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.
- 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
“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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“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…”
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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…”
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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 301–400 of 587
Trials and tribulations: so many potential treatments, so few answers
Gazendam, A., Nucci, N. W., Ekhtiari, S., Gohal, C., Zhu, M. T., Payne, A., & Bhandari, M. (2020) · International Orthopaedics · doi:10.1007/s00264-020-04625-7 · Cited by 6
Social Resilience Indicators for Pandemic CrisesPreprint
Busic, A., & Schubert, R. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3938198 · Cited by 6
Song, B., Fan, Z., Song, Y., Ding, L., Qin, Y., & Wang, X. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117438 · Cited by 5
Milhazes-Cunha, J., & de Oliveira, L. G. (2023) · Societies · doi:10.3390/soc13100226 · Cited by 5
Dobrina, R., Starec, A., Brunelli, L., Orzan, E., Vita, C. D., Bicego, L., Ronfani, L., de Castro, V. P. N., Rocco, P. D., Zanchiello, S., et al. (2024) · Healthcare · doi:10.3390/healthcare12040442 · Cited by 6
“…as well as the risks of misinformation that characterize digital societies and their potential impact on citizens’ health decision-making processes [25], the committee included items in the questionnaire aimed at exploring the information sources most commonly used by ENT patients’ caregivers.”
BackgroundEducation and COVID-19 excess mortality
Bello, P., & Rocco, L. (2022) · Economics & Human Biology · doi:10.1016/j.ehb.2022.101194 · Cited by 8
Differences in beliefs about COVID-19 by gun ownership: a cross-sectional survey of Texas adults
Johnson, R. M., Crifasi, C. K., Goodell, E. M. A., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-048094 · Cited by 5
Arellano-Rojas, P., Calisto-Breiding, C., Brignardello-Burgos, S., & Peña-Pallauta, P. (2022) · Palabra clave · doi:10.24215/18539912e154 · Cited by 8
In.To. COVID-19 socio-epidemiological co-causality
Galbraith, E., Li, J., Vilas, V. D. R., & Convertino, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-09656-1 · Cited by 6
“1) manifesting seasonality, memory and fluctuations are on average [0, 1, 1] for all models including ACF, [0, 1, 2] toward the end of the monitored period that highlights the increase importance of fluctuations, and [1, 1, 2] for volume and positivity that highlights the higher seasonality of tweet volume and ability…”
BackgroundPatients’ Intentions to Use a Physician-Provided Digital Health Information Service
Memenga, P., & Link, E. (2024) · European Journal of Health Communication · doi:10.47368/ejhc.2024.201 · Cited by 5
Wehrli, S., Irrgang, C., Scott, M., Bert Arnrich, A., & Boender, T. S. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1378412 · Cited by 5
“Social media data are also used to improve the understanding of social media’s role in infodemics: how COVID-19-related topics spread online [e.g., (24, 25)], the prevalence of misinformation [e.g., (26, 27)], or misinformation interventions [e.g., (28)].”
MethodRethinking the Infodemic: Social Media and Offline Action in the COVID-19 Pandemic
Heyerdahl, L. W., Lana, B., & Giles‐Vernick, T. (2021) · Economics, law, and institutions in Asia Pacific · doi:10.1007/978-981-16-5727-6_4 · Cited by 5
“…appears to have catalyzed distrust and denial of COVID-19, uses of dangerous or ineffective remedies, and the destruction of telecommunications towers based on claims linking 5G networks with viral transmission (Biasio et al. 2020; Dubey et al. 2020; Gallotti et al. 2020; Naeem et al. 2020).”
BackgroundMethodPark, S., & Wang, R. (2022) · Behavioral Sciences · doi:10.3390/bs12060190 · Cited by 5
“Infodemic refers to the prevalence of unreliable, erroneous, or inaccurate information and is one of the biggest challenges that the world has faced [1].”
BackgroundHow Health-Related Misinformation Spreads Across the Internet: Evidence for the “Typhoon Eye” Effect
Zheng, L., Cai, J., Wang, F., Ruan, C., Xu, M., & Miao, M. (2022) · Cyberpsychology Behavior and Social Networking · doi:10.1089/cyber.2022.0047 · Cited by 5
“13 According to the stimulus–organism–response (SOR) theory, an environmental stimulus that influences a person’s behavioral reactions is presented by intermediary cognitive processes.”
Narain, K., Bimpong, K. A., Wamukota, O. K., et al. (2023) · Global Health · doi:10.1155/2023/1406035 · Cited by 5
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Clustering Analysis of Website Usage on Twitter During the COVID-19 Pandemic
Cruickshank, I. J., & Carley, K. M. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-76228-5_28 · Cited by 5
COVID-19 home remedies and myths becoming a hazardous health infodemic?
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Pierri, F., Tocchetti, A., Corti, L., Giovanni, M. D., Pavanetto, S., Brambilla, M., & Ceri, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03757 · Cited by 6
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Agunlejika, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5122225 · Cited by 6
Malek, S., Griffin, C. H., Fraleigh, R. D., Lennon, R. P., Monga, V., & Shen, L. (2025) · medRxiv · doi:10.1101/2025.05.22.25327931 · Cited by 4
“False remedies, unverified claims on the origins of the virus, and politicized narratives about preventive measures severely hampered containment efforts (20–22).”
Assessing the impact of misinformation during the spread of infectious diseases
Bernardin, A., & Pérez‐Acle, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-18457-1 · Cited by 4
“The COVID-19 pandemic has given rise to “infodemics”, as defined by the World Health Organization: an overwhelming mix of accurate and misleading information flooding digital platforms and social media 4,5 .”
Zhai, S., Cao, J. X., & Zhu, W. (2025) · Automatica · doi:10.1016/j.automatica.2025.112757 · Cited by 4
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“Proper information and education of patients and citizens are milestones of disease prevention and health promotion, and the importance of combating infodemic and fake news has been and continues to be a burning issue, especially in times such as the COVID-19 pandemic, which was characterized by an overabundance of in…”
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Scheduler Dependencies in Agent-Based Models: A Case-Study Using a Contagion Model
Mudigonda, S. P., Núñez-Corrales, S., Venkatachalapathy, R., et al. (2022) · Springer proceedings in complexity · doi:10.1007/978-3-030-96188-6_5 · Cited by 4
Nguyen, D. V. P., & Phung, T.-B. (2023) · SAGE Open · doi:10.1177/21582440231205169 · Cited by 5
“In the Covid-19, information credibility is the most important factor, while the ‘‘infodemic’’ with rapidly spreading news poses a risk to its quality in general (Gallotti et al., 2020).”
BackgroundGyftopoulos, S., Drosatos, G., Fico, G., Pecchia, L., & Kaldoudi, E. (2024) · Behavioral Sciences · doi:10.3390/bs14020128 · Cited by 5
Ort, A., & Rohrbach, T. (2024) · Public Health · doi:10.1016/j.puhe.2023.12.033 · Cited by 4
Song, J., Kang, J., & Cho, Y. J. (2024) · Social Policy and Administration · doi:10.1111/spol.13027 · Cited by 4
Cervera, R. B., Pérez‐Solà, C., & Batlle, A. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01251-8 · Cited by 5
“Also, the Covid-19 pandemic multiplied the attention of network analytics researchers to this topic (Brennen et al. 2020; Gallotti et al. 2020), a sub-field with similarities in the analysis, even though it is not a full political framework.”
Cognitive flexibility predicts attitudes towards vaccination: evidence from a New Zealand sample
Gomes‐Ng, S., Wood, J. K., & Cowie, S. (2024) · BMC Psychology · doi:10.1186/s40359-024-02048-2 · Cited by 5
“These kinds of questions are especially pertinent amid the proliferation of information – and mis information – brought on by the rise of online media [34–36].”
BackgroundSocial media‐enabled crisis response in Vietnam: A tương thân tương ái perspective
Qi, H., Xu, L., Pan, S. L., & Du, W. (2024) · Information Systems Journal · doi:10.1111/isj.12562 · Cited by 4
“In the early stage of COVID-19, rampant misinformation on social media has become a serious threat to public health (Gallotti et al., 2020).”
Geissler, D., & Feuerriegel, S. (2023) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3686930 · Cited by 8
“In this regard, it may also be likely that behavioral interventions such as counter-measures against fake news [14, 15, 27, 40] can be adapted to online propaganda.”
Evolutionary modeling and analysis of opinion exchange and epidemic spread among individuals
Zeng, R., Chang, X., & Liu, B. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1501807 · Cited by 4
Digitalization, Cultural Production, Exchange, and Consumption
Shaban, A. (2024) · Springer eBooks · doi:10.1007/978-981-97-9278-8_5 · Cited by 5
Wen-Yi, A. W., Lan, J.-Y., Wang, M.-H., & Yu, C. (2021) · JMIR Medical Informatics · doi:10.2196/30467 · Cited by 5
“Gallotti et al [10] looked at how much unreliable COVID-19 information Twitter users were exposed to across countries.”
BackgroundHow infodemic intoxicates public health surveillance: from a big to a slow data culture
Chioléro, A. (2022) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2021-216584 · Cited by 4
Complex systems for the most vulnerable
Omodei, E., García–Herranz, M., Paolotti, D., & Tizzoni, M. (2022) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac60b1 · Cited by 5
Nakanishi, M., Sakai, M., Takagi, G., Toshi, K., Wakashima, K., & Yoshii, H. (2022) · JMIR Formative Research · doi:10.2196/35806 · Cited by 4
“Adequate communication strategies should be embedded in reliable information from trusted sources [31,33].”
BackgroundSalcedo, P., Kotz, G., Espinoza, C. M. V., Sánchez-Hechavarría, M. E., Universidad de Concepción, C., Universidad de Concepción, C., Universidad de Concepción, C., Universidad de Concepción, C., & de Chile, U. A. (2022) · Revista Latinoamericana de Psicología · doi:10.14349/rlp.2022.v54.1 · Cited by 4
Coronado, S., Martínez, J. N., Estrada, V. H. G., et al. (2023) · Mathematics · doi:10.3390/math11020394 · Cited by 4
Bridging online and offline dynamics of the face mask infodemic
Uyheng, J., Robertson, D. C., & Carley, K. M. (2023) · BMC Digital Health · doi:10.1186/s44247-023-00026-z · Cited by 4
“The interoperable methodology we employ likewise constitutes valuable contributions to future study and practical monitoring of infodemics during crisis [9, 31].”
BackgroundMethodMuhanga, M. I., Jesse, A. M., & Ngowi, E. E. (2024) · Parasite Epidemiology and Control · doi:10.1016/j.parepi.2024.e00345 · Cited by 4
“The overwhelming spread of information, particularly fake ones, amid the efforts in mitigating and preventing the pandemic has been seen to impede the pandemic response efforts (Gallotti et al., 2020).”
BackgroundPatra, R., Das, N. C., Bhattacharya, M., Shit, P. K., Patra, B. C., & Mukherjee, S. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-68936-0_3 · Cited by 4
Kopecký, K., Voráč, D., Mikulcová, K., Krejčí, V., & García, G. G. (2022) · Libri · doi:10.1515/libri-2021-0048 · Cited by 3
User Experience Design for Automatic Credibility Assessment of News Content About COVID-19
Schulz, K., Rauenbusch, J., Fillies, J., Rutenburg, L., Karvelas, D., & Rehm, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22131-6_11 · Cited by 5
Socio-Life Science and the COVID-19 Outbreak
Yano, M., Matsuda, F., Sakuntabhai, A., & Hirota, S. (2021) · Economics, law, and institutions in Asia Pacific · doi:10.1007/978-981-16-5727-6 · Cited by 4
Characterizing Twitter users behaviour during the Spanish Covid-19 first wavePreprint
Esquirol, B., Prignano, L., Dı́az-Guilera, A., & Cozzo, E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06550 · Cited by 4
“A large-scale public debate is taking place in a hybrid media ecosystem in which online social media (OSM) in general, and Twitter in particular, play a central role [1, 2, 3].”
BackgroundStella, M. (2020) · doi:10.31234/osf.io/69fex · Cited by 4
Torunsky, N. T., Kedrick, K., & Vilares, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-89781-9 · Cited by 3
“Given the “misinformation pandemic” amidst the pandemic 54 , understanding how particular profiles of information utility ratings and weightings and other individual differences may be related to the ability to distinguish information from misinformation/disinformation is a particularly important…”
Heikkilä, T., Laaksonen, S.-M., & Pohjonen, M. (2025) · New Media & Society · doi:10.1177/14614448251365270 · Cited by 4
“During the COVID-19 pandemic, such fears became known as the ‘(mis)infodemic’ (Gallotti et al., 2020).”
How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics
Trump, B. D., Galaitsi, S., Cegan, J. C., & Linkov, I. (2025) · Risk Analysis · doi:10.1111/risa.70103 · Cited by 6
Fake News Detection in Portuguese Under Large Language Model-Generated Content
Silva, R. M., Amamou, H., Ferraz, L. B. S., Silva, F. K. A. D., & Avila, A. R. (2025) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2025.5525 · Cited by 7
“During the COVID-19 pandemic, for example, while several governments strived to inform and protect the public, an overload of inaccurate information, also known as infodemics [Gallotti et al ., 2020], was being consumed on social media platforms [Smith et al ., 2021].”
Mei, G., Cai, Y., Zhang, S.-S., et al. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3632506 · Cited by 3
“During an epidemic, information related to the disease often spreads rapidly through online social networks, influencing public awareness and individual responses [8], [9], [10].”
Rumor propagation and supervision during confrontation: An importance-driven SIRQS network model
Zhang, W., Wu, J., Feng, M., et al. (2026) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0336448 · Cited by 3
Community-based fact-checking reduces the spread of misleading posts on X (formerly Twitter)
Chuai, Y., Pilarski, M., Renault, T., Amariles, D. R., Troussel, A., Lenzini, G., & Pröllochs, N. (2026) · Nature Communications · doi:10.1038/s41467-026-72597-0 · Cited by 11
“To shed further light on the causal mechanisms underlying the reduction in resharing of community-noted posts, we used the X (formerly Twitter) API to collect additional information on a random subset of users who reshared posts with community notes (see Supplementary Note 9).”
Topic Modeling for Tracking COVID-19 Communication on Twitter
Bogović, P. K., Meštrović, A., & Martinčić-Ipšić, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-16302-9_19 · Cited by 3
Analysis of Evolutionary Social Media Activities: Pre-Vaccine and Post-Vaccine Emergency Use
Liu, H., Zhou, M., Lu, X., et al. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123156 · Cited by 3
Tian, X., Mai, Q., & An, S. (2023) · International Journal of Modern Physics B · doi:10.1142/s0217979224502837 · Cited by 3
Vaccine-related misinformation and attitude toward COVID-19 vaccination in Japan
Furuse, Y., & Tabuchi, T. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1805589 · Cited by 3
“During the pandemic, misinformation and conspiratorial narratives related to COVID-19 and vaccines also spread widely (17, 18).”
Early morning hour and evening usage habits increase misinformation-spread
Stockinger, E., Gallotti, R., & Hausladen, C. I. (2024) · Scientific Reports · doi:10.1038/s41598-024-69447-8 · Cited by 5
“Geocoding and geodata cleaning was conducted by the dataset authors 71 based on the user’s self-declared location field ArcGIS API .”
BackgroundMethodInfluentialSelf-citationMapping Infodemic Responses: A Geospatial Analysis of COVID-19 Discourse on Twitter in Italy
Fernandez, G., Suresh-Babu, S., & Vito, D. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22050668 · Cited by 5
“In contrast, these platforms have also been used to counteract misinformation, foster community support, and disseminate accurate information [3].”
Nohl, A., Abdallah, H. B., Weichert, V., Zeiger, S., Ohmann, T., & Dudda, M. (2021) · Healthcare · doi:10.3390/healthcare9121616 · Cited by 3
“It is almost impossible for medical laypersons to refute these false reports [30,31].”
BackgroundSentinel node approach to monitoring online COVID-19 misinformation
Osborne, M., Malloy, S. S., Nisbet, E. C., Bond, R. M., & Tien, J. H. (2022) · Scientific Reports · doi:10.1038/s41598-022-12450-8 · Cited by 6
“The proliferation of online misinformation has presented a challenge to public health throughout the COVID-19 pandemic [1], and is characterized by broad demographic and geographic reach [2].”
BackgroundNetwork Dynamics of COVID-19 Fake and True News Diffusion Networks
Zahid, S., Malick, R. A. S., & Sagri, M. R. (2022) · Journal of Information & Knowledge Management · doi:10.1142/s0219649222400093 · Cited by 3
User profiling and role evaluation of government microbloggers in the context of public emergencies
An, L., Shen, Y., Tao, Y., et al. (2022) · Online Information Review · doi:10.1108/oir-10-2021-0509 · Cited by 3
Javed, M. I., Dimaguila, G. L., Khademi, S., Palmer, C. J., & Buttery, J. P. (2023) · Australasian Computer Science Week Multiconference (ACSW) · doi:10.1145/3579375.3579400 · Cited by 3
“This is particularly relevant during the rollout of the COVID-19 vaccines as social bots have become more active during the pandemic [31, 32].”
BackgroundQuian, A., Elías, C., & Pérez, X. S. (2023) · El Profesional de la Informacion · doi:10.3145/epi.2023.jul.13 · Cited by 4
IRMA: the 335-million-word Italian coRpus for studying MisinformAtion
Carrella, F., Miani, A., & Lewandowsky, S. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.171 · Cited by 5
“The MD dataset is an open-source collection of domains referenced by Gallotti et al. (2020) and extended with other lists curated by fact-checking collectives, individual scholars, and journalists.”
Semaan, J., Farah, C., Harb, R. A., Bardus, M., Germani, A., & Elhajj, I. H. (2023) · Digital Health · doi:10.1177/20552076231205280 · Cited by 3
“…(i.e., misinformation) or deliberately shared wrong information and conspiracy theories (i.e., disinfor-mation), creating panic and contributing to the diffusion of rumors 2,3 and confusion among the general public and mass media, who were diffusing information received through internet resources.”
BackgroundMilich, K. M., Fisher, N. C., & Sobral, G. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02461-9 · Cited by 3
Hernández‐Vásquez, A., Visconti-Lopez, F. J., Solorzano-Salazar, D. M., & Barrenechea-Pulache, A. (2024) · Pharmacy Practice · doi:10.18549/pharmpract.2023.4.2877 · Cited by 4
“55 Despite the existence of a rumor surveillance system implemented by the Ministry of Health, this did not prevent pseudoscientists from appearing in several mass media outlets to make controversial claims on the protective effects of different drug combinations or preclude the spread of fake news…”
BackgroundTeaching epistemic integrity to promote reliable scientific communication
Allard, A., & Clavien, C. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1308304 · Cited by 6
Carrasco-Farré, C. (2024) · Business & Society · doi:10.1177/00076503241255343 · Cited by 3
“…evidence has primarily focused on examining people’s reactions, sharing behaviors, and exposure to misinformation on social networks during the COVID-19 pandemic (Aiello et al., 2021; Gallotti et al., 2020; Khanday et al., 2021; Loomba et al., 2021; Mazzuca et al., 2021; Wicke & Bolognesi, 2020).”
Media Frames of COVID-19 Pandemic
Bulatović, L. L., & Bulatović, G. (2021) · In medias res · doi:10.46640/imr.10.19.2 · Cited by 3
Implicações da desinformação e da infodemia no contexto da pandemia da Covid-19
de Cristo, H. S., Santos, T. D. A., Horita, F., & Saba, H. C. (2021) · Research Society and Development · doi:10.33448/rsd-v10i2.12998 · Cited by 3
Die Auswirkungen der COVID-19-Pandemie aus der Sicht ambulanter Krebspatienten
Pichler, T., Frank, T., Maier, S., Batenhorst, I., Abawi-Daltrozzo, T., Harbeck, N., Algül, H., Heinemann, V., Hermelink, K., Mumm, F., et al. (2022) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/a-1746-7534 · Cited by 3
“Unter dem Stichwort „ infodemic “ [21] wurde im Zuge von Pandemien das weitgefasste Phänomen einer raschen und weitreichenden, primär digitalen Verbreitung von Informationen mit fragwürdiger Qualität beschrieben [22, 23].”
(2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01627-4 · Cited by 3
Risiko- und Krisenkommunikation
Dickmann, P. (2023) · Elsevier eBooks · doi:10.1016/b978-3-437-24591-6.00026-6 · Cited by 3
The CLAIRE COVID-19 initiative: approach, experiences and recommendations
Bontempi, G., Chavarriaga, R., Canck, H. E., Girardi, E., Hoos, H. H., Kilbane-Dawe, I., Ball, T., Nowé, A., Sousa, J., Bacciu, D., et al. (2021) · Ethics and Information Technology · doi:10.1007/s10676-020-09567-7 · Cited by 3
“Products of the individual team members, such as the info-demic observatory model (Gallotti et al. 2004) developed by the topic coordinator within the Complex Multilayer Networks Lab at FBK, allow to monitor the current infodemic globally, in each country, or at sub-regional resolution in real time.”
BackgroundSelf-citationHussein, M. R., Shams, A. B., Rahman, A., Raihan, M. M. S., Mostari, S., Siddika, N., Kabir, R., & Apu, E. H. (2020) · Research Square · doi:10.21203/rs.3.rs-60301/v1 · Cited by 3
“In an Italian CoMuNe laboratory, Gallotti et al. (2020) have set up a COVID-19 “infodemic observatory,” where they used arti cial intelligence (AI) integrated automated software to follow 4.7 million tweets on COVID-19 streaming past every day.”
MethodInfluentialSocial Media Surveillance and (Dis)Misinformation in the COVID ‐19 Pandemic
Spitzberg, B. H., Tsou, M., & Gawron, M. (2021) · Wiley eBooks · doi:10.1002/9781119751809.ch12 · Cited by 4
Combating fake news by empowering fact-checked news spread via topology-based interventionsPreprint
Wang, K., Yaqub, W., Lakhdari, A., & Suleiman, B. (2021) · arXiv.org · doi:10.48550/arxiv.2107.05016 · Cited by 3
“Recently, in the light of the outbreak of COVID-19, several research papers have studied the propagation of misinformation related to the coronavirus on social media [9, 14, 31, 57, 65, 79].”
BackgroundAge as a Moderator of Health Outcomes and Trust in Physicians and the Healthcare System
Katz, E. (2021) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.8331 · Cited by 3
Characterizing the Gendered Twitter Discussion of COVID-19 Hoax
Al‐Rawi, A., & Jamieson, K. (2022) · Health Communication · doi:10.1080/10410236.2022.2149112 · Cited by 2
“In their classification of “conspiracy and junk science,” Gallotti et al. (2020) define the category as containing “systematically manipulative and fabricated content with the purpose of legitimizing implausible conceptualizations of facts and knowledge” (p. 1287) which also lacks a factual basis…”
BackgroundMay You Live in Interesting Times. Science vs. Pseudoscience in the Era of the Internet
Szynkiewicz, M. (2020) · ETHICS IN PROGRESS · doi:10.14746/eip.2020.1.5 · Cited by 2
“This phenomenon is currently also being investigated in communication contexts (Gallotti et al. 2020).”
BackgroundNon-stationary temporal-spatio correlation analysis of information-driven complex financial dynamics
Zhang, J., Zheng, B., Jin, L., et al. (2023) · Chinese Journal of Physics · doi:10.1016/j.cjph.2023.10.035 · Cited by 2
Infodemic Threat As an Obstacle in The Covid-19 Pandemic Handling in Indonesia
Kundhalini, A. S., Afifuddin, M., & Widodo, P. (2023) · International Journal Of Humanities Education and Social Sciences (IJHESS) · doi:10.55227/ijhess.v3i2.609 · Cited by 2
“This is due to information whose veracity cannot be verified sabotaging information relating to the government's handling of Covid-19 that is communicated through multiple media, causing public health communication to become chaotic (Gallotti et al., 2020; Naeem & Bhatti, 2020).”
BackgroundWould You Mind Sharing Your Opinion About the Covid-19 Vaccination?
Link, E., Scheper, J., & Memenga, P. (2024) · European Journal of Health Communication · doi:10.47368/ejhc.2024.101 · Cited by 3
“Especially in the context of the Covid-19 vaccination, where an overload of information, including lots of misinformation, is prevalent (Gallotti et al., 2020; Soroya et al., 2021), individuals may reflect on seeking information in the safe haven of the social environment.”
BackgroundStjernswärd, S., Tilgmann, C., & Glasdam, S. (2024) · Ethics & Human Research · doi:10.1002/eahr.500197 · Cited by 2
Covering Major Epidemics/Pandemics
Perelman, C., López‐León, S., & Wegman-Ostrosky, T. (2024) · Springer eBooks · doi:10.1007/978-3-031-49084-2_16 · Cited by 2
Moyo, T. (2024) · Anthropology Southern Africa · doi:10.1080/23323256.2023.2265942 · Cited by 2
“Gallotti et al. (2020) analysed more than 100 million Twitter messages posted world-wide in the nascent stages of the pandemic and discovered that there was a significant amount of unreliable information circulating.”
BackgroundPersuasive Effects of Crisis Communication during Public Health Emergency Outbreaks in China
Wu, T. N., & Yu, G. (2024) · Behavioral Sciences · doi:10.3390/bs14100885 · Cited by 2
“An infodemic not only negatively affects widely adopted health protection behaviors such as NPIs [10–13] but also jeopardizes public behavioral patterns and health [13–15].”
BackgroundAdversarial botometer: adversarial analysis for social bot detection
Najari, S., Rafiei, D., Salehi, M., & Farahbakhsh, R. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01387-7 · Cited by 2
“Similar results were found regarding the presence of bots in online cryptocurrency discussions [4] and their involvement in spreading “infodemics” during the COVID-19 pandemic [5].”
Creativity in crisis communication: a systematic review
Smaliukienė, R. (2024) · Creativity Studies · doi:10.3846/cs.2024.20061 · Cited by 2
Mazza, F., Brambilla, M., Piccardi, C., & Pierri, F. (2025) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2025.0511 · Cited by 2
“During the COVID-19 pandemic, unsafe health behaviors were often associated with the spread of misinformation on online social networks [6, 7, 8].”
Sapiña, L., Lafita, I., & Domínguez, M. (2021) · Advances in human services and public health (AHSPH) book series · doi:10.4018/978-1-7998-7987-9.ch019 · Cited by 2
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
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