020853 · Cited by 10
“In the era of the COVID-19 pandemic, social media data has been leveraged by researchers for various purposes [15] such as analyzing public attitudes [2], [16], identifying infodemics [4], [17], assessing mental health [18], [19], and so on.”
BackgroundCommunity notes reduce the spread of misleading posts on XPreprint
Chuai, Y., Pilarski, M., Lenzini, G., & Pröllochs, N. (2024) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/3a4fe · Cited by 13
Editorial: The infodemic, young consumers and responsible stakeholdership
Ting, H., Gong, J., Cheah, J., et al. (2024) · Young Consumers Insight and Ideas for Responsible Marketers · doi:10.1108/yc-04-2024-2059 · Cited by 10
Generalized Local Prominence for Source Detection in Real-World Rumor Networks
Ali, S. S., Rastogi, A., Anwar, T., Rizvi, S. A. M., Yang, J., Wu, J. X., & Sheng, Q. Z. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3567282 · Cited by 12
“To understand the risks of infodemics [34] related to COVID-19, [36] developed an index called Infodemic Risk Index .”
Song, J., Li, W., Xiao, Y., Chen, L., Yang, C., Qi, L., & Wang, W. (2025) · Applied Mathematics and Computation · doi:10.1016/j.amc.2025.129725 · Cited by 10
Misinformation does not reduce trust in accurate search results, but warning banners may backfire
Williams‐Ceci, S., Macy, M. W., & Naaman, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-61645-8 · Cited by 13
“10 of the Supplementary Information for details on the power analysis and limitations).”
BackgroundBulai, I. M., Sensi, M., & Sottile, S. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.115104 · Cited by 9
“…information based behaviours allows for more complex dynamics to be described, analysed and simulated, [10, 11] and it is especially relevant in recent years, in which public opinion on COVID-19 and the corresponding containment measures reflected on the evolution of the pandemic, [12, 13, 14].”
BackgroundWho Shares Fake News? Uncovering Insights from Social Media Users’ Post Histories
Schoenmueller, V., Blanchard, S. J., & Johar, G. V. (2024) · Journal of Marketing Research · doi:10.1177/00222437241281873 · Cited by 20
“The focus on the users ’ role in propagating fake news is crucial, as such content can go from friends to strangers within seconds on social media (Gallotti et al. 2020).”
Kim, J., Wang, Z., Shi, H., Ling, H.-K., & Evans, J. A. (2025) · Nature Communications · doi:10.1038/s41467-025-55868-0 · Cited by 8
“Political diversity measures whether a poster ’ s tweet cites a source with opposing political stance(e.g.,a right-leaning poster references left-leaning articles) 5,47 .”
Epidemic spreading of two interacting diseases with mass media in multiplex networks
Wu, Y., Zhao, D., & Xia, C. (2025) · Nonlinear Dynamics · doi:10.1007/s11071-025-10883-0 · Cited by 8
A model for predicting factors affecting health information avoidance on WeChat
Chen, M., Huang, X., Wu, Y., Song, S., & Qi, X. (2025) · Digital Health · doi:10.1177/20552076251314277 · Cited by 9
“120,121 In contrast, passive avoidance, often associated with negative emotions, involves health information that might bring about cognitive con fl icts, cognitive overload, comprehension dif fi culties, or social pressures.”
Liu, L., Cai, C.-R., Zhang, S.-P., et al. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.130534 · Cited by 9
“As previously stated, human communication often re-sults in the generation of a considerable amount of information noise, misinformation, and even false information [7, 45].”
Reflections from the COVID-19 pandemic in Germany: lessons for global health
Wieler, L. H., Antão, E.-M., & Hanefeld, J. (2023) · BMJ Global Health · doi:10.1136/bmjgh-2023-013913 · Cited by 9
Uyurdağ, N., Eskicioğlu, G., Aksu, S., & Soyata, A. Z. (2020) · Anadolu Kliniği Tıp Bilimleri Dergisi · doi:10.21673/anadoluklin.783596 · Cited by 12
“Bunlar arasında en önemlisi sosyal medya ve haberlerde gözlenen infodemi etkisidir (5).”
BackgroundInfluentialJohansen, N., Marjanović, S. V., Kjær, C. V., Baglini, R., & Adler‐Nissen, R. (2022) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-93 · Cited by 8
“Most studies of misinformation during the pandemic focus on the disseminators (Caldarelli et al., 2021; Cinelli et al., 2020; Gallotti et al., 2020), while few scholars have explored how citizens combat false information online (Abidin, 2020; Micallef et al., 2020; Pulido et al., 2020).”
BackgroundOnline information seeking during the COVID-19 pandemic: A cross-country analysis
Khosrowjerdi, M., Fylking, C. B., & Zeraatkar, N. (2023) · IFLA Journal · doi:10.1177/03400352221141466 · Cited by 9
“Gallotti et al. (2020) stated that online rumours and misinformation on social media before an increase in COVID-19 cases could constitute a severe threat to public health.”
BackgroundZhang, X., Fu, J., Hua, S., Liang, H., & Zhang, Z. (2023) · Nonlinear Dynamics · doi:10.1007/s11071-023-08427-5 · Cited by 11
“Consequently, low-quality information is likely to intrigue panic and irrational behaviors that rise up the infection risk[49].”
BackgroundA Multi-Platform Collection of Social Media Posts about the 2022 U.S. Midterm Elections
Aiyappa, R., DeVerna, M., Pote, M., Truong, B. T., Zhao, W., Axelrod, D., Pessianzadeh, A., Kachwala, Z., Kim, M., Seckin, O. C., et al. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22205 · Cited by 10
“For instance, some research mines social media data to learn how misinformation spreads during elections (Shao et al. 2018a; Grinberg et al. 2019) and pandemics (Pierri et al. 2023a; Gallotti et al. 2020) and how polarization emerges in digital communities (Waller and Anderson 2021).”
Stella, M. (2020) · Systems · doi:10.3390/systems8040038 · Cited by 12
Comprehending Covidiocy Communication
Spitzberg, B. H. (2021) · Wiley eBooks · doi:10.1002/9781119751809.ch2 · Cited by 13
Misinformation about COVID-19: Psychological Insights
Anastasiades, E., Argyrides, M., & Mousoulidou, M. (2021) · Encyclopedia · doi:10.3390/encyclopedia1040091 · Cited by 10
Online misinformation is linked to COVID-19 vaccination hesitancy and refusal (Preprint)Preprint
Pierri, F., Perry, B. L., DeVerna, M., Yang, K.-C., Flammini, A., Menczer, F., & Bryden, J. (2021) · doi:10.2196/preprints.32237 · Cited by 9
The complex interplay between risk tolerance and the spread of infectious diseases
Nguyen, M., Freedman, A. S., Cheung, M. A., Saad-Roy, C. M., Espinoza, B., Grenfell, B. T., & Levin, S. A. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0486 · Cited by 8
“Compounding the issue were the multiple streams of information and potential misinformation spread through social media and other channels [11–14].”
Adaptive human behavior and delays in information availability autonomously modulate epidemic waves
Mahmud, M. S., Eshun, S., Espinoza, B., & Kadelka, C. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf145 · Cited by 10
Maniou, T. A., & Papa, V. (2023) · Communication & Society · doi:10.15581/003.36.1.35-46 · Cited by 9
Ali, S. M. A., & Sherman‐Morris, K. (2023) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2023.100739 · Cited by 13
Canada’s response to international travel during COVID-19 pandemic – a media analysis
Reddy, K. S., Mithani, S. S., Wilson, L. A., et al. (2021) · BMC Public Health · doi:10.1186/s12889-021-11082-3 · Cited by 8
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter
Pranesh, R. R., Farokhenajd, M., Shekhar, A., & Solar, G. V. (2021) · doi:10.18653/v1/2021.acl-srw.28 · Cited by 8
“Unfortunately, the fake news spread faster than the virus (Gallotti et al., 2020).”
BackgroundO’Neil, M., Khan, M. I., Holland, K., & Cai, X. (2022) · Information Communication & Society · doi:10.1080/1369118x.2022.2032260 · Cited by 9
“Gallotti et al. (2020) classified the reliability of news being circulated on social media, analysing more than 100 million Twitter messages posted worldwide during the early stages of the pandemic’s spread, from 22 January to 10 March 2020.”
BackgroundZelič, Ž., Berič, M., & Grum, D. K. (2022) · Studia Psychologica · doi:10.31577/sp.2022.01.844 · Cited by 10
Ein neues Verständnis von Risikokommunikation in Public-Health-Notlagen
Dickmann, P., & Strahwald, B. (2022) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-022-03529-8 · Cited by 8
Preferences of COVID-19 Vaccines in the General Population in Belgrade, Serbia: A Qualitative Study
Gazibara, T. M., Cvjetković, S., Milić, M., et al. (2022) · Behavioral Medicine · doi:10.1080/08964289.2022.2085652 · Cited by 8
Buarque, B. (2022) · Social Epistemology · doi:10.1080/02691728.2022.2040636 · Cited by 8
Limit your body area -a COVID-19 mass radicalisation challenging autonomy and basic human rights
Glasdam, S., & Stjernswärd, S. (2021) · International Journal of Human Rights in Healthcare · doi:10.1108/ijhrh-07-2020-0055 · Cited by 8
The COVID-19 Infodemic: Twitter versus FacebookPreprint
Yang, K.-C., Pierri, F., Hui, P.-M., Axelrod, D., Torres-Lugo, C., Bryden, J., & Menczer, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09353 · Cited by 8
“A similar delay was recently reported between peaks of exposure to Infodemic tweets and of COVID-19 cases in different countries (Gallotti et al., 2020).”
BackgroundInfluentialMulti-stage self-training social bot detection based on graph neural network
Wang, K., Wang, X., Chen, K., Wang, Z., & Zheng, K. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110816 · Cited by 8
VaccinItaly: monitoring Italian conversations around vaccines on Twitter
Pierri, F., Pavanetto, S., Brambilla, M., & Ceri, S. (2021) · Europe PMC (PubMed Central) · Cited by 7
Tonkin, E., Pillen, H., Meyer, S. B., Ward, P., Beard, C., Toson, B., Coveney, J., Henderson, J., Webb, T. J., McCullum, D., et al. (2022) · SSM - Qualitative Research in Health · doi:10.1016/j.ssmqr.2022.100099 · Cited by 7
“The so-called ‘infodemic’ of COVID-19 (Gallotti et al., 2020; Tangcharoensathien et al., 2020) obviously needs to be taken into account when trying to work with population groups to undertake testing or comply with COVID-19 regulations, since information provided may well differ from the complex and confusing tidal wa…”
Pierri, F. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00501-1 · Cited by 9
A SWOT analysis of smart governance applications amid the COVID-19 pandemic
Jiang, H. (2022) · Information Polity · doi:10.3233/ip-220030 · Cited by 7
Schawe, H., Beiró, M. G., Alvarez-Hamelin, J. I., Kotzinos, D., & Hernández, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-30367-8 · Cited by 9
“Special attention has also been given to the diffusion of rumours and fake news in relation to the COVID-19 pandemic 12 , to the extent that the term infodemics was coined to highlight the parallelism with the diffusion of the virus 13–15 .”
BackgroundGenerational effects of culture and digital media in former Soviet Republics
Horne, B., Rice, N., Luther, C. A., Ruck, D. J., Borycz, J., Allard, S., FitzGerald, M., Manaev, O., Prins, B. C., Taylor, M., et al. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-01670-6 · Cited by 6
Endemic infectious states below the epidemic threshold and beyond herd immunity
Aguilar, J., García, B. A., Toral, R., Meloni, S., & Ramasco, J. J. (2023) · Communications Physics · doi:10.1038/s42005-023-01302-0 · Cited by 8
“…of understanding other aspects of infectious diseases spreading such as: the effect of non-pharmaceutical interventions [5–7], long-range travel restrictions [4], the predictability of epidemic models [8], the impact of city structure [9, 10] and their effect on public opinion [11–13], etc.”
Nejat, P., Heirani-Tabas, A., & Nazarpour, M. M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1201695 · Cited by 12
“This can lead to their higher exposure to COVID conspiracy beliefs, especially in the context of Iran’s high Infodemic Risk Index (Gallotti et al., 2020), and result in higher endorsement of these beliefs as a consequence.”
BackgroundInfluentialEfficient network immunization strategy based on generalized Herfindahl–Hirschman index
Chen, P., Qi, M., Lu, X. B., et al. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac05e0 · Cited by 7
Andreu-Sánchez, C., & Martín-Pascual, M. Á. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253738 · Cited by 7
“This latter group has become vulnerable to ‘infodemics’ [21] and as such, we were interested in analysing the general public’s perception of the scientific representation of coronavirus images.”
Lisi, M. (2023) · Royal Society Open Science · doi:10.1098/rsos.230417 · Cited by 11
“:230417 2 accompanied by an ‘infodemic’ [1,2]—a rapid spread of false or questionable information, growing and evolving in parallel with the epidemic [3].”
BackgroundWITHDRAWN: Predicting the effect of Covid-19 by using artificial intelligence: A case study
Sucharitha, G., & Chary, D. V. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.02.202 · Cited by 7
“For example, social media research provides some insights throughout environmental changes and distractions all over the infection and therefore its social; and cultural consequences [17].”
RetractedEditorial: COVID-19 and Behavioral Sciences
Jackson, T., Steed, L., Pedruzzi, R. A., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2021.830797 · Cited by 7
“The infodemic is recognized to be a key contributor to the behavioral response to COVID-19, and may have fuelled vaccine hesitancy, due to misinformation and disinformation spread through media, social media and online sources (3).”
BackgroundInvestigating Fake and Reliable News Sources Using Complex Networks Analysis
Mazzeo, V., & Rapisarda, A. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.886544 · Cited by 6
Yu, P.-D., & Tan, C. W. (2025) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2025.3527755 · Cited by 6
“It became the first pandemic in history that triggered an epidemic of online misinformation , which significantly undermined the efficacy of online social networks and disrupted public health risk communications [5], [6], [7].”
Álvarez‐Gálvez, J., Lagares, C., Ortega‐Martin, E., Sola, H. D., Rojas‐García, A., Sanz‐Marcos, P., Barrios, J. A., Kassianos, A. P., Montagni, I., Camacho-García, M., et al. (2025) · JMIR Infodemiology · doi:10.2196/69945 · Cited by 6
Bailo, F., Johns, A., & Rizoiu, M.-A. (2023) · Information Communication & Society · doi:10.1080/1369118x.2023.2205479 · Cited by 23
“…al., 2020; Shahi et al., 2021) – with some scholars pre-ferring approaches where misinformation content is veri fi ed by fact-checking experts (see Gallotti et al., 2020) – it is accepted as an approach which allows automatic measurement of the credibility of information across large datasets…”
BackgroundMethodInfluentialDecoding the News Media Diet of Disinformation Spreaders
Bertani, A., Mazzeo, V., & Gallotti, R. (2024) · Entropy · doi:10.3390/e26030270 · Cited by 6
“Building on the approach outlined in [5], we classified news sources as reliable (belonging to the Science or Mainstream Media categories), low-risk (Satire, Clickbait, Political, Other), or unreliable (Fake or Hoax, Conspiracy or Junk science).”
BackgroundMethodInfluentialSelf-citationMasele, J. J. (2024) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2024.2324527 · Cited by 6
Dong, J., Hao, M., Ding, F., Chen, S., Wu, J., Zhuo, J., & Jiang, D. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9030063 · Cited by 6
Caliskan, C. (2021) · Journal of Computational Social Science · doi:10.1007/s42001-021-00111-1 · Cited by 6
Physicians’ Role in the COVID-19 Infodemic: A Reflection
Blankenship, S. B., Nakano‐Okuno, M., & Zhong, R. (2021) · Southern Medical Journal · doi:10.14423/smj.0000000000001327 · Cited by 6
Debunking Misinformation and Communicating Critical Events in Vaccine Trials
Memenga, P., Eitze, S., Shamsrizi, P., Addo, M. M., & Betsch, C. (2022) · European Journal of Health Communication · doi:10.47368/ejhc.2022.204 · Cited by 8
“As it is difficult to build trust (Levi, 1998), especially at an early stage (Wang & Huff, 2007) and in times when misinformation is widespread in social media (Gallotti et al., 2020), there could be differences in confidence related to the vaccine type.”
BackgroundSesa, G., Czabanowska, K., Giangreco, A., et al. (2022) · Public Health in Practice · doi:10.1016/j.puhip.2022.100345 · Cited by 6
“Specifically, the diffusion of unreliable information on social media platforms preceded a rise in COVID-19 infections [10].”
Chomel, V., Panahi, M., & Chavalarias, D. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21127-0_11 · Cited by 7
Omar, R. G. A. S., & Casero-Ripollés, A. (2023) · Online Journal of Communication and Media Technologies · doi:10.30935/ojcmt/13005 · Cited by 6
Promote to protect: data-driven computational model of peer influence for vaccine perception
Ghosh, S., Bhattacharya, S., Mukherjee, S., & Chakravarty, S. (2024) · Scientific Reports · doi:10.1038/s41598-023-50756-3 · Cited by 6
Masele, J. J., & Daud, E. (2022) · Global Knowledge, Memory and Communication · doi:10.1108/gkmc-05-2022-0103 · Cited by 6
Социальные боты в компьютерной пропаганде: серфинг на информационной волне коронавируса
Василькова, В. В., & Легостаева, Н. И. (2020) · Monitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · doi:10.14515/monitoring.2020.6.1762 · Cited by 6
Gugliotta, C., Gentili, D., Marras, S., Dettori, M., Muglia, P. P., Desole, M. G., Acciaro, M., Bellu, S., Azara, A., & Castiglia, P. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18115712 · Cited by 6
“The risk is having to fight, even before the pandemic, with infodemics or the viral spread of false, partial, or erroneous information [54].”
BackgroundRumour Detection on Social Media with Long-Tail Strategy
Zhang, G., Liang, R., Yu, Z., & Zhang, S. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892019 · Cited by 7
Cola, G., Mazza, M., & Tesconi, M. (2023) · IEEE Access · doi:10.1109/access.2023.3282580 · Cited by 7
Metadata
- Authors
- Gallotti, R., Valle, F., Castaldo, N., Sacco, P., & De Domenico, M.
- Affiliations
- Fondazione Bruno Kessler (IT)
- Venue
- Nature Human Behaviour
- Published
- 2020-10-29
- Volume
- 4
- Issue
- 12
- Pages
- 1285-1293
- ISSN
- 2397-3374 (ISSN-L)
- OpenAlex
- W3015622078
- Cited by
- 591
- References
- 61
- Citations by year
- 2026: 39 · 2025: 69 · 2024: 80 · 2023: 90 · 2022: 129 · 2021: 137 · 2020: 46
- FWCI
- 134.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Human Behaviour (publishedVersion, public-domain)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- infodemic, COVID-19 pandemic, misinformation, Twitter, news reliability, early warning indicators, epidemic spread, misinformation mitigation, public health communication, social media
- MeSH terms
- COVID-19, Humans, Mass Media, Models, Theoretical, Risk Assessment, Consumer Health Information, Social Networking, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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